Deep_Learning_Model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Deep_Learning_Model.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1KuYp2WlzjRG-MGmNx00tSzP4-gMj8jYy",
      "authorship_tag": "ABX9TyM4xlL433J3ix0OmGgqGEZR",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/amanjain252002/Stock-Price-Prediction/blob/main/Deep_Learning_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a92nHELU1gxI"
      },
      "source": [
        "import math\r\n",
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "import tensorflow as tf\r\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\r\n",
        "\r\n",
        "import warnings\r\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cniPewwH16-t"
      },
      "source": [
        "AAPL = pd.read_csv(\"/content/drive/MyDrive/Database/Pre_Processed_AAPL.csv\")\r\n",
        "TSLA = pd.read_csv(\"/content/drive/MyDrive/Database/Pre_Processed_TSLA.csv\")\r\n",
        "GOOG = pd.read_csv(\"/content/drive/MyDrive/Database/Pre_Processed_GOOG.csv\")\r\n",
        "MSFT = pd.read_csv(\"/content/drive/MyDrive/Database/Pre_Processed_MSFT.csv\")\r\n",
        "AMZN = pd.read_csv(\"/content/drive/MyDrive/Database/Pre_Processed_AMZN.csv\")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vDdZr0DH0sor"
      },
      "source": [
        "### Data Proprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kkZs6LC9QPMp"
      },
      "source": [
        "def Dataset(Data, Date):\r\n",
        "\r\n",
        "  Train_Data = Data['Adj. Close'][Data['Date'] < Date].to_numpy()\r\n",
        "  Data_Train = []\r\n",
        "  Data_Train_X = []\r\n",
        "  Data_Train_Y = []\r\n",
        "  for i in range(0, len(Train_Data), 5):\r\n",
        "    try:\r\n",
        "      Data_Train.append(Train_Data[i : i + 5])\r\n",
        "    except:\r\n",
        "      pass\r\n",
        "\r\n",
        "  if len(Data_Train[-1]) < 5:\r\n",
        "    Data_Train.pop(-1)\r\n",
        "  \r\n",
        "  Data_Train_X = Data_Train[0 : -1]\r\n",
        "  Data_Train_X = np.array(Data_Train_X)\r\n",
        "  Data_Train_X = Data_Train_X.reshape((-1, 5, 1))\r\n",
        "  Data_Train_Y = Data_Train[1 : len(Data_Train)]\r\n",
        "  Data_Train_Y = np.array(Data_Train_Y)\r\n",
        "  Data_Train_Y = Data_Train_Y.reshape((-1, 5, 1))\r\n",
        "\r\n",
        "\r\n",
        "  Test_Data = Data['Adj. Close'][Data['Date'] >= Date].to_numpy()\r\n",
        "  Data_Test = []\r\n",
        "  Data_Test_X = []\r\n",
        "  Data_Test_Y = []\r\n",
        "  for i in range(0, len(Test_Data), 5):\r\n",
        "    try:\r\n",
        "      Data_Test.append(Test_Data[i : i + 5])\r\n",
        "    except:\r\n",
        "      pass\r\n",
        "\r\n",
        "  if len(Data_Test[-1]) < 5:\r\n",
        "    Data_Test.pop(-1)\r\n",
        "  \r\n",
        "  Data_Test_X = Data_Test[0 : -1]\r\n",
        "  Data_Test_X = np.array(Data_Test_X)\r\n",
        "  Data_Test_X = Data_Test_X.reshape((-1, 5, 1))\r\n",
        "  Data_Test_Y = Data_Test[1 : len(Data_Test)]\r\n",
        "  Data_Test_Y = np.array(Data_Test_Y)\r\n",
        "  Data_Test_Y = Data_Test_Y.reshape((-1, 5, 1))\r\n",
        "\r\n",
        "  return Data_Train_X, Data_Train_Y, Data_Test_X, Data_Test_Y"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fFmGO6j50wbO"
      },
      "source": [
        "### Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DPWMIKGv_7b1"
      },
      "source": [
        "def Model():\r\n",
        "  model = tf.keras.models.Sequential([\r\n",
        "                                      tf.keras.layers.LSTM(200, input_shape = (5, 1), activation = tf.nn.leaky_relu, return_sequences = True),\r\n",
        "                                      tf.keras.layers.LSTM(200, activation = tf.nn.leaky_relu),\r\n",
        "                                      tf.keras.layers.Dense(200, activation = tf.nn.leaky_relu),\r\n",
        "                                      tf.keras.layers.Dense(100, activation = tf.nn.leaky_relu),\r\n",
        "                                      tf.keras.layers.Dense(50, activation = tf.nn.leaky_relu),\r\n",
        "                                      tf.keras.layers.Dense(5, activation = tf.nn.leaky_relu)\r\n",
        "                                      ])\r\n",
        "  return model"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-zXwjAyrGm7"
      },
      "source": [
        "model = Model()"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "id": "RAS71VonrLzU",
        "outputId": "18166993-ab89-4ce0-84ea-f367714824db"
      },
      "source": [
        "tf.keras.utils.plot_model(model, show_shapes=True)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "neXmHN79rO0k",
        "outputId": "307f6638-11fe-4ad2-c5bb-51fe1611d996"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm (LSTM)                  (None, 5, 200)            161600    \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 200)               320800    \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 200)               40200     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 100)               20100     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 50)                5050      \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 5)                 255       \n",
            "=================================================================\n",
            "Total params: 548,005\n",
            "Trainable params: 548,005\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vJDxyTC40zmg"
      },
      "source": [
        "### Custom Learning Rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vLjevQLkiIoP"
      },
      "source": [
        "def scheduler(epoch):\r\n",
        "  \r\n",
        "  if epoch <= 150:\r\n",
        "    lrate = (10 ** -5) * (epoch / 150) \r\n",
        "  elif epoch <= 400:\r\n",
        "    initial_lrate = (10 ** -5)\r\n",
        "    k = 0.01\r\n",
        "    lrate = initial_lrate * math.exp(-k * (epoch - 150))\r\n",
        "  else:\r\n",
        "    lrate = (10 ** -6)\r\n",
        "  \r\n",
        "  return lrate"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "H5Aoh-QGiLh-",
        "outputId": "0d26ee6d-1c08-40fb-c73d-9680afab6ba1"
      },
      "source": [
        "epochs = [i for i in range(1, 1001, 1)]\r\n",
        "lrate = [scheduler(i) for i in range(1, 1001, 1)]\r\n",
        "plt.plot(epochs, lrate)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9688b03150>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R0NG_mhJiQH_"
      },
      "source": [
        "callback = tf.keras.callbacks.LearningRateScheduler(scheduler)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P9l6y-jBQL-G"
      },
      "source": [
        "#Apple"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "thrQ_-6x7_7m",
        "outputId": "95ad792e-1c99-4e80-b987-9ca65ffe5fac"
      },
      "source": [
        "AAPL.head()"
      ],
      "execution_count": 11,
      "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>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj. Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-02</td>\n",
              "      <td>27.85</td>\n",
              "      <td>27.86</td>\n",
              "      <td>26.84</td>\n",
              "      <td>27.33</td>\n",
              "      <td>24.86</td>\n",
              "      <td>212818400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-05</td>\n",
              "      <td>27.07</td>\n",
              "      <td>27.16</td>\n",
              "      <td>26.35</td>\n",
              "      <td>26.56</td>\n",
              "      <td>24.16</td>\n",
              "      <td>257142000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-06</td>\n",
              "      <td>26.64</td>\n",
              "      <td>26.86</td>\n",
              "      <td>26.16</td>\n",
              "      <td>26.57</td>\n",
              "      <td>24.16</td>\n",
              "      <td>263188400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-07</td>\n",
              "      <td>26.80</td>\n",
              "      <td>27.05</td>\n",
              "      <td>26.67</td>\n",
              "      <td>26.94</td>\n",
              "      <td>24.50</td>\n",
              "      <td>160423600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-08</td>\n",
              "      <td>27.31</td>\n",
              "      <td>28.04</td>\n",
              "      <td>27.17</td>\n",
              "      <td>27.97</td>\n",
              "      <td>25.44</td>\n",
              "      <td>237458000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date   Open   High    Low  Close  Adj. Close       Volume\n",
              "0  2015-01-02  27.85  27.86  26.84  27.33       24.86  212818400.0\n",
              "1  2015-01-05  27.07  27.16  26.35  26.56       24.16  257142000.0\n",
              "2  2015-01-06  26.64  26.86  26.16  26.57       24.16  263188400.0\n",
              "3  2015-01-07  26.80  27.05  26.67  26.94       24.50  160423600.0\n",
              "4  2015-01-08  27.31  28.04  27.17  27.97       25.44  237458000.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IA_pY7CN8B0x",
        "outputId": "41684e67-b44b-4a2b-f80a-122356808927"
      },
      "source": [
        "AAPL.info()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1549 entries, 0 to 1548\n",
            "Data columns (total 7 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Date        1549 non-null   object \n",
            " 1   Open        1549 non-null   float64\n",
            " 2   High        1549 non-null   float64\n",
            " 3   Low         1549 non-null   float64\n",
            " 4   Close       1549 non-null   float64\n",
            " 5   Adj. Close  1549 non-null   float64\n",
            " 6   Volume      1549 non-null   float64\n",
            "dtypes: float64(6), object(1)\n",
            "memory usage: 84.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "elqcyE_H3h2S"
      },
      "source": [
        "# Change Dtype of Date column\r\n",
        "AAPL[\"Date\"] = pd.to_datetime(AAPL[\"Date\"])"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FPawFX7U5VVz"
      },
      "source": [
        "###Split the Data into Training and Test set\r\n",
        "    Training Period: 2015-01-02 - 2020-09-30\r\n",
        "\r\n",
        "    Testing Period:  2020-10-01 - 2021-02-26"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cdu0-_N238N2"
      },
      "source": [
        "AAPL_Date = '2020-10-01'\r\n",
        "AAPL_Train_X, AAPL_Train_Y, AAPL_Test_X, AAPL_Test_Y = Dataset(AAPL, AAPL_Date)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WhsAWnFP1-_t"
      },
      "source": [
        "### Model Fitting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yMBW7DTXAOo6"
      },
      "source": [
        "AAPL_Model = Model()"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YIN5tmbx9Pz5"
      },
      "source": [
        "AAPL_Model.compile(optimizer = tf.keras.optimizers.Adam(), loss = 'mse', metrics = tf.keras.metrics.RootMeanSquaredError())"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BNxXmS48BxEb"
      },
      "source": [
        "AAPL_hist = AAPL_Model.fit(AAPL_Train_X, AAPL_Train_Y, epochs = 1000, validation_data = (AAPL_Test_X, AAPL_Test_Y), callbacks=[callback])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GI_rlBQ4HBim"
      },
      "source": [
        "history_dict = AAPL_hist.history\r\n",
        "\r\n",
        "loss = history_dict[\"loss\"]\r\n",
        "root_mean_squared_error = history_dict[\"root_mean_squared_error\"]\r\n",
        "val_loss = history_dict[\"val_loss\"]\r\n",
        "val_root_mean_squared_error = history_dict[\"val_root_mean_squared_error\"]\r\n",
        "\r\n",
        "epochs = range(1, len(loss) + 1)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "ExABS58lFHrS",
        "outputId": "8f3d3aaa-f907-47b3-c6fb-f617d6774e25"
      },
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2)\r\n",
        "\r\n",
        "fig.set_figheight(5)\r\n",
        "fig.set_figwidth(15)\r\n",
        "\r\n",
        "ax1.plot(epochs, loss, label = 'Training Loss')\r\n",
        "ax1.plot(epochs, val_loss, label = 'Validation Loss')\r\n",
        "ax1.set(xlabel = \"Epochs\", ylabel = \"Loss\")\r\n",
        "ax1.legend()\r\n",
        "\r\n",
        "ax2.plot(epochs, root_mean_squared_error, label = \"Training Root Mean Squared Error\")\r\n",
        "ax2.plot(epochs, val_root_mean_squared_error, label = \"Validation Root Mean Squared Error\")\r\n",
        "ax2.set(xlabel = \"Epochs\", ylabel = \"Loss\")\r\n",
        "ax2.legend()\r\n",
        "\r\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zrM4MV_A1Y26"
      },
      "source": [
        "### Predicting the closing stock price of Apple"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BlBhKm6dEye7"
      },
      "source": [
        "AAPL_prediction = AAPL_Model.predict(AAPL_Test_X)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "zKt-0GuXEFbL",
        "outputId": "53c40a47-f4cb-4c7a-e64f-7fd7e9f1c2f7"
      },
      "source": [
        "plt.figure(figsize=(20, 5))\r\n",
        "plt.plot(AAPL['Date'][AAPL['Date'] < '2020-10-12'], AAPL['Adj. Close'][AAPL['Date'] < '2020-10-12'], label = 'Training')\r\n",
        "plt.plot(AAPL['Date'][AAPL['Date'] >= '2020-10-09'], AAPL['Adj. Close'][AAPL['Date'] >= '2020-10-09'], label = 'Testing')\r\n",
        "plt.plot(AAPL['Date'][AAPL['Date'] >= '2020-10-12'], AAPL_prediction.reshape(-1), label = 'Predictions')\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('Closing Price')\r\n",
        "plt.legend(loc = 'best')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f9684fbce90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rk9tSItpFveZ",
        "outputId": "2f2bb456-a8ca-4265-84cc-6ae3d0e88a18"
      },
      "source": [
        "rmse = math.sqrt(mean_squared_error(AAPL_Test_Y.reshape(-1, 5), AAPL_prediction))\r\n",
        "mape = np.mean(np.abs(AAPL_prediction - AAPL_Test_Y.reshape(-1, 5))/np.abs(AAPL_Test_Y.reshape(-1, 5)))\r\n",
        "print(f'RMSE: {rmse}')\r\n",
        "print(f'MAPE: {mape}')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE: 4.822911467470171\n",
            "MAPE: 0.030390689646776783\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ypf8ZYJOLDBM"
      },
      "source": [
        "#Tesla"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "QJq_CWY6TPHd",
        "outputId": "471a8905-1005-45bd-aa3b-f24cb7c86e8a"
      },
      "source": [
        "TSLA.head()"
      ],
      "execution_count": 23,
      "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>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj. Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-02</td>\n",
              "      <td>44.57</td>\n",
              "      <td>44.65</td>\n",
              "      <td>42.65</td>\n",
              "      <td>43.86</td>\n",
              "      <td>43.86</td>\n",
              "      <td>23822000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-05</td>\n",
              "      <td>42.91</td>\n",
              "      <td>43.30</td>\n",
              "      <td>41.43</td>\n",
              "      <td>42.02</td>\n",
              "      <td>42.02</td>\n",
              "      <td>26842500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-06</td>\n",
              "      <td>42.01</td>\n",
              "      <td>42.84</td>\n",
              "      <td>40.84</td>\n",
              "      <td>42.26</td>\n",
              "      <td>42.26</td>\n",
              "      <td>31309500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-07</td>\n",
              "      <td>42.67</td>\n",
              "      <td>42.96</td>\n",
              "      <td>41.96</td>\n",
              "      <td>42.19</td>\n",
              "      <td>42.19</td>\n",
              "      <td>14842000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-08</td>\n",
              "      <td>42.56</td>\n",
              "      <td>42.76</td>\n",
              "      <td>42.00</td>\n",
              "      <td>42.12</td>\n",
              "      <td>42.12</td>\n",
              "      <td>17212500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date   Open   High    Low  Close  Adj. Close      Volume\n",
              "0  2015-01-02  44.57  44.65  42.65  43.86       43.86  23822000.0\n",
              "1  2015-01-05  42.91  43.30  41.43  42.02       42.02  26842500.0\n",
              "2  2015-01-06  42.01  42.84  40.84  42.26       42.26  31309500.0\n",
              "3  2015-01-07  42.67  42.96  41.96  42.19       42.19  14842000.0\n",
              "4  2015-01-08  42.56  42.76  42.00  42.12       42.12  17212500.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pyL1B3uMTUNv",
        "outputId": "ec027b1a-8917-4d44-faaa-e3d082a7792a"
      },
      "source": [
        "TSLA.info()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1549 entries, 0 to 1548\n",
            "Data columns (total 7 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Date        1549 non-null   object \n",
            " 1   Open        1549 non-null   float64\n",
            " 2   High        1549 non-null   float64\n",
            " 3   Low         1549 non-null   float64\n",
            " 4   Close       1549 non-null   float64\n",
            " 5   Adj. Close  1549 non-null   float64\n",
            " 6   Volume      1549 non-null   float64\n",
            "dtypes: float64(6), object(1)\n",
            "memory usage: 84.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "acJ0D4D8TbuT"
      },
      "source": [
        "# Change Dtype of Date column\r\n",
        "TSLA[\"Date\"] = pd.to_datetime(TSLA[\"Date\"])"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fdCQQVG36NtH"
      },
      "source": [
        "###Split the Data into Training and Test set\r\n",
        "    Training Period: 2015-01-02 - 2020-09-30\r\n",
        "\r\n",
        "    Testing Period:  2020-10-01 - 2021-02-26"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJ4YxPbLTogR"
      },
      "source": [
        "TSLA_Date = '2020-10-01'\r\n",
        "TSLA_Train_X, TSLA_Train_Y, TSLA_Test_X, TSLA_Test_Y = Dataset(TSLA, TSLA_Date)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aYKWpHMa17Kh"
      },
      "source": [
        "### Model Fitting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SH2GuSX6T2Cm"
      },
      "source": [
        "TSLA_Model = Model()"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w-nr1vkYT_JJ"
      },
      "source": [
        "TSLA_Model.compile(optimizer = tf.keras.optimizers.Adam(), loss = 'mse', metrics = tf.keras.metrics.RootMeanSquaredError())"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GcfkNJk2ULD0"
      },
      "source": [
        "TSLA_hist = TSLA_Model.fit(TSLA_Train_X, TSLA_Train_Y, epochs = 200, validation_data = (TSLA_Test_X, TSLA_Test_Y), callbacks=[callback])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZXWgxxkkUXt-"
      },
      "source": [
        "history_dict = TSLA_hist.history\r\n",
        "\r\n",
        "loss = history_dict[\"loss\"]\r\n",
        "root_mean_squared_error = history_dict[\"root_mean_squared_error\"]\r\n",
        "val_loss = history_dict[\"val_loss\"]\r\n",
        "val_root_mean_squared_error = history_dict[\"val_root_mean_squared_error\"]\r\n",
        "\r\n",
        "epochs = range(1, len(loss) + 1)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "fkrETLmdUy88",
        "outputId": "0f24d23b-a061-495e-ddcb-2eea048752ce"
      },
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2)\r\n",
        "\r\n",
        "fig.set_figheight(5)\r\n",
        "fig.set_figwidth(15)\r\n",
        "\r\n",
        "ax1.plot(epochs, loss, label = \"Training Loss\")\r\n",
        "ax1.plot(epochs, val_loss, label = \"Validation Loss\")\r\n",
        "ax1.set(xlabel = \"Epochs\", ylabel = \"Loss\")\r\n",
        "ax1.legend()\r\n",
        "\r\n",
        "ax2.plot(epochs, root_mean_squared_error, label = \"Training Root Mean Squared Error\")\r\n",
        "ax2.plot(epochs, val_root_mean_squared_error, label = \"Validation Root Mean Squared Error\")\r\n",
        "ax2.set(xlabel = \"Epochs\", ylabel = \"Loss\")\r\n",
        "ax2.legend()\r\n",
        "\r\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kz5vH7re1Unf"
      },
      "source": [
        "### Predicting the closing stock price of Tesla"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LvaeZqRJUe1C"
      },
      "source": [
        "TSLA_prediction = TSLA_Model.predict(TSLA_Test_X)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "2wuKdS85Ut1t",
        "outputId": "abfe5cfe-2101-4df4-e580-8686fc36849d"
      },
      "source": [
        "plt.figure(figsize=(10, 5))\r\n",
        "plt.plot(TSLA['Date'][TSLA['Date'] < '2020-10-12'], TSLA['Adj. Close'][TSLA['Date'] < '2020-10-12'], label = 'Training')\r\n",
        "plt.plot(TSLA['Date'][TSLA['Date'] >= '2020-10-09'], TSLA['Adj. Close'][TSLA['Date'] >= '2020-10-09'], label = 'Testing')\r\n",
        "plt.plot(TSLA['Date'][TSLA['Date'] >= '2020-10-12'], TSLA_prediction.reshape(-1), label = 'Predictions')\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('Closing Price')\r\n",
        "plt.legend(loc = 'best')"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f968415de50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GtI6XOGNUmqG",
        "outputId": "e9b5d415-886a-42d9-c71f-8deb636a399b"
      },
      "source": [
        "rmse = math.sqrt(mean_squared_error(TSLA_Test_Y.reshape(-1, 5), TSLA_prediction))\r\n",
        "mape = np.mean(np.abs(TSLA_prediction - TSLA_Test_Y.reshape(-1, 5))/np.abs(TSLA_Test_Y.reshape(-1, 5)))\r\n",
        "print(f'RMSE: {rmse}')\r\n",
        "print(f'MAPE: {mape}')"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE: 66.40686299614501\n",
            "MAPE: 0.07223948908878701\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2JhHyPLLYnUY"
      },
      "source": [
        "#Google"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "Ss34732uYmXB",
        "outputId": "13da1f56-696c-4d35-f6c6-8e9be62d0666"
      },
      "source": [
        "GOOG.head()"
      ],
      "execution_count": 35,
      "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>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj. Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-02</td>\n",
              "      <td>527.56</td>\n",
              "      <td>529.82</td>\n",
              "      <td>522.67</td>\n",
              "      <td>523.37</td>\n",
              "      <td>523.37</td>\n",
              "      <td>1447563.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-05</td>\n",
              "      <td>521.83</td>\n",
              "      <td>522.89</td>\n",
              "      <td>511.66</td>\n",
              "      <td>512.46</td>\n",
              "      <td>512.46</td>\n",
              "      <td>2059840.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-06</td>\n",
              "      <td>513.59</td>\n",
              "      <td>514.76</td>\n",
              "      <td>499.68</td>\n",
              "      <td>500.59</td>\n",
              "      <td>500.59</td>\n",
              "      <td>2899940.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-07</td>\n",
              "      <td>505.61</td>\n",
              "      <td>505.86</td>\n",
              "      <td>498.28</td>\n",
              "      <td>499.73</td>\n",
              "      <td>499.73</td>\n",
              "      <td>2065054.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-08</td>\n",
              "      <td>496.63</td>\n",
              "      <td>502.10</td>\n",
              "      <td>489.66</td>\n",
              "      <td>501.30</td>\n",
              "      <td>501.30</td>\n",
              "      <td>3353582.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date    Open    High     Low   Close  Adj. Close     Volume\n",
              "0  2015-01-02  527.56  529.82  522.67  523.37      523.37  1447563.0\n",
              "1  2015-01-05  521.83  522.89  511.66  512.46      512.46  2059840.0\n",
              "2  2015-01-06  513.59  514.76  499.68  500.59      500.59  2899940.0\n",
              "3  2015-01-07  505.61  505.86  498.28  499.73      499.73  2065054.0\n",
              "4  2015-01-08  496.63  502.10  489.66  501.30      501.30  3353582.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n5JxMBTjZ_2Q",
        "outputId": "7f4f54f7-1aca-42d8-e58b-fe99dd399a64"
      },
      "source": [
        "GOOG.info()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1549 entries, 0 to 1548\n",
            "Data columns (total 7 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Date        1549 non-null   object \n",
            " 1   Open        1549 non-null   float64\n",
            " 2   High        1549 non-null   float64\n",
            " 3   Low         1549 non-null   float64\n",
            " 4   Close       1549 non-null   float64\n",
            " 5   Adj. Close  1549 non-null   float64\n",
            " 6   Volume      1549 non-null   float64\n",
            "dtypes: float64(6), object(1)\n",
            "memory usage: 84.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SB2BNkTKaGnx"
      },
      "source": [
        "# Change Dtype of Date column\r\n",
        "GOOG[\"Date\"] = pd.to_datetime(GOOG[\"Date\"])"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZPLEQ3pv6h2S"
      },
      "source": [
        "###Split the Data into Training and Test set\r\n",
        "    Training Period: 2015-01-02 - 2020-10-30\r\n",
        "\r\n",
        "    Testing Period:  2020-11-02 - 2021-02-26"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CATiVyQ4aZ93"
      },
      "source": [
        "GOOG_Date = '2020-11-01'\r\n",
        "GOOG_Train_X, GOOG_Train_Y, GOOG_Test_X, GOOG_Test_Y = Dataset(GOOG, GOOG_Date)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "toxFZOh914XG"
      },
      "source": [
        "### Model Fitting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RV2GNSl6aaBf"
      },
      "source": [
        "GOOG_Model = Model()"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PPjOZW8AaaFD"
      },
      "source": [
        "GOOG_Model.compile(optimizer = tf.keras.optimizers.Adam(), loss = 'mse', metrics = tf.keras.metrics.RootMeanSquaredError())"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fh9miHDgaaIk"
      },
      "source": [
        "GOOG_hist = GOOG_Model.fit(GOOG_Train_X, GOOG_Train_Y, epochs = 1000, validation_data = (GOOG_Test_X, GOOG_Test_Y), callbacks = [callback])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vp7tR-PBaaL8"
      },
      "source": [
        "history_dict = GOOG_hist.history\r\n",
        "\r\n",
        "loss = history_dict[\"loss\"]\r\n",
        "root_mean_squared_error = history_dict[\"root_mean_squared_error\"]\r\n",
        "val_loss = history_dict[\"val_loss\"]\r\n",
        "val_root_mean_squared_error = history_dict[\"val_root_mean_squared_error\"]\r\n",
        "\r\n",
        "epochs = range(1, len(loss) + 1)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "DcU99odHbA-f",
        "outputId": "0e3d7e96-3502-4792-c0e3-3240f063c30a"
      },
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2)\r\n",
        "\r\n",
        "fig.set_figheight(5)\r\n",
        "fig.set_figwidth(15)\r\n",
        "\r\n",
        "ax1.plot(epochs, loss, label = \"Training Loss\")\r\n",
        "ax1.plot(epochs, val_loss, label = \"Validation Loss\")\r\n",
        "ax1.set(xlabel = \"Epochs\", ylabel = \"Loss\")\r\n",
        "ax1.legend()\r\n",
        "\r\n",
        "ax2.plot(epochs, root_mean_squared_error, label = \"Training Root Mean Squared Error\")\r\n",
        "ax2.plot(epochs, val_root_mean_squared_error, label = \"Validation Root Mean Squared Error\")\r\n",
        "ax2.set(xlabel = \"Epochs\", ylabel = \"Loss\")\r\n",
        "ax2.legend()\r\n",
        "\r\n",
        "plt.show()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YKZETIgK1MgM"
      },
      "source": [
        "### Predicting the closing stock price of Google"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W5on4_hjbFb1"
      },
      "source": [
        "GOOG_prediction = GOOG_Model.predict(GOOG_Test_X)"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "jqwt0ivCbBdD",
        "outputId": "78429ed1-e1fd-4837-c05e-1959aeecc5d6"
      },
      "source": [
        "plt.figure(figsize=(10, 5))\r\n",
        "plt.plot(GOOG['Date'][GOOG['Date'] < '2020-11-07'], GOOG['Adj. Close'][GOOG['Date'] < '2020-11-07'], label = 'Training')\r\n",
        "plt.plot(GOOG['Date'][GOOG['Date'] >= '2020-11-07'], GOOG['Adj. Close'][GOOG['Date'] >= '2020-11-07'], label = 'Testing')\r\n",
        "plt.plot(GOOG['Date'][GOOG['Date'] >= '2020-11-07'], GOOG_prediction.reshape(-1), label = 'Predictions')\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('Closing Price')\r\n",
        "plt.legend(loc = 'best')"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f96850a5e50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xdol8v86bkj5",
        "outputId": "565a80ca-10b9-42e2-9f46-31b09ed48d85"
      },
      "source": [
        "rmse = math.sqrt(mean_squared_error(GOOG_Test_Y.reshape(-1, 5), GOOG_prediction))\r\n",
        "mape = np.mean(np.abs(GOOG_prediction - GOOG_Test_Y.reshape(-1, 5))/np.abs(GOOG_Test_Y.reshape(-1, 5)))\r\n",
        "print(f'RMSE: {rmse}')\r\n",
        "print(f'MAPE: {mape}')"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE: 115.66296805524927\n",
            "MAPE: 0.039782693356206676\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7QKWND5DTruk"
      },
      "source": [
        "#Microsoft"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "6qpItzGXT1gu",
        "outputId": "c31f0878-fe42-49f5-fee8-522a66c2efee"
      },
      "source": [
        "MSFT.head()"
      ],
      "execution_count": 47,
      "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>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj. Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-02</td>\n",
              "      <td>46.66</td>\n",
              "      <td>47.42</td>\n",
              "      <td>46.54</td>\n",
              "      <td>46.76</td>\n",
              "      <td>41.44</td>\n",
              "      <td>27913900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-05</td>\n",
              "      <td>46.37</td>\n",
              "      <td>46.73</td>\n",
              "      <td>46.25</td>\n",
              "      <td>46.33</td>\n",
              "      <td>41.06</td>\n",
              "      <td>39673900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-06</td>\n",
              "      <td>46.38</td>\n",
              "      <td>46.75</td>\n",
              "      <td>45.54</td>\n",
              "      <td>45.65</td>\n",
              "      <td>40.46</td>\n",
              "      <td>36447900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-07</td>\n",
              "      <td>45.98</td>\n",
              "      <td>46.46</td>\n",
              "      <td>45.49</td>\n",
              "      <td>46.23</td>\n",
              "      <td>40.97</td>\n",
              "      <td>29114100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-08</td>\n",
              "      <td>46.75</td>\n",
              "      <td>47.75</td>\n",
              "      <td>46.72</td>\n",
              "      <td>47.59</td>\n",
              "      <td>42.18</td>\n",
              "      <td>29645200.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date   Open   High    Low  Close  Adj. Close      Volume\n",
              "0  2015-01-02  46.66  47.42  46.54  46.76       41.44  27913900.0\n",
              "1  2015-01-05  46.37  46.73  46.25  46.33       41.06  39673900.0\n",
              "2  2015-01-06  46.38  46.75  45.54  45.65       40.46  36447900.0\n",
              "3  2015-01-07  45.98  46.46  45.49  46.23       40.97  29114100.0\n",
              "4  2015-01-08  46.75  47.75  46.72  47.59       42.18  29645200.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E3MHxY8bT7a5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3dba2670-166d-4a51-9383-09f8f115adde"
      },
      "source": [
        "MSFT.info()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1549 entries, 0 to 1548\n",
            "Data columns (total 7 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Date        1549 non-null   object \n",
            " 1   Open        1549 non-null   float64\n",
            " 2   High        1549 non-null   float64\n",
            " 3   Low         1549 non-null   float64\n",
            " 4   Close       1549 non-null   float64\n",
            " 5   Adj. Close  1549 non-null   float64\n",
            " 6   Volume      1549 non-null   float64\n",
            "dtypes: float64(6), object(1)\n",
            "memory usage: 84.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hYeHjmqGT8sh"
      },
      "source": [
        "# Change Dtype of Date column\r\n",
        "MSFT[\"Date\"] = pd.to_datetime(MSFT[\"Date\"])"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KCrPxO3S6uPh"
      },
      "source": [
        "###Split the Data into Training and Test set\r\n",
        "    Training Period: 2015-01-02 - 2020-09-30\r\n",
        "\r\n",
        "    Testing Period:  2020-10-01 - 2021-02-26"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A10WQqexUJK2"
      },
      "source": [
        "MSFT_Date = '2020-10-01'\r\n",
        "MSFT_Train_X, MSFT_Train_Y, MSFT_Test_X, MSFT_Test_Y = Dataset(MSFT, MSFT_Date)"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vk8LE9OW1zco"
      },
      "source": [
        "### Model Fitting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2b8w-sYTUTpq"
      },
      "source": [
        "MSFT_Model = Model()"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aav5PTMSUYzx"
      },
      "source": [
        "MSFT_Model.compile(optimizer = tf.keras.optimizers.Adam(), loss = 'mse', metrics = tf.keras.metrics.RootMeanSquaredError())"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uMbnWIM9UhDx"
      },
      "source": [
        "MSFT_hist = MSFT_Model.fit(MSFT_Train_X, MSFT_Train_Y, epochs = 1000, validation_data = (MSFT_Test_X, MSFT_Test_Y), callbacks=[callback])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9ztdmCqUrec"
      },
      "source": [
        "history_dict = MSFT_hist.history\r\n",
        "\r\n",
        "loss = history_dict[\"loss\"]\r\n",
        "root_mean_squared_error = history_dict[\"root_mean_squared_error\"]\r\n",
        "val_loss = history_dict[\"val_loss\"]\r\n",
        "val_root_mean_squared_error = history_dict[\"val_root_mean_squared_error\"]\r\n",
        "\r\n",
        "epochs = range(1, len(loss) + 1)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zp60IGaqUw7-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "outputId": "d8db879c-2cc7-4c8d-fc69-11cc89b8098d"
      },
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2)\r\n",
        "\r\n",
        "fig.set_figheight(5)\r\n",
        "fig.set_figwidth(15)\r\n",
        "\r\n",
        "ax1.plot(epochs, loss, label = \"Training Loss\")\r\n",
        "ax1.plot(epochs, val_loss, label = \"Validation Loss\")\r\n",
        "ax1.set(xlabel = \"Epochs\", ylabel = \"Loss\")\r\n",
        "ax1.legend()\r\n",
        "\r\n",
        "ax2.plot(epochs, root_mean_squared_error, label = \"Training Root Mean Squared Error\")\r\n",
        "ax2.plot(epochs, val_root_mean_squared_error, label = \"Validation Root Mean Squared Error\")\r\n",
        "ax2.set(xlabel = \"Epochs\", ylabel = \"Loss\")\r\n",
        "ax2.legend()\r\n",
        "\r\n",
        "plt.show()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IzoKGAK71e2e"
      },
      "source": [
        "### Predicting the closing stock price of Microsoft"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aIR_MW2PU0OX"
      },
      "source": [
        "MSFT_prediction = MSFT_Model.predict(MSFT_Test_X)"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5tqUN3xU6m6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "f2c2ff1c-da54-4acd-fe0d-ced11a0f2193"
      },
      "source": [
        "plt.figure(figsize=(10, 5))\r\n",
        "plt.plot(MSFT['Date'][MSFT['Date'] < '2020-10-12'], MSFT['Adj. Close'][MSFT['Date'] < '2020-10-12'], label = 'Training')\r\n",
        "plt.plot(MSFT['Date'][MSFT['Date'] >= '2020-10-09'], MSFT['Adj. Close'][MSFT['Date'] >= '2020-10-09'], label = 'Testing')\r\n",
        "plt.plot(MSFT['Date'][MSFT['Date'] >= '2020-10-12'], MSFT_prediction.reshape(-1), label = 'Predictions')\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('Closing Price')\r\n",
        "plt.legend(loc = 'best')"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f967efd55d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q7U_uYv1U_Xf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "046bcf3a-0f5c-459f-802d-c8379294e996"
      },
      "source": [
        "rmse = math.sqrt(mean_squared_error(MSFT_Test_Y.reshape(-1, 5), MSFT_prediction))\r\n",
        "mape = np.mean(np.abs(MSFT_prediction - MSFT_Test_Y.reshape(-1, 5))/np.abs(MSFT_Test_Y.reshape(-1, 5)))\r\n",
        "print(f'RMSE: {rmse}')\r\n",
        "print(f'MAPE: {mape}')"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE: 6.2306768444560205\n",
            "MAPE: 0.02214984775145576\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SkK2GpHFVUM4"
      },
      "source": [
        "#Amazon"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fUPDJfvkVXBS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "6a704552-cba6-4ecb-a9d6-9cdfd74a231c"
      },
      "source": [
        "AMZN.head()"
      ],
      "execution_count": 59,
      "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>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj. Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-02</td>\n",
              "      <td>312.58</td>\n",
              "      <td>314.75</td>\n",
              "      <td>306.96</td>\n",
              "      <td>308.52</td>\n",
              "      <td>308.52</td>\n",
              "      <td>2783200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-05</td>\n",
              "      <td>307.01</td>\n",
              "      <td>308.38</td>\n",
              "      <td>300.85</td>\n",
              "      <td>302.19</td>\n",
              "      <td>302.19</td>\n",
              "      <td>2774200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-06</td>\n",
              "      <td>302.24</td>\n",
              "      <td>303.00</td>\n",
              "      <td>292.38</td>\n",
              "      <td>295.29</td>\n",
              "      <td>295.29</td>\n",
              "      <td>3519000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-07</td>\n",
              "      <td>297.50</td>\n",
              "      <td>301.28</td>\n",
              "      <td>295.33</td>\n",
              "      <td>298.42</td>\n",
              "      <td>298.42</td>\n",
              "      <td>2640300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-08</td>\n",
              "      <td>300.32</td>\n",
              "      <td>303.14</td>\n",
              "      <td>296.11</td>\n",
              "      <td>300.46</td>\n",
              "      <td>300.46</td>\n",
              "      <td>3088400.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date    Open    High     Low   Close  Adj. Close     Volume\n",
              "0  2015-01-02  312.58  314.75  306.96  308.52      308.52  2783200.0\n",
              "1  2015-01-05  307.01  308.38  300.85  302.19      302.19  2774200.0\n",
              "2  2015-01-06  302.24  303.00  292.38  295.29      295.29  3519000.0\n",
              "3  2015-01-07  297.50  301.28  295.33  298.42      298.42  2640300.0\n",
              "4  2015-01-08  300.32  303.14  296.11  300.46      300.46  3088400.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-xF-HPHwVaK2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f0a9605-4815-4ed0-c538-154924b9bccb"
      },
      "source": [
        "AMZN.info()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1549 entries, 0 to 1548\n",
            "Data columns (total 7 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Date        1549 non-null   object \n",
            " 1   Open        1549 non-null   float64\n",
            " 2   High        1549 non-null   float64\n",
            " 3   Low         1549 non-null   float64\n",
            " 4   Close       1549 non-null   float64\n",
            " 5   Adj. Close  1549 non-null   float64\n",
            " 6   Volume      1549 non-null   float64\n",
            "dtypes: float64(6), object(1)\n",
            "memory usage: 84.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DnZTLxJ3Vczg"
      },
      "source": [
        "# Change Dtype of Date column\r\n",
        "AMZN[\"Date\"] = pd.to_datetime(AMZN[\"Date\"])"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8OeQGXjM6405"
      },
      "source": [
        "###Split the Data into Training and Test set\r\n",
        "    Training Period: 2015-01-02 - 2020-10-30\r\n",
        "\r\n",
        "    Testing Period:  2020-11-02 - 2021-02-26"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vzD9HTYoViBa"
      },
      "source": [
        "AMZN_Date = '2020-11-01'\r\n",
        "AMZN_Train_X, AMZN_Train_Y, AMZN_Test_X, AMZN_Test_Y = Dataset(AMZN, AMZN_Date)"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ir-w5AGj1oiK"
      },
      "source": [
        "### Model Fitting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2LOoZH8NVpUj"
      },
      "source": [
        "AMZN_Model = Model()"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qln_peIdVs7s"
      },
      "source": [
        "AMZN_Model.compile(optimizer = tf.keras.optimizers.Adam(), loss = 'mse', metrics = tf.keras.metrics.RootMeanSquaredError())"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lo_MPS7aVwLg"
      },
      "source": [
        "AMZN_hist = AMZN_Model.fit(AMZN_Train_X, AMZN_Train_Y, epochs = 200, validation_data = (AMZN_Test_X, AMZN_Test_Y), callbacks=[callback])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O3U9RoCMV5us"
      },
      "source": [
        "history_dict = AMZN_hist.history\r\n",
        "\r\n",
        "loss = history_dict[\"loss\"]\r\n",
        "root_mean_squared_error = history_dict[\"root_mean_squared_error\"]\r\n",
        "val_loss = history_dict[\"val_loss\"]\r\n",
        "val_root_mean_squared_error = history_dict[\"val_root_mean_squared_error\"]\r\n",
        "\r\n",
        "epochs = range(1, len(loss) + 1)"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nOT_ddNYV9sX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "outputId": "088ec2d4-c15f-4776-c2d9-2dd12f6b8bb2"
      },
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2)\r\n",
        "\r\n",
        "fig.set_figheight(5)\r\n",
        "fig.set_figwidth(15)\r\n",
        "\r\n",
        "ax1.plot(epochs, loss, label = \"Training Loss\")\r\n",
        "ax1.plot(epochs, val_loss, label = \"Validation Loss\")\r\n",
        "ax1.set(xlabel = \"Epochs\", ylabel = \"Loss\")\r\n",
        "ax1.legend()\r\n",
        "\r\n",
        "ax2.plot(epochs, root_mean_squared_error, label = \"Training Root Mean Squared Error\")\r\n",
        "ax2.plot(epochs, val_root_mean_squared_error, label = \"Validation Root Mean Squared Error\")\r\n",
        "ax2.set(xlabel = \"Epochs\", ylabel = \"Loss\")\r\n",
        "ax2.legend()\r\n",
        "\r\n",
        "plt.show()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xnAosvbS1jIK"
      },
      "source": [
        "### Predicting the closing stock price of Amazon"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ulvjzyNnWAKY"
      },
      "source": [
        "AMZN_prediction = AMZN_Model.predict(AMZN_Test_X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yqz-_qZeWFPL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "4f60be4f-def8-4bc9-8566-164a90786c79"
      },
      "source": [
        "plt.figure(figsize=(10, 5))\r\n",
        "plt.plot(AMZN['Date'][AMZN['Date'] < '2020-11-07'], AMZN['Adj. Close'][AMZN['Date'] < '2020-11-07'], label = 'Training')\r\n",
        "plt.plot(AMZN['Date'][AMZN['Date'] >= '2020-11-07'], AMZN['Adj. Close'][AMZN['Date'] >= '2020-11-07'], label = 'Testing')\r\n",
        "plt.plot(AMZN['Date'][AMZN['Date'] >= '2020-11-07'], AMZN_prediction.reshape(-1), label = 'Predictions')\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('Closing Price')\r\n",
        "plt.legend(loc = 'best')"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f967ec8c6d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pdn-MQg6WJQu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d5af807-18ae-480d-90b2-62025aeb200b"
      },
      "source": [
        "rmse = math.sqrt(mean_squared_error(AMZN_Test_Y.reshape(-1, 5), AMZN_prediction))\r\n",
        "mape = np.mean(np.abs(AMZN_prediction - AMZN_Test_Y.reshape(-1, 5))/np.abs(AMZN_Test_Y.reshape(-1, 5)))\r\n",
        "print(f'RMSE: {rmse}')\r\n",
        "print(f'MAPE: {mape}')"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE: 87.48506342352238\n",
            "MAPE: 0.021571786340561845\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}