stockpricepredicitonModel.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "stockpricepredicitonModel",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BNMEZsGjynjM"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AsR7ELouyq0G",
        "outputId": "3a1c3b78-591d-4ad3-c2d5-5d19345a2e1b"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM\n",
        "import math\n",
        "from sklearn.preprocessing import MinMaxScaler\n"
      ],
      "metadata": {
        "id": "Ik0Iabg3zfTp"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/ML stuff/all_stocks_5yr.csv')"
      ],
      "metadata": {
        "id": "aSpJq1B-0SdP"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "DGlT4mKI0czY",
        "outputId": "ae43ef28-b781-41b5-b858-73385f9b5c36"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date   open   high    low  close    volume Name\n",
              "0  2013-02-08  15.07  15.12  14.63  14.75   8407500  AAL\n",
              "1  2013-02-11  14.89  15.01  14.26  14.46   8882000  AAL\n",
              "2  2013-02-12  14.45  14.51  14.10  14.27   8126000  AAL\n",
              "3  2013-02-13  14.30  14.94  14.25  14.66  10259500  AAL\n",
              "4  2013-02-14  14.94  14.96  13.16  13.99  31879900  AAL"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-12b78a71-70cd-4cc8-8ff9-716bedb0da83\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2013-02-08</td>\n",
              "      <td>15.07</td>\n",
              "      <td>15.12</td>\n",
              "      <td>14.63</td>\n",
              "      <td>14.75</td>\n",
              "      <td>8407500</td>\n",
              "      <td>AAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2013-02-11</td>\n",
              "      <td>14.89</td>\n",
              "      <td>15.01</td>\n",
              "      <td>14.26</td>\n",
              "      <td>14.46</td>\n",
              "      <td>8882000</td>\n",
              "      <td>AAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013-02-12</td>\n",
              "      <td>14.45</td>\n",
              "      <td>14.51</td>\n",
              "      <td>14.10</td>\n",
              "      <td>14.27</td>\n",
              "      <td>8126000</td>\n",
              "      <td>AAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2013-02-13</td>\n",
              "      <td>14.30</td>\n",
              "      <td>14.94</td>\n",
              "      <td>14.25</td>\n",
              "      <td>14.66</td>\n",
              "      <td>10259500</td>\n",
              "      <td>AAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2013-02-14</td>\n",
              "      <td>14.94</td>\n",
              "      <td>14.96</td>\n",
              "      <td>13.16</td>\n",
              "      <td>13.99</td>\n",
              "      <td>31879900</td>\n",
              "      <td>AAL</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-12b78a71-70cd-4cc8-8ff9-716bedb0da83')\"\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-12b78a71-70cd-4cc8-8ff9-716bedb0da83 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-12b78a71-70cd-4cc8-8ff9-716bedb0da83');\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": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock_name = 'PSX'"
      ],
      "metadata": {
        "id": "rxV9o1kH1ADC"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data = data['Name'] == stock_name"
      ],
      "metadata": {
        "id": "Zj1ZqzGW1Syl"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Putting all the rows of specific stock in a variable\n",
        "final_data = data[all_data]"
      ],
      "metadata": {
        "id": "xSiVUb3d15LH"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "sXk8utA42MLv",
        "outputId": "8de834bd-d9e4-4b13-f43c-f5e576a93528"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              date   open   high    low  close   volume Name\n",
              "467759  2013-02-08  63.41  65.24  63.22  64.02  4745571  PSX\n",
              "467760  2013-02-11  64.00  64.66  63.72  64.18  3097765  PSX\n",
              "467761  2013-02-12  64.62  64.78  63.78  64.43  3701971  PSX\n",
              "467762  2013-02-13  64.50  64.50  63.85  64.11  2777089  PSX\n",
              "467763  2013-02-14  63.77  64.92  63.75  64.83  3559969  PSX"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2fe033b9-b20e-4c7e-a4bc-6b14460c2a19\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>467759</th>\n",
              "      <td>2013-02-08</td>\n",
              "      <td>63.41</td>\n",
              "      <td>65.24</td>\n",
              "      <td>63.22</td>\n",
              "      <td>64.02</td>\n",
              "      <td>4745571</td>\n",
              "      <td>PSX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>467760</th>\n",
              "      <td>2013-02-11</td>\n",
              "      <td>64.00</td>\n",
              "      <td>64.66</td>\n",
              "      <td>63.72</td>\n",
              "      <td>64.18</td>\n",
              "      <td>3097765</td>\n",
              "      <td>PSX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>467761</th>\n",
              "      <td>2013-02-12</td>\n",
              "      <td>64.62</td>\n",
              "      <td>64.78</td>\n",
              "      <td>63.78</td>\n",
              "      <td>64.43</td>\n",
              "      <td>3701971</td>\n",
              "      <td>PSX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>467762</th>\n",
              "      <td>2013-02-13</td>\n",
              "      <td>64.50</td>\n",
              "      <td>64.50</td>\n",
              "      <td>63.85</td>\n",
              "      <td>64.11</td>\n",
              "      <td>2777089</td>\n",
              "      <td>PSX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>467763</th>\n",
              "      <td>2013-02-14</td>\n",
              "      <td>63.77</td>\n",
              "      <td>64.92</td>\n",
              "      <td>63.75</td>\n",
              "      <td>64.83</td>\n",
              "      <td>3559969</td>\n",
              "      <td>PSX</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2fe033b9-b20e-4c7e-a4bc-6b14460c2a19')\"\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-2fe033b9-b20e-4c7e-a4bc-6b14460c2a19 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-2fe033b9-b20e-4c7e-a4bc-6b14460c2a19');\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": [
        "#  Plotting date vs the close market stock price\n",
        "final_data.plot('date','close',color=\"red\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "wKiXxUoR34-F",
        "outputId": "bd5511cf-a5f4-4076-95a9-de9fc1f13929"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5e1d53e7d0>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract only top 60 rows to make the plot a little clearer\n",
        "new_data = final_data.head(60)"
      ],
      "metadata": {
        "id": "lRRLU3MM3-ea"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Plotting date vs the close  market stock price\n",
        "new_data.plot('date','close',color=\"green\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "ycBxcBlLGflR",
        "outputId": "c3b3dbce-dce6-4e95-9853-8f226d96278a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5e1d497d10>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_data.plot('date','high',color=\"blue\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "NUTRwLKsGlZ2",
        "outputId": "2bdb9602-533f-43e8-b433-cac4ee9f6eb8"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5e1cf7ae50>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_data.plot('date','low',color=\"yellow\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "WT0UG4D4Dv6O",
        "outputId": "c15091a5-5ca9-4587-e7ff-e5652301dbdc"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5e1cf7ae90>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_data.plot('date','volume',color=\"black\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "CMfS6o9BD2_u",
        "outputId": "0d7e1b40-ecd3-4e79-d03e-1a0e8b5ba946"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5e1ce61d50>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating a new Dataframe and Training data\n"
      ],
      "metadata": {
        "id": "YgI2bKolEkNy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Filter out the closing market price data\n",
        "close_data = final_data.filter(['close'])"
      ],
      "metadata": {
        "id": "o6aIfTPkEoNp"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Convert the data into array for easy evaluation\n",
        "dataset = close_data.values"
      ],
      "metadata": {
        "id": "avYJCqxTEqjw"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Scale/Normalize the data to make all values between 0 and 1\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "scaled_data = scaler.fit_transform(dataset)\n",
        " "
      ],
      "metadata": {
        "id": "gPyLwGlrEuea"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Creating training data size : 70% of the data\n",
        "training_data_len = math.ceil(len(dataset) *.7)\n",
        "train_data = scaled_data[0:training_data_len  , : ]"
      ],
      "metadata": {
        "id": "0nA9hKOfHsVF"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 5. Separating the data into x and y data\n",
        "x_train_data=[]\n",
        "y_train_data =[]\n",
        "for i in range(60,len(train_data)):\n",
        "    x_train_data=list(x_train_data)\n",
        "    y_train_data=list(y_train_data)\n",
        "    x_train_data.append(train_data[i-60:i,0])\n",
        "    y_train_data.append(train_data[i,0])"
      ],
      "metadata": {
        "id": "hwqA_Y-UHwac"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 6. Converting the training x and y values to numpy arrays\n",
        "x_train_data1, y_train_data1 = np.array(x_train_data), np.array(y_train_data)"
      ],
      "metadata": {
        "id": "--UJFhgKHzOH"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 7. Reshaping training s and y data to make the calculations easier\n",
        "x_train_data2 = np.reshape(x_train_data1, (x_train_data1.shape[0],x_train_data1.shape[1],1))\n"
      ],
      "metadata": {
        "id": "tpHXG3aJH4RR"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(units=50, return_sequences=True,input_shape=(x_train_data2.shape[1],1)))\n",
        "model.add(LSTM(units=50, return_sequences=False))\n",
        "model.add(Dense(units=25))\n",
        "model.add(Dense(units=1))"
      ],
      "metadata": {
        "id": "4nv0DiayIJPA"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "model.fit(x_train_data2, y_train_data1, batch_size=1, epochs=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JHLp_r5cIMqd",
        "outputId": "36c6b20d-5dca-4ea0-b79b-572a9c51f620"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "822/822 [==============================] - 26s 27ms/step - loss: 0.0040\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f5e18ec48d0>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Creating a dataset for testing\n",
        "test_data = scaled_data[training_data_len - 60: , : ]\n",
        "x_test = []\n",
        "y_test =  dataset[training_data_len : , : ]\n",
        "for i in range(60,len(test_data)):\n",
        "    x_test.append(test_data[i-60:i,0])"
      ],
      "metadata": {
        "id": "-x_bdkO-ISuF"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 2.  Convert the values into arrays for easier computation\n",
        "x_test = np.array(x_test)\n",
        "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))"
      ],
      "metadata": {
        "id": "CIlLO4SIIVy0"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Making predictions on the testing data\n",
        "predictions = model.predict(x_test)\n",
        "predictions = scaler.inverse_transform(predictions)\n"
      ],
      "metadata": {
        "id": "DPtjheK1I4QG"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rmse=np.sqrt(np.mean(((predictions- y_test)**2)))\n",
        "print(rmse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eHNXyejUI8Di",
        "outputId": "7d403bdd-b22c-4089-81b1-e9bf238a5c12"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.3940031608541936\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = data[:training_data_len]\n",
        "valid = data[training_data_len:]\n",
        " \n",
        "plt.title('Model')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Close')\n",
        " \n",
        "plt.plot(train['close'])\n",
        "plt.plot(valid[['close', 'Predictions']])\n",
        " \n",
        "plt.legend(['Train', 'Val', 'Predictions'], loc='lower right')\n",
        " \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "_vApkHrQJCDs",
        "outputId": "94cf0a4b-3a0a-4f5b-a1f0-9aa0db1eb310"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-77-352630754994>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mvalid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtraining_data_len\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mvalid\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Predictions'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Model'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   3610\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3611\u001b[0m             \u001b[0;31m# set column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3612\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3613\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3614\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   3782\u001b[0m         \u001b[0mensure\u001b[0m \u001b[0mhomogeneity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3783\u001b[0m         \"\"\"\n\u001b[0;32m-> 3784\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3785\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3786\u001b[0m         if (\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_sanitize_column\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m   4507\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4508\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4509\u001b[0;31m             \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequire_length_match\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4510\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msanitize_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/common.py\u001b[0m in \u001b[0;36mrequire_length_match\u001b[0;34m(data, index)\u001b[0m\n\u001b[1;32m    530\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    531\u001b[0m         raise ValueError(\n\u001b[0;32m--> 532\u001b[0;31m             \u001b[0;34m\"Length of values \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    533\u001b[0m             \u001b[0;34mf\"({len(data)}) \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    534\u001b[0m             \u001b[0;34m\"does not match length of index \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Length of values (377) does not match length of index (618158)"
          ]
        }
      ]
    }
  ]
}