IBM_LSTM.ipynb

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    "colab": {
      "name": "IBM_LSTM.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "47NRnzIrW2Mo",
        "outputId": "4eafbc0a-b5a6-4af3-bc54-93c04cfef436"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "sys.path.append('/content/drive/MyDrive/data-sets/IBM.csv')"
      ],
      "metadata": {
        "id": "URsYZcc-W7pO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VIg-BjwpUSYs"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv(\"/content/drive/MyDrive/data-sets/IBM.csv\", index_col=\"Date\", parse_dates=[\"Date\"], usecols=[0, 2])"
      ],
      "metadata": {
        "id": "uq6ye5YVUdM-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "KAvLR6rBXYOK",
        "outputId": "63280092-1f4a-4fc0-9c77-320f394b1d69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             High\n",
              "Date             \n",
              "2006-01-03  41.22\n",
              "2006-01-04  41.90\n",
              "2006-01-05  41.73\n",
              "2006-01-06  43.57\n",
              "2006-01-09  43.66\n",
              "...           ...\n",
              "2017-12-22  71.87\n",
              "2017-12-26  71.39\n",
              "2017-12-27  70.49\n",
              "2017-12-28  70.32\n",
              "2017-12-29  70.13\n",
              "\n",
              "[3019 rows x 1 columns]"
            ],
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              "      <th>High</th>\n",
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              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2006-01-03</th>\n",
              "      <td>41.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-01-04</th>\n",
              "      <td>41.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-01-05</th>\n",
              "      <td>41.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-01-06</th>\n",
              "      <td>43.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-01-09</th>\n",
              "      <td>43.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-22</th>\n",
              "      <td>71.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-26</th>\n",
              "      <td>71.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-27</th>\n",
              "      <td>70.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-28</th>\n",
              "      <td>70.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-29</th>\n",
              "      <td>70.13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3019 rows × 1 columns</p>\n",
              "</div>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_set = dataset[:'2016'].values\n",
        "test_set = dataset[\"2017\":].values"
      ],
      "metadata": {
        "id": "ZA6GfnXRXYWY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"High\"][:\"2016\"].plot(figsize=(16, 4), legend=True)\n",
        "dataset[\"High\"][\"2017\":].plot(figsize=(16, 4), legend=True)\n",
        "plt.legend([\"Training set before 2017\", \"Test set beyond 2017\"])\n",
        "plt.title(\"IBM stock price\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "h23Omr0xXYgh",
        "outputId": "2dffc8ae-21a4-423f-b26a-942b5de4091c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import libraries\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "metadata": {
        "id": "iWaqhe1SXYkZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scaling the training set\n",
        "sc = MinMaxScaler(feature_range=(0, 1))\n",
        "training_set_scaled = sc.fit_transform(training_set)"
      ],
      "metadata": {
        "id": "z7U2Dv_ZZkw4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_set_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rf8N5nsUaBOc",
        "outputId": "97559788-4fb4-4aea-f693-47ea8c7c36fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.73574409],\n",
              "       [0.75150672],\n",
              "       [0.74756606],\n",
              "       ...,\n",
              "       [0.6893834 ],\n",
              "       [0.6826611 ],\n",
              "       [0.68428373]])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = []\n",
        "y_train = []\n",
        "\n",
        "for i in range(10, len(training_set_scaled)):\n",
        "    x_train.append(training_set_scaled[i-10:i, 0])\n",
        "    y_train.append(training_set_scaled[i, 0])\n",
        "\n",
        "x_train, y_train = np.array(x_train), np.array(y_train)"
      ],
      "metadata": {
        "id": "kh0aZ2Y-aQtW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4PO3WrS4cTFO",
        "outputId": "949868fc-87a1-40fe-fb94-ba4fd79a24b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2758, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n",
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YAizJ2ePcV8n",
        "outputId": "70d1513b-d2f3-4504-fc59-07348223e2ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2758, 10, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import libraries \n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM, Dropout"
      ],
      "metadata": {
        "id": "d4gDURZWjUOj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "regressor = Sequential()\n",
        "regressor.add(LSTM(units=10))\n",
        "regressor.add(Dropout(0.2))\n",
        "regressor.add(Dense(1)) # output layer"
      ],
      "metadata": {
        "id": "nrLo9Ensj3Rh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "regressor.compile(optimizer=\"adam\", loss=\"mean_squared_error\")"
      ],
      "metadata": {
        "id": "7xItlW82kZDs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "regressor.fit(x_train, y_train, epochs=100, batch_size=32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MMl8YPsSkkl3",
        "outputId": "3a668e1a-bb69-490e-f062-a3fd718325b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "87/87 [==============================] - 3s 6ms/step - loss: 0.0125\n",
            "Epoch 2/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0063\n",
            "Epoch 3/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0054\n",
            "Epoch 4/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0050\n",
            "Epoch 5/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0041\n",
            "Epoch 6/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0040\n",
            "Epoch 7/100\n",
            "87/87 [==============================] - 1s 8ms/step - loss: 0.0035\n",
            "Epoch 8/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0033\n",
            "Epoch 9/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0033\n",
            "Epoch 10/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0029\n",
            "Epoch 11/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0029\n",
            "Epoch 12/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0024\n",
            "Epoch 13/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0025\n",
            "Epoch 14/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0025\n",
            "Epoch 15/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0023\n",
            "Epoch 16/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0021\n",
            "Epoch 17/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0021\n",
            "Epoch 18/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0019\n",
            "Epoch 19/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0019\n",
            "Epoch 20/100\n",
            "87/87 [==============================] - 0s 6ms/step - loss: 0.0020\n",
            "Epoch 21/100\n",
            "87/87 [==============================] - 0s 6ms/step - loss: 0.0019\n",
            "Epoch 22/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0019\n",
            "Epoch 23/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0020\n",
            "Epoch 24/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0019\n",
            "Epoch 25/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0019\n",
            "Epoch 26/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0018\n",
            "Epoch 27/100\n",
            "87/87 [==============================] - 0s 6ms/step - loss: 0.0018\n",
            "Epoch 28/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0017\n",
            "Epoch 29/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0018\n",
            "Epoch 30/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0017\n",
            "Epoch 31/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0018\n",
            "Epoch 32/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0018\n",
            "Epoch 33/100\n",
            "87/87 [==============================] - 0s 6ms/step - loss: 0.0018\n",
            "Epoch 34/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0019\n",
            "Epoch 35/100\n",
            "87/87 [==============================] - 1s 8ms/step - loss: 0.0018\n",
            "Epoch 36/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0018\n",
            "Epoch 37/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0019\n",
            "Epoch 38/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0018\n",
            "Epoch 39/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0017\n",
            "Epoch 40/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0018\n",
            "Epoch 41/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0017\n",
            "Epoch 42/100\n",
            "87/87 [==============================] - 0s 5ms/step - loss: 0.0018\n",
            "Epoch 43/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0017\n",
            "Epoch 44/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0019\n",
            "Epoch 45/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0019\n",
            "Epoch 46/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0017\n",
            "Epoch 47/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0017\n",
            "Epoch 48/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0017\n",
            "Epoch 49/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0017\n",
            "Epoch 50/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0017\n",
            "Epoch 51/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0018\n",
            "Epoch 52/100\n",
            "87/87 [==============================] - 0s 6ms/step - loss: 0.0017\n",
            "Epoch 53/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0017\n",
            "Epoch 54/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0017\n",
            "Epoch 55/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0018\n",
            "Epoch 56/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0015\n",
            "Epoch 57/100\n",
            "87/87 [==============================] - 1s 8ms/step - loss: 0.0017\n",
            "Epoch 58/100\n",
            "87/87 [==============================] - 0s 6ms/step - loss: 0.0017\n",
            "Epoch 59/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0016\n",
            "Epoch 60/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0017\n",
            "Epoch 61/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0016\n",
            "Epoch 62/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0017\n",
            "Epoch 63/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0018\n",
            "Epoch 64/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0017\n",
            "Epoch 65/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0017\n",
            "Epoch 66/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0017\n",
            "Epoch 67/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0018\n",
            "Epoch 68/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0017\n",
            "Epoch 69/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0017\n",
            "Epoch 70/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0018\n",
            "Epoch 71/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0017\n",
            "Epoch 72/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0016\n",
            "Epoch 73/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0016\n",
            "Epoch 74/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0016\n",
            "Epoch 75/100\n",
            "87/87 [==============================] - 0s 5ms/step - loss: 0.0017\n",
            "Epoch 76/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0017\n",
            "Epoch 77/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0017\n",
            "Epoch 78/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0017\n",
            "Epoch 79/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0016\n",
            "Epoch 80/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0016\n",
            "Epoch 81/100\n",
            "87/87 [==============================] - 1s 13ms/step - loss: 0.0017\n",
            "Epoch 82/100\n",
            "87/87 [==============================] - 1s 9ms/step - loss: 0.0016\n",
            "Epoch 83/100\n",
            "87/87 [==============================] - 1s 9ms/step - loss: 0.0016\n",
            "Epoch 84/100\n",
            "87/87 [==============================] - 1s 10ms/step - loss: 0.0016\n",
            "Epoch 85/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0018\n",
            "Epoch 86/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0017\n",
            "Epoch 87/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0017\n",
            "Epoch 88/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0017\n",
            "Epoch 89/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0017\n",
            "Epoch 90/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0016\n",
            "Epoch 91/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0017\n",
            "Epoch 92/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0017\n",
            "Epoch 93/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0016\n",
            "Epoch 94/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0016\n",
            "Epoch 95/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0016\n",
            "Epoch 96/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0016\n",
            "Epoch 97/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0018\n",
            "Epoch 98/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0016\n",
            "Epoch 99/100\n",
            "87/87 [==============================] - 1s 6ms/step - loss: 0.0016\n",
            "Epoch 100/100\n",
            "87/87 [==============================] - 1s 7ms/step - loss: 0.0017\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fc3581fe310>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "regressor.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CmzWDkgalUQu",
        "outputId": "aaac8634-2416-40cd-8170-8547858fb5b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 10)                480       \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 10)                0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 491\n",
            "Trainable params: 491\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_set.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d-W4dB5elZWM",
        "outputId": "04d8cada-597f-49ea-8bcd-40833088df2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(251, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = dataset[len(dataset)-len(test_set)-10:].values\n",
        "inputs.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JUAn5x8woS-H",
        "outputId": "34c1aa08-a824-4387-9670-5b3201b9b7b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(261, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = sc.fit_transform(inputs)"
      ],
      "metadata": {
        "id": "J8kS6eTZov7w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = []\n",
        "for i in range(10, len(inputs)):\n",
        "  X_test.append(inputs[i-10:i, 0])\n",
        "X_test = np.array(X_test)\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1],1))"
      ],
      "metadata": {
        "id": "xhXyBuIjprhQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_stock_price = regressor.predict(X_test)"
      ],
      "metadata": {
        "id": "Q1FQpCjwqszo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_stock_price = sc.inverse_transform(predicted_stock_price)"
      ],
      "metadata": {
        "id": "9zl1iGN3q8j7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(test_set, color=\"blue\", label=\"Real IBM Stock Price\")\n",
        "plt.plot(predicted_stock_price, color=\"red\", label=\"Predicted IBM Stock Price\")\n",
        "plt.title(\"IBM Stock Price Prediction\")\n",
        "plt.xlabel(\"Time\")\n",
        "plt.ylabel(\"IBM Stock Price\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "q5oZ4R21rDj1",
        "outputId": "ad4115fe-0b12-44dc-a669-dbe54d06a30a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
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          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}