Apple_Stock_Price_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Apple Stock Price Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNMkYcs+2Td+ZV1rx1cC5FS"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "edif3gdYYkBu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# This program uses artifical nueral netwrok called long short term memeory (LSTM) \n",
        "# to predict closing price of the Apple using past 60 day data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJMRrMadZLh6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "4c63479d-2a49-4a46-9a0a-8d4368170493"
      },
      "source": [
        "import math\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import pandas_datareader as web\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')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C4gAskoNaJxu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "outputId": "2f274a58-332b-4114-a004-ac8340f535fa"
      },
      "source": [
        "# Get stock Quote of Apple Inc.\n",
        "df = web.DataReader('AAPL', data_source='yahoo', start='2012-01-01', end='2020-01-30')\n",
        "# Show the data\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2012-01-03</th>\n",
              "      <td>58.928570</td>\n",
              "      <td>58.428570</td>\n",
              "      <td>58.485714</td>\n",
              "      <td>58.747143</td>\n",
              "      <td>75555200.0</td>\n",
              "      <td>50.994907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-04</th>\n",
              "      <td>59.240002</td>\n",
              "      <td>58.468571</td>\n",
              "      <td>58.571430</td>\n",
              "      <td>59.062859</td>\n",
              "      <td>65005500.0</td>\n",
              "      <td>51.268970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-05</th>\n",
              "      <td>59.792858</td>\n",
              "      <td>58.952858</td>\n",
              "      <td>59.278572</td>\n",
              "      <td>59.718571</td>\n",
              "      <td>67817400.0</td>\n",
              "      <td>51.838169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-06</th>\n",
              "      <td>60.392857</td>\n",
              "      <td>59.888573</td>\n",
              "      <td>59.967144</td>\n",
              "      <td>60.342857</td>\n",
              "      <td>79573200.0</td>\n",
              "      <td>52.380054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-09</th>\n",
              "      <td>61.107143</td>\n",
              "      <td>60.192856</td>\n",
              "      <td>60.785713</td>\n",
              "      <td>60.247143</td>\n",
              "      <td>98506100.0</td>\n",
              "      <td>52.296970</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>2020-01-24</th>\n",
              "      <td>323.329987</td>\n",
              "      <td>317.519989</td>\n",
              "      <td>320.250000</td>\n",
              "      <td>318.309998</td>\n",
              "      <td>36634400.0</td>\n",
              "      <td>317.556335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-27</th>\n",
              "      <td>311.769989</td>\n",
              "      <td>304.880005</td>\n",
              "      <td>310.059998</td>\n",
              "      <td>308.950012</td>\n",
              "      <td>40485000.0</td>\n",
              "      <td>308.218506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-28</th>\n",
              "      <td>318.399994</td>\n",
              "      <td>312.190002</td>\n",
              "      <td>312.600006</td>\n",
              "      <td>317.690002</td>\n",
              "      <td>40558500.0</td>\n",
              "      <td>316.937805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-29</th>\n",
              "      <td>327.850006</td>\n",
              "      <td>321.380005</td>\n",
              "      <td>324.450012</td>\n",
              "      <td>324.339996</td>\n",
              "      <td>54057300.0</td>\n",
              "      <td>323.572052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-30</th>\n",
              "      <td>324.089996</td>\n",
              "      <td>318.750000</td>\n",
              "      <td>320.540009</td>\n",
              "      <td>323.869995</td>\n",
              "      <td>31685800.0</td>\n",
              "      <td>323.103180</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2032 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  High         Low  ...      Volume   Adj Close\n",
              "Date                                ...                        \n",
              "2012-01-03   58.928570   58.428570  ...  75555200.0   50.994907\n",
              "2012-01-04   59.240002   58.468571  ...  65005500.0   51.268970\n",
              "2012-01-05   59.792858   58.952858  ...  67817400.0   51.838169\n",
              "2012-01-06   60.392857   59.888573  ...  79573200.0   52.380054\n",
              "2012-01-09   61.107143   60.192856  ...  98506100.0   52.296970\n",
              "...                ...         ...  ...         ...         ...\n",
              "2020-01-24  323.329987  317.519989  ...  36634400.0  317.556335\n",
              "2020-01-27  311.769989  304.880005  ...  40485000.0  308.218506\n",
              "2020-01-28  318.399994  312.190002  ...  40558500.0  316.937805\n",
              "2020-01-29  327.850006  321.380005  ...  54057300.0  323.572052\n",
              "2020-01-30  324.089996  318.750000  ...  31685800.0  323.103180\n",
              "\n",
              "[2032 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QmKe2tXjbjQn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "60d8d2c8-8c2f-4f5a-d58f-63bf05c2cc00"
      },
      "source": [
        "# Get the number of rows and column\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2032, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3chbA22Dbrur",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        },
        "outputId": "e8d3a5c8-e8ad-4eed-90d8-77ef398e0e4f"
      },
      "source": [
        "# Visualize the closing price history\n",
        "plt.figure(figsize = (16,8))\n",
        "plt.title('Close Price Histroy')\n",
        "plt.plot(df['Close'])\n",
        "plt.xlabel('Date',fontsize='16')\n",
        "plt.ylabel('Close Price', fontsize='16')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/plotting/_matplotlib/converter.py:103: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
            "\n",
            "To register the converters:\n",
            "\t>>> from pandas.plotting import register_matplotlib_converters\n",
            "\t>>> register_matplotlib_converters()\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABCkAAAIaCAYAAAANyBdeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIiIiMgo/D8FkEOV6AvQ8QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0MsQ6MOjdCZ_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "46f29372-3967-428c-ab42-a8f6502eae1d"
      },
      "source": [
        "# Create a new dataframe with only close column\n",
        "data = df.filter(['Close'])\n",
        "# Convert the dataframe to numpy \n",
        "dataset = data.values\n",
        "# Get the number of rows to train the model on\n",
        "training_data_len = math.ceil(len(dataset)*0.8)\n",
        "training_data_len"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1626"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KBfKn765elre",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "a8d5be3f-711f-41a1-e695-589a083c399e"
      },
      "source": [
        "# Scale the data\n",
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "scaled_data = scaler.fit_transform(dataset)\n",
        "scaled_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.01101151],\n",
              "       [0.01218714],\n",
              "       [0.01462882],\n",
              "       ...,\n",
              "       [0.97523741],\n",
              "       [1.        ],\n",
              "       [0.99824986]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sezW3fCpfJSf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "outputId": "9caf7600-4d30-4dcc-a348-4b70c5bb6cb3"
      },
      "source": [
        "# Create the training dataset\n",
        "# Create the scaled training dataset\n",
        "train_data = scaled_data[0:training_data_len, :]\n",
        "# Split the data in xtrain and ytrain dataset\n",
        "x_train = []\n",
        "y_train = []\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",
        "\n",
        "# In first pass through the lopp x_train has one array but in second pass thorugh\n",
        "# there are two array. Second array's first element is equal to second element of\n",
        "# first array and y_train's first element is the same of the second array's last \n",
        "# element"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[array([0.01101151, 0.01218714, 0.01462882, 0.01695348, 0.01659707,\n",
            "       0.01740032, 0.01703327, 0.0164162 , 0.01557571, 0.01817698,\n",
            "       0.02052292, 0.01979945, 0.01583637, 0.01961858, 0.01589488,\n",
            "       0.02985876, 0.02877888, 0.03018858, 0.03323669, 0.03508257,\n",
            "       0.03492831, 0.03435912, 0.03678485, 0.03906693, 0.04165226,\n",
            "       0.04582813, 0.0546001 , 0.05473309, 0.05961647, 0.06326568,\n",
            "       0.05699389, 0.05940899, 0.05936112, 0.06613295, 0.06517009,\n",
            "       0.06695214, 0.07015452, 0.0719366 , 0.07706998, 0.08080965,\n",
            "       0.0818895 , 0.0822672 , 0.07587309, 0.07433039, 0.07455914,\n",
            "       0.08057024, 0.08226189, 0.08589514, 0.09445966, 0.10588611,\n",
            "       0.10374764, 0.10375295, 0.11201426, 0.11459956, 0.11275899,\n",
            "       0.11107802, 0.10932788, 0.11514215, 0.11913185, 0.1208022 ])]\n",
            "[0.11667419757475875]\n",
            "[array([0.01101151, 0.01218714, 0.01462882, 0.01695348, 0.01659707,\n",
            "       0.01740032, 0.01703327, 0.0164162 , 0.01557571, 0.01817698,\n",
            "       0.02052292, 0.01979945, 0.01583637, 0.01961858, 0.01589488,\n",
            "       0.02985876, 0.02877888, 0.03018858, 0.03323669, 0.03508257,\n",
            "       0.03492831, 0.03435912, 0.03678485, 0.03906693, 0.04165226,\n",
            "       0.04582813, 0.0546001 , 0.05473309, 0.05961647, 0.06326568,\n",
            "       0.05699389, 0.05940899, 0.05936112, 0.06613295, 0.06517009,\n",
            "       0.06695214, 0.07015452, 0.0719366 , 0.07706998, 0.08080965,\n",
            "       0.0818895 , 0.0822672 , 0.07587309, 0.07433039, 0.07455914,\n",
            "       0.08057024, 0.08226189, 0.08589514, 0.09445966, 0.10588611,\n",
            "       0.10374764, 0.10375295, 0.11201426, 0.11459956, 0.11275899,\n",
            "       0.11107802, 0.10932788, 0.11514215, 0.11913185, 0.1208022 ]), array([0.01218714, 0.01462882, 0.01695348, 0.01659707, 0.01740032,\n",
            "       0.01703327, 0.0164162 , 0.01557571, 0.01817698, 0.02052292,\n",
            "       0.01979945, 0.01583637, 0.01961858, 0.01589488, 0.02985876,\n",
            "       0.02877888, 0.03018858, 0.03323669, 0.03508257, 0.03492831,\n",
            "       0.03435912, 0.03678485, 0.03906693, 0.04165226, 0.04582813,\n",
            "       0.0546001 , 0.05473309, 0.05961647, 0.06326568, 0.05699389,\n",
            "       0.05940899, 0.05936112, 0.06613295, 0.06517009, 0.06695214,\n",
            "       0.07015452, 0.0719366 , 0.07706998, 0.08080965, 0.0818895 ,\n",
            "       0.0822672 , 0.07587309, 0.07433039, 0.07455914, 0.08057024,\n",
            "       0.08226189, 0.08589514, 0.09445966, 0.10588611, 0.10374764,\n",
            "       0.10375295, 0.11201426, 0.11459956, 0.11275899, 0.11107802,\n",
            "       0.10932788, 0.11514215, 0.11913185, 0.1208022 , 0.1166742 ])]\n",
            "[0.11667419757475875, 0.11118972675232822]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6kWygTqEhnM8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Convert the x_train and y_train to numpy\n",
        "x_train, y_train = np.array(x_train),np.array(y_train)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uclK8jo_iv9r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "866ac86f-f1f8-4ef7-af37-c5f715c24768"
      },
      "source": [
        "# Reshape the x_train data\n",
        "# LSTM network expects the input to be in 3D in form of no.of sample, no. of timestep\n",
        "# and no. of feature\n",
        "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1],1))\n",
        "x_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1566, 60, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZF3e-ucfjvoz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "4420fa47-f3d8-4eb0-84c4-d82154102056"
      },
      "source": [
        "# Build the Model\n",
        "model = Sequential()\n",
        "model.add(LSTM(50, return_sequences=True, input_shape = (x_train.shape[1],1)))\n",
        "# return_sequence = True because we are using another LSTM Layer for model architecture\n",
        "model.add(LSTM(50, return_sequences=False, ))\n",
        "model.add(Dense(25))\n",
        "model.add(Dense(1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVnCaopdlPnZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "836df667-50fd-43af-9873-6b6d8eb57811"
      },
      "source": [
        "# Complie the model\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "# Optimizer is used to upon imporve loss function\n",
        "# Loss function is used to mesaure how well the model did on training"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wx53lXL3lw_m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "outputId": "dc772eb3-3d23-4846-81b4-30eea665d5c3"
      },
      "source": [
        "# Train the model\n",
        "model.fit(x_train, y_train, batch_size=1,epochs=1)\n",
        "# Epochs is the number of iteration when the entire dataset is passed forward and backward in neural network"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "Epoch 1/1\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "1566/1566 [==============================] - 282s 180ms/step - loss: 4.8309e-04\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f09b2cf1c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ZVhZkxX4T0V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create the testing dataset\n",
        "# Create new array conatining containing scaled value from index 1566 to 2032\n",
        "test_data = scaled_data[training_data_len-60:, :]\n",
        "# Create the datasets x_test and y_test\n",
        "x_test = []\n",
        "y_test = dataset[training_data_len:, :]\n",
        "# y_test will be the value we want our model to predict\n",
        "for i in range(60,len(test_data)):\n",
        "  x_test.append(test_data[i-60:i,0])\n",
        "  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P32RTp5z537a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Convert the data into a numpy array so that we can use it in LSTM Model\n",
        "x_test =np.array(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5gsy3G_56Gg8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Reshape the data\n",
        "x_test = np.reshape(x_test,(x_test.shape[0],x_test.shape[1],1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "547HL0qb6ua-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Get the models predicted price values\n",
        "predictions = model.predict(x_test)\n",
        "predictions = scaler.inverse_transform(predictions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIk0uTkU7PJs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a684347a-f657-4bd5-a4cc-d019e0e996f0"
      },
      "source": [
        "# Evalue the model br getting root mean squared error (RMSE) \n",
        "# Lower value of RMSE indicated better fit\n",
        "rmse = np.sqrt(np.mean(predictions - y_test)**2)\n",
        "rmse"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.9613851537845406"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QX4E1nHV77qt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "outputId": "c333c951-672d-448f-803b-e2e78c5455c0"
      },
      "source": [
        "# Plot the data\n",
        "train = data[:training_data_len]\n",
        "valid = data[training_data_len:]\n",
        "valid['Prediction'] = predictions\n",
        "# Visualize the data\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Model')\n",
        "plt.xlabel('Date',fontsize=16)\n",
        "plt.ylabel('Close Price',fontsize=16)\n",
        "plt.plot(train['Close'])\n",
        "plt.plot(valid[['Close','Prediction']])\n",
        "plt.legend(['Train', 'Val', 'Prediction'], loc='lower right')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABCkAAAIaCAYAAAANyBdeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FgZT_Wn89Vft",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "outputId": "44e23b68-5979-414d-af57-64153fc38c67"
      },
      "source": [
        "# Show the valid and predicted price\n",
        "valid"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "      <th>Prediction</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>2018-06-20</th>\n",
              "      <td>186.500000</td>\n",
              "      <td>187.653137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-06-21</th>\n",
              "      <td>185.460007</td>\n",
              "      <td>186.818542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-06-22</th>\n",
              "      <td>184.919998</td>\n",
              "      <td>186.015945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-06-25</th>\n",
              "      <td>182.169998</td>\n",
              "      <td>185.283035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-06-26</th>\n",
              "      <td>184.429993</td>\n",
              "      <td>184.311951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-24</th>\n",
              "      <td>318.309998</td>\n",
              "      <td>305.822266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-27</th>\n",
              "      <td>308.950012</td>\n",
              "      <td>306.426971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-28</th>\n",
              "      <td>317.690002</td>\n",
              "      <td>305.480682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-29</th>\n",
              "      <td>324.339996</td>\n",
              "      <td>305.295685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-30</th>\n",
              "      <td>323.869995</td>\n",
              "      <td>306.429321</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>406 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Close  Prediction\n",
              "Date                              \n",
              "2018-06-20  186.500000  187.653137\n",
              "2018-06-21  185.460007  186.818542\n",
              "2018-06-22  184.919998  186.015945\n",
              "2018-06-25  182.169998  185.283035\n",
              "2018-06-26  184.429993  184.311951\n",
              "...                ...         ...\n",
              "2020-01-24  318.309998  305.822266\n",
              "2020-01-27  308.950012  306.426971\n",
              "2020-01-28  317.690002  305.480682\n",
              "2020-01-29  324.339996  305.295685\n",
              "2020-01-30  323.869995  306.429321\n",
              "\n",
              "[406 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ffIg2TEi9sv3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "435b846b-b8d0-46ed-c537-843e51f47827"
      },
      "source": [
        "# Get the quote\n",
        "apple_quote = web.DataReader('AAPL', data_source='yahoo', start='2012-01-01', end='2020-02-20')\n",
        "# Create a new dataframe\n",
        "new_df = apple_quote.filter(['Close'])\n",
        "# Ge the last 60 day close price value and convert the dataframe to array\n",
        "last_60_days = new_df[-60:].values\n",
        "# Scale the data to be values between 0 & 1\n",
        "last_60_days_scaled = scaler.transform(last_60_days)\n",
        "# Create an empty list\n",
        "X_test = []\n",
        "# Append the past 60 days\n",
        "X_test.append(last_60_days_scaled)\n",
        "# Convert the X_test to numpy array\n",
        "X_test = np.array(X_test)\n",
        "# Reshape the data\n",
        "X_test = np.reshape(X_test,(X_test.shape[0],X_test.shape[1],1))\n",
        "# Get the predicted scale data\n",
        "pred_price = model.predict(X_test)\n",
        "# undo the scaling\n",
        "pred_price = scaler.inverse_transform(pred_price)\n",
        "print(pred_price)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[312.1597]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Zkj-jrC_zyA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "a37a69ce-daae-4b1d-f5e2-cada78375765"
      },
      "source": [
        "# Get the quote\n",
        "apple_quote2 = web.DataReader('AAPL', data_source='yahoo', start='2020-02-20', end='2020-02-20')\n",
        "print(apple_quote2['Close'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Date\n",
            "2020-02-19    323.619995\n",
            "Name: Close, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}