Stock_Price_Prediction.ipynb

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        "# This program uses an artificial recurrent neural network called Long Short Term Memory (LSTM)\n",
        "# LSTMs are used to predict the closing stock price of a corporation (Apple Inc.) using the past 60 day stock price\n",
        "# Unlike feed forward neural netwroks, LSTM has feedback connections. Therefore it can process entire sequences of data like a speech or video.\n",
        "# Widely used for sequence prediction problems, because LSTM are able to store past information which is important, and able to forget irrevelant information"
      ]
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      "source": [
        "#Import the libraries\n",
        "import math\n",
        "import pandas_datareader as pdr\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.models import Sequential         \n",
        "from keras.layers import Dense, LSTM\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('fivethirtyeight')"
      ],
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      "source": [
        "#Read the uploaded stock data (18-10-2012 to 18-10-2022)\n",
        "df = pd.read_csv('AAPL.csv')\n",
        "#display the data\n",
        "df"
      ],
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              "            Date        Open        High         Low       Close   Adj Close  \\\n",
              "0     2012-10-18   22.842501   22.930714   22.500000   22.594286   19.374552   \n",
              "1     2012-10-19   22.537500   22.563213   21.772142   21.780001   18.676304   \n",
              "2     2012-10-22   21.872143   22.692142   21.812857   22.643929   19.417120   \n",
              "3     2012-10-23   22.535713   22.639286   21.846430   21.905714   18.784101   \n",
              "4     2012-10-24   22.194286   22.376785   21.808571   22.029642   18.890368   \n",
              "...          ...         ...         ...         ...         ...         ...   \n",
              "2510  2022-10-11  139.899994  141.350006  138.220001  138.979996  138.979996   \n",
              "2511  2022-10-12  139.130005  140.360001  138.160004  138.339996  138.339996   \n",
              "2512  2022-10-13  134.990005  143.589996  134.369995  142.990005  142.990005   \n",
              "2513  2022-10-14  144.309998  144.520004  138.190002  138.380005  138.380005   \n",
              "2514  2022-10-17  141.070007  142.899994  140.270004  142.410004  142.410004   \n",
              "\n",
              "         Volume  \n",
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              "      <th>1</th>\n",
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      "source": [
        "#Check the type of date column\n",
        "df['Date']"
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              "0       2012-10-18\n",
              "1       2012-10-19\n",
              "2       2012-10-22\n",
              "3       2012-10-23\n",
              "4       2012-10-24\n",
              "           ...    \n",
              "2510    2022-10-11\n",
              "2511    2022-10-12\n",
              "2512    2022-10-13\n",
              "2513    2022-10-14\n",
              "2514    2022-10-17\n",
              "Name: Date, Length: 2515, dtype: object"
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          "execution_count": 4
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      "cell_type": "code",
      "source": [
        "#Convert object to date\n",
        "import datetime\n",
        "\n",
        "def str_to_datetime(s):\n",
        "  split = s.split('-')\n",
        "  year, month, day = int(split[0]), int(split[1]), int(split[2])\n",
        "  return datetime.datetime(year=year, month=month, day=day)\n",
        "\n",
        "#testing the function\n",
        "datetime_object = str_to_datetime('2012-10-18')\n",
        "datetime_object"
      ],
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              "datetime.datetime(2012, 10, 18, 0, 0)"
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          "metadata": {},
          "execution_count": 5
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      "cell_type": "code",
      "source": [
        "#Apply this function to all the values in the Date column\n",
        "df['Date'] = df['Date'].apply(str_to_datetime)\n",
        "#Check type\n",
        "df['Date']"
      ],
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              "0      2012-10-18\n",
              "1      2012-10-19\n",
              "2      2012-10-22\n",
              "3      2012-10-23\n",
              "4      2012-10-24\n",
              "          ...    \n",
              "2510   2022-10-11\n",
              "2511   2022-10-12\n",
              "2512   2022-10-13\n",
              "2513   2022-10-14\n",
              "2514   2022-10-17\n",
              "Name: Date, Length: 2515, dtype: datetime64[ns]"
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          "execution_count": 6
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    {
      "cell_type": "code",
      "source": [
        "#Replace the first column and make the date column the index\n",
        "df.index = df.pop('Date')"
      ],
      "metadata": {
        "id": "J-NSFVMDRIgI"
      },
      "execution_count": null,
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    {
      "cell_type": "code",
      "source": [
        "#Display the date frame\n",
        "df"
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              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2012-10-18   22.842501   22.930714   22.500000   22.594286   19.374552   \n",
              "2012-10-19   22.537500   22.563213   21.772142   21.780001   18.676304   \n",
              "2012-10-22   21.872143   22.692142   21.812857   22.643929   19.417120   \n",
              "2012-10-23   22.535713   22.639286   21.846430   21.905714   18.784101   \n",
              "2012-10-24   22.194286   22.376785   21.808571   22.029642   18.890368   \n",
              "...                ...         ...         ...         ...         ...   \n",
              "2022-10-11  139.899994  141.350006  138.220001  138.979996  138.979996   \n",
              "2022-10-12  139.130005  140.360001  138.160004  138.339996  138.339996   \n",
              "2022-10-13  134.990005  143.589996  134.369995  142.990005  142.990005   \n",
              "2022-10-14  144.309998  144.520004  138.190002  138.380005  138.380005   \n",
              "2022-10-17  141.070007  142.899994  140.270004  142.410004  142.410004   \n",
              "\n",
              "               Volume  \n",
              "Date                   \n",
              "2012-10-18  476624400  \n",
              "2012-10-19  744086000  \n",
              "2012-10-22  546730800  \n",
              "2012-10-23  707145600  \n",
              "2012-10-24  558527200  \n",
              "...               ...  \n",
              "2022-10-11   77033700  \n",
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              "2022-10-13  113224000  \n",
              "2022-10-14   88512300  \n",
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              "      <th>2012-10-18</th>\n",
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              "      <td>22.537500</td>\n",
              "      <td>22.563213</td>\n",
              "      <td>21.772142</td>\n",
              "      <td>21.780001</td>\n",
              "      <td>18.676304</td>\n",
              "      <td>744086000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-22</th>\n",
              "      <td>21.872143</td>\n",
              "      <td>22.692142</td>\n",
              "      <td>21.812857</td>\n",
              "      <td>22.643929</td>\n",
              "      <td>19.417120</td>\n",
              "      <td>546730800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-23</th>\n",
              "      <td>22.535713</td>\n",
              "      <td>22.639286</td>\n",
              "      <td>21.846430</td>\n",
              "      <td>21.905714</td>\n",
              "      <td>18.784101</td>\n",
              "      <td>707145600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-24</th>\n",
              "      <td>22.194286</td>\n",
              "      <td>22.376785</td>\n",
              "      <td>21.808571</td>\n",
              "      <td>22.029642</td>\n",
              "      <td>18.890368</td>\n",
              "      <td>558527200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-11</th>\n",
              "      <td>139.899994</td>\n",
              "      <td>141.350006</td>\n",
              "      <td>138.220001</td>\n",
              "      <td>138.979996</td>\n",
              "      <td>138.979996</td>\n",
              "      <td>77033700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-12</th>\n",
              "      <td>139.130005</td>\n",
              "      <td>140.360001</td>\n",
              "      <td>138.160004</td>\n",
              "      <td>138.339996</td>\n",
              "      <td>138.339996</td>\n",
              "      <td>70433700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-13</th>\n",
              "      <td>134.990005</td>\n",
              "      <td>143.589996</td>\n",
              "      <td>134.369995</td>\n",
              "      <td>142.990005</td>\n",
              "      <td>142.990005</td>\n",
              "      <td>113224000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-14</th>\n",
              "      <td>144.309998</td>\n",
              "      <td>144.520004</td>\n",
              "      <td>138.190002</td>\n",
              "      <td>138.380005</td>\n",
              "      <td>138.380005</td>\n",
              "      <td>88512300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17</th>\n",
              "      <td>141.070007</td>\n",
              "      <td>142.899994</td>\n",
              "      <td>140.270004</td>\n",
              "      <td>142.410004</td>\n",
              "      <td>142.410004</td>\n",
              "      <td>85073900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2515 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e9a23c2-e886-4bf0-b5c0-0e455b989de6')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-6e9a23c2-e886-4bf0-b5c0-0e455b989de6 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-6e9a23c2-e886-4bf0-b5c0-0e455b989de6');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Number of rows and columns and rows in the dataset\n",
        "df.shape"
      ],
      "metadata": {
        "id": "_i6NKY22Lm_D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "353dcd42-94ca-4902-f8b5-e654cebb97cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2515, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualize the closing price using matplotlib\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Closing Price History')\n",
        "plt.plot(df['Close'])\n",
        "plt.xlabel('Date',fontsize = 18)\n",
        "plt.ylabel('Close Price (in USD $)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "jP5CwYDMLvZ0",
        "outputId": "f31f547e-b682-432d-cef4-ede780752352"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Create a new dataframe with only the 'Close column'\n",
        "data = df.filter(['Close'])\n",
        "\n",
        "#Convert the dataframe to a numpy array\n",
        "dataset = data.values\n",
        "\n",
        "#Get the number of rows to train the LSTM model on (80% training data)\n",
        "training_data_len = math.ceil(len(dataset) * 0.8)\n",
        "training_data_len"
      ],
      "metadata": {
        "id": "mW09kWzbVGrq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61dc2748-8905-4acc-e7a1-32f4f9de027c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2012"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Display the scaled data\n",
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "scaled_data = scaler.fit_transform(dataset)\n",
        "\n",
        "scaled_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R-EQisyvlCBz",
        "outputId": "b135ab28-5d98-4f80-81dd-c5980f72c2ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.05144983],\n",
              "       [0.04660469],\n",
              "       [0.05174521],\n",
              "       ...,\n",
              "       [0.76782452],\n",
              "       [0.74039425],\n",
              "       [0.76437342]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Create and scale the training data set\n",
        "train_data = scaled_data[0:training_data_len, :]\n",
        "\n",
        "#Split the data into x_train and y_train data sets\n",
        "#x_train dataset will contain independent training features, y_train will be the dependent/target feature\n",
        "x_train = []\n",
        "y_train = []\n",
        "\n",
        "\n",
        "for i in range(60, len(train_data)):\n",
        "  x_train.append(train_data[i-60:i, 0])   \n",
        "  y_train.append(train_data[i, 0])\n",
        "  if i<=61:\n",
        "    print(x_train)\n",
        "    print(y_train)\n",
        "    print()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sb_Jdu3_mBRI",
        "outputId": "c6a16f3f-c61c-44e6-fd3c-7d41aa78ddd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[array([0.05144983, 0.04660469, 0.05174521, 0.04735271, 0.0480901 ,\n",
            "       0.04654093, 0.04536365, 0.0435191 , 0.04377836, 0.03958349,\n",
            "       0.04124529, 0.04086915, 0.03558838, 0.03128513, 0.03326356,\n",
            "       0.03236466, 0.03237954, 0.03110025, 0.02870743, 0.0291452 ,\n",
            "       0.03723105, 0.03620676, 0.03637465, 0.0384572 , 0.0422887 ,\n",
            "       0.04127929, 0.04088828, 0.04225256, 0.04138554, 0.04157891,\n",
            "       0.03938161, 0.03150614, 0.03330182, 0.03032885, 0.02959997,\n",
            "       0.03205866, 0.03155076, 0.02957233, 0.02534346, 0.02726451,\n",
            "       0.03046699, 0.02885406, 0.02788079, 0.02737078, 0.02754927,\n",
            "       0.0260256 , 0.02646337, 0.02530096, 0.03009934, 0.03368219,\n",
            "       0.03220953, 0.02900069, 0.02834192, 0.02864155, 0.02689688,\n",
            "       0.02825905, 0.02757691, 0.02363492, 0.02027095, 0.02455719])]\n",
            "[0.023832539199183017]\n",
            "\n",
            "[array([0.05144983, 0.04660469, 0.05174521, 0.04735271, 0.0480901 ,\n",
            "       0.04654093, 0.04536365, 0.0435191 , 0.04377836, 0.03958349,\n",
            "       0.04124529, 0.04086915, 0.03558838, 0.03128513, 0.03326356,\n",
            "       0.03236466, 0.03237954, 0.03110025, 0.02870743, 0.0291452 ,\n",
            "       0.03723105, 0.03620676, 0.03637465, 0.0384572 , 0.0422887 ,\n",
            "       0.04127929, 0.04088828, 0.04225256, 0.04138554, 0.04157891,\n",
            "       0.03938161, 0.03150614, 0.03330182, 0.03032885, 0.02959997,\n",
            "       0.03205866, 0.03155076, 0.02957233, 0.02534346, 0.02726451,\n",
            "       0.03046699, 0.02885406, 0.02788079, 0.02737078, 0.02754927,\n",
            "       0.0260256 , 0.02646337, 0.02530096, 0.03009934, 0.03368219,\n",
            "       0.03220953, 0.02900069, 0.02834192, 0.02864155, 0.02689688,\n",
            "       0.02825905, 0.02757691, 0.02363492, 0.02027095, 0.02455719]), array([0.04660469, 0.05174521, 0.04735271, 0.0480901 , 0.04654093,\n",
            "       0.04536365, 0.0435191 , 0.04377836, 0.03958349, 0.04124529,\n",
            "       0.04086915, 0.03558838, 0.03128513, 0.03326356, 0.03236466,\n",
            "       0.03237954, 0.03110025, 0.02870743, 0.0291452 , 0.03723105,\n",
            "       0.03620676, 0.03637465, 0.0384572 , 0.0422887 , 0.04127929,\n",
            "       0.04088828, 0.04225256, 0.04138554, 0.04157891, 0.03938161,\n",
            "       0.03150614, 0.03330182, 0.03032885, 0.02959997, 0.03205866,\n",
            "       0.03155076, 0.02957233, 0.02534346, 0.02726451, 0.03046699,\n",
            "       0.02885406, 0.02788079, 0.02737078, 0.02754927, 0.0260256 ,\n",
            "       0.02646337, 0.02530096, 0.03009934, 0.03368219, 0.03220953,\n",
            "       0.02900069, 0.02834192, 0.02864155, 0.02689688, 0.02825905,\n",
            "       0.02757691, 0.02363492, 0.02027095, 0.02455719, 0.02383254])]\n",
            "[0.023832539199183017, 0.02326303081481683]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Convert the x_train and y_train to numpy arrays\n",
        "x_train, y_train = np.array(x_train), np.array(y_train)"
      ],
      "metadata": {
        "id": "nYcXUxaCrIV3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Reshape the data for LSTM\n",
        "#LSTM expects the input to be 3-dimensional (#samples, #timesteps, #features)\n",
        "x_train = np.reshape(x_train,(x_train.shape[0],x_train.shape[1],1))\n",
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N57HjekPrkM_",
        "outputId": "f6d17588-979e-49c8-f431-619f5cbce54e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1952, 60, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Build the LSTM model\n",
        "model = Sequential()\n",
        "\n",
        "#The first argument indicates the number of neurons\n",
        "model.add(LSTM(50, return_sequences = True, input_shape = (x_train.shape[1],1)))\n",
        "model.add(LSTM(50,return_sequences = False))  \n",
        "#Return sequences is false, because we will not be using any more LSTM layers for our model\n",
        "model.add(Dense(25))\n",
        "model.add(Dense(1))"
      ],
      "metadata": {
        "id": "sCSDpUPGsjBY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Compile the model with parameters\n",
        "#Optimizer is used to improve upon the loss function\n",
        "#Loss function is used to tell how well the model did on training data\n",
        "model.compile(optimizer = 'adam', loss = 'mean_squared_error')\n"
      ],
      "metadata": {
        "id": "ffB2nQqyPC4o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Train the model\n",
        "#epochs is the number of iterations when the entire dataset is passed forward and backward through a NN\n",
        "model.fit(x_train, y_train, batch_size = 1, epochs = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kAMSDom1Pg9N",
        "outputId": "f6831e0a-4537-4f70-fa20-141562a7ed1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1952/1952 [==============================] - 50s 24ms/step - loss: 5.5412e-04\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa33c7931d0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Create the test data set\n",
        "#Create a new array containing scaled values from index 1952 to 2515\n",
        "test_data = scaled_data[training_data_len - 60: , :]\n",
        "#Create the data sets x_test and y_test (the values we want to predict)\n",
        "x_test = []\n",
        "y_test = dataset[training_data_len: , :]\n",
        "\n",
        "for i in range(60, len(test_data)):\n",
        "  x_test.append(test_data[i-60:i, 0])\n"
      ],
      "metadata": {
        "id": "N4ym9uCBQIJW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Convert the data to a numpy array\n",
        "x_test = np.array(x_test)"
      ],
      "metadata": {
        "id": "mjpqso5haEve"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Reshape the data to 3 dimensional for LSTM\n",
        "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))"
      ],
      "metadata": {
        "id": "d2PrApueamoA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Get the models predicted price values\n",
        "predictions = model.predict(x_test)\n",
        "predictions = scaler.inverse_transform(predictions) #unscaling"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rv25fAsUc3HH",
        "outputId": "4a528d65-03f0-47f6-8e1c-cfafae2bc82f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/16 [==============================] - 1s 15ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Model evaluation using Root Mean Square Error (RMSE)\n",
        "#Lower values of RMSE indicate a better fit\n",
        "rmse=np.sqrt(np.mean(((predictions- y_test)**2)))\n",
        "rmse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_sYu7ElmdkPh",
        "outputId": "2f2cd29c-39d6-4343-bf60-ebd14423cd49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.52219608085513"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot the data\n",
        "train = data[0:training_data_len]\n",
        "valid_data = data[training_data_len:]\n",
        "valid_data['Predictions'] = predictions\n",
        "\n",
        "#Visualize the data\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Model')\n",
        "plt.xlabel('Date', fontsize=18)\n",
        "plt.ylabel('Close price USD ($)', fontsize = 18)\n",
        "plt.plot(train['Close'])\n",
        "plt.plot(valid_data[['Close', 'Predictions']])\n",
        "plt.legend(['Train','Validation','Predictions'], loc=\"lower right\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "6i7F-G63iWME",
        "outputId": "71fb2712-3705-46dc-9595-a263fefa841b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Show the validation and predicted prices\n",
        "valid_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "VqS6JzFpjkV6",
        "outputId": "4b5cd524-5185-4893-95a2-1002fdde094d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Close  Predictions\n",
              "Date                               \n",
              "2020-10-19  115.980003   119.090454\n",
              "2020-10-20  117.510002   118.873550\n",
              "2020-10-21  116.870003   118.579742\n",
              "2020-10-22  115.750000   118.184471\n",
              "2020-10-23  115.040001   117.651932\n",
              "...                ...          ...\n",
              "2022-10-11  138.979996   142.971909\n",
              "2022-10-12  138.339996   142.039001\n",
              "2022-10-13  142.990005   141.083588\n",
              "2022-10-14  138.380005   140.778046\n",
              "2022-10-17  142.410004   140.239777\n",
              "\n",
              "[503 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8e96b8e1-49ff-453f-b453-fa59a6ccbd70\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "      <th>Predictions</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-10-19</th>\n",
              "      <td>115.980003</td>\n",
              "      <td>119.090454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-20</th>\n",
              "      <td>117.510002</td>\n",
              "      <td>118.873550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-21</th>\n",
              "      <td>116.870003</td>\n",
              "      <td>118.579742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-22</th>\n",
              "      <td>115.750000</td>\n",
              "      <td>118.184471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-23</th>\n",
              "      <td>115.040001</td>\n",
              "      <td>117.651932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-11</th>\n",
              "      <td>138.979996</td>\n",
              "      <td>142.971909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-12</th>\n",
              "      <td>138.339996</td>\n",
              "      <td>142.039001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-13</th>\n",
              "      <td>142.990005</td>\n",
              "      <td>141.083588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-14</th>\n",
              "      <td>138.380005</td>\n",
              "      <td>140.778046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17</th>\n",
              "      <td>142.410004</td>\n",
              "      <td>140.239777</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>503 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8e96b8e1-49ff-453f-b453-fa59a6ccbd70')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8e96b8e1-49ff-453f-b453-fa59a6ccbd70');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    }
  ]
}