Apple stock forecasting.ipynb

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        "# Time-Series Forecasting: Predicting Apple Stock Price Using An LSTM Model\n",
        "Traditionally, most machine learning (ML) models have used some observations (samples / examples) as input features, but the data has no time dimension.\n",
        "\n",
        "Models capable of predicting future values based on previously observed values are known as time-series forecasting models. For non-stationary data, time-series forecasting is widely used. Non-stationary data are those whose statistical features, such as mean and standard deviation, do not remain constant throughout time but instead change.\n",
        "\n",
        "The non-stationary input data used as input to these models is typically called to as time-series. Temperature values over time, stock prices over time, and the price of a property over time are all instances of time-series. As a result, the input is a signal (time-series) characterised by observations recorded in a specific order throughout time."
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      "source": [
        "## The LSTM model\n",
        "- Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning\n",
        "\n",
        "- LSTM Models, in a nutshell, can store data throughout time. When working with Time Series or Sequential Data, this is really handy.\n",
        "\n",
        "- The Human Brain is remarkably similar to LSTM Models. When you hear someone speak to you:"
      ]
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      "source": [
        "!pip install --upgrade pandas\n",
        "!pip install --upgrade pandas_datareader"
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      "source": [
        "import pandas_datareader as web # to read data from web\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt"
      ]
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              "                 High        Low       Open      Close       Volume  Adj Close\n",
              "Date                                                                          \n",
              "2015-01-02  27.860001  26.837500  27.847500  27.332500  212818400.0  24.603205\n",
              "2015-01-05  27.162500  26.352501  27.072500  26.562500  257142000.0  23.910095\n",
              "2015-01-06  26.857500  26.157499  26.635000  26.565001  263188400.0  23.912340\n",
              "2015-01-07  27.049999  26.674999  26.799999  26.937500  160423600.0  24.247650\n",
              "2015-01-08  28.037500  27.174999  27.307501  27.972500  237458000.0  25.179298"
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              "      <th>Close</th>\n",
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              "      <th>2015-01-02</th>\n",
              "      <td>27.860001</td>\n",
              "      <td>26.837500</td>\n",
              "      <td>27.847500</td>\n",
              "      <td>27.332500</td>\n",
              "      <td>212818400.0</td>\n",
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              "      <th>2015-01-05</th>\n",
              "      <td>27.162500</td>\n",
              "      <td>26.352501</td>\n",
              "      <td>27.072500</td>\n",
              "      <td>26.562500</td>\n",
              "      <td>257142000.0</td>\n",
              "      <td>23.910095</td>\n",
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              "    <tr>\n",
              "      <th>2015-01-06</th>\n",
              "      <td>26.857500</td>\n",
              "      <td>26.157499</td>\n",
              "      <td>26.635000</td>\n",
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              "      <td>263188400.0</td>\n",
              "      <td>23.912340</td>\n",
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              "      <th>2015-01-07</th>\n",
              "      <td>27.049999</td>\n",
              "      <td>26.674999</td>\n",
              "      <td>26.799999</td>\n",
              "      <td>26.937500</td>\n",
              "      <td>160423600.0</td>\n",
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              "      <th>2015-01-08</th>\n",
              "      <td>28.037500</td>\n",
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              "      <td>27.307501</td>\n",
              "      <td>27.972500</td>\n",
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      ],
      "source": [
        "# import the data\n",
        "APPL_data= web.DataReader('AAPL',data_source=\"yahoo\",start='2015-01-01',end='2021-09-30')\n",
        "APPL_data.head()"
      ]
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            "text/plain": [
              "(1699, 6)"
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        "APPL_data.shape"
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            "text/plain": [
              "Text(0.5, 1.0, 'Closing price History')"
            ]
          },
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          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#lets look at the stock closing price of history\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(16,8))\n",
        "sns.lineplot(x= APPL_data.index,y=APPL_data['Close'])\n",
        "plt.xlabel('Date', fontsize=20)\n",
        "plt.ylabel('closing price USD ($)',fontsize=20)\n",
        "plt.title('Closing price History',fontsize=25)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "59d788b8",
      "metadata": {
        "id": "59d788b8"
      },
      "source": [
        "#### I am using the AAPL dataset, which is already divided into training set and test set, but you can do the divison with a simple command!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "dea9b0b5",
      "metadata": {
        "id": "dea9b0b5"
      },
      "outputs": [],
      "source": [
        "# Split into train and test:\n",
        "data_to_train = APPL_data[:1530]\n",
        "data_to_test = APPL_data[1530:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "f1f31f26",
      "metadata": {
        "id": "f1f31f26"
      },
      "outputs": [],
      "source": [
        "#Now, we can save the 2 csv files, Train and Test.\n",
        "data_to_train.to_csv('train_data.csv')\n",
        "data_to_test.to_csv('test_data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "bf3e3fc0",
      "metadata": {
        "id": "bf3e3fc0",
        "outputId": "6f1f2f49-1364-4d3c-a811-274013fbb6ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Close\n",
              "Date                 \n",
              "2015-01-02  27.332500\n",
              "2015-01-05  26.562500\n",
              "2015-01-06  26.565001\n",
              "2015-01-07  26.937500\n",
              "2015-01-08  27.972500"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2b2bf487-181a-45b4-8c9e-9cf0dedbf97b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-01-02</th>\n",
              "      <td>27.332500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-05</th>\n",
              "      <td>26.562500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-06</th>\n",
              "      <td>26.565001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-07</th>\n",
              "      <td>26.937500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-08</th>\n",
              "      <td>27.972500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      \n",
              "  <style>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "aapl_data= APPL_data.iloc[: , 3:4]\n",
        "aapl_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "3a586dd1",
      "metadata": {
        "id": "3a586dd1"
      },
      "outputs": [],
      "source": [
        "## We want to create a numpy arrary not a vector \n",
        "trainig_set= aapl_data.iloc[:1530,:].values\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "c10d660f",
      "metadata": {
        "id": "c10d660f"
      },
      "outputs": [],
      "source": [
        "test_set= aapl_data.iloc[1530:,:].values\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e057c645",
      "metadata": {
        "id": "e057c645"
      },
      "source": [
        "#### It’s a good idea to normalize the data before model fitting. This will boost the performance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "d720b808",
      "metadata": {
        "id": "d720b808"
      },
      "outputs": [],
      "source": [
        "# Feature scalling, Here we will do normalizatioin \n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "sc= MinMaxScaler(feature_range=(0,1))\n",
        "trainig_set_scaled= sc.fit_transform(trainig_set)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bc3c0bd4",
      "metadata": {
        "id": "bc3c0bd4"
      },
      "source": [
        "#### After scaling the training data, we must format it into a three-dimensional array for use in our LSTM model. We accomplish this by generating 60 timestamps."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "0a43bf07",
      "metadata": {
        "id": "0a43bf07"
      },
      "outputs": [],
      "source": [
        "# Create a data structure with 60 timesteps and 1 output\n",
        "X_train=[] #Independent variables\n",
        "y_train= [] # Dependent variables \n",
        "# I am going to append past 60 days data \n",
        "for i in range(60,1530):\n",
        "    X_train.append(trainig_set_scaled[i-60:i,0]) # Appending prevois 60 days data not including 60\n",
        "    y_train.append(trainig_set_scaled[i,0])\n",
        "    \n",
        "X_train, y_train= np.array(X_train), np.array(y_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "09a84e7c",
      "metadata": {
        "id": "09a84e7c",
        "outputId": "51f6c139-d46f-4e45-f465-dde779afab05",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1470, 60), (1470,))"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# lETS CHECK THE SHAPE OF X_train and y_train\n",
        "X_train.shape, y_train.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f44320ea",
      "metadata": {
        "id": "f44320ea"
      },
      "source": [
        "#### We have now reshaped the data into the following format (values, time-steps, 1 dimensional output)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "a9c0e3e6",
      "metadata": {
        "id": "a9c0e3e6",
        "outputId": "2f230961-93a8-438c-e169-b516652af892",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1470, 60, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# LSMT Model needs to be 3- dimensional, so need to rehsape the x_train, y_train\n",
        "# Reshaping\n",
        "#numpy.reshape(array, shape, order = 'C')\n",
        "X_train= np.reshape(X_train,(X_train.shape[0],X_train.shape[1],1))\n",
        "X_train.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f78a770e",
      "metadata": {
        "id": "f78a770e"
      },
      "source": [
        "#### Now, it’s time to build the model. We will build the LSTM with 100 neurons and 5 hidden layers. Finally, we will assign 1 neuron in the output layer for predicting the normalized stock price. We will use the MSE loss function and the Adam stochastic gradient descent optimizer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "2db39c1b",
      "metadata": {
        "id": "2db39c1b"
      },
      "outputs": [],
      "source": [
        "# Importing the Keras libraries and packages\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, LSTM\n",
        "\n",
        "#  Initialising the RNN\n",
        "model= Sequential()\n",
        "\n",
        "# Adding first LSTM layer and some dropout Dropout regularisation\n",
        "model.add(LSTM(units=100,return_sequences=True, input_shape=(X_train.shape[1],1)))\n",
        "model.add(Dropout(rate=0.2))\n",
        "\n",
        "# Adding second LSTM layer and some dropout Dropout regularisation\n",
        "model.add(LSTM(units=100,return_sequences=True))\n",
        "model.add(Dropout(rate=0.2))\n",
        "\n",
        "# Adding third LSTM layer and some dropout Dropout regularisation\n",
        "model.add(LSTM(units=100,return_sequences=True))\n",
        "model.add(Dropout(rate=0.2))\n",
        "\n",
        "# Adding fourth LSTM layer and some dropout Dropout regularisation\n",
        "model.add(LSTM(units=100,return_sequences=True))\n",
        "model.add(Dropout(rate=0.2))\n",
        "\n",
        "# Adding fifth LSTM layer and some dropout Dropout regularisation\n",
        "model.add(LSTM(units=100))\n",
        "model.add(Dropout(rate=0.2))\n",
        "\n",
        "# Adding the Output Layer\n",
        "model.add(Dense(units=1))\n",
        "\n",
        "# Compiling the Model\n",
        "# Because we're doing regression hence mean_squared_error\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "d7dafcf6",
      "metadata": {
        "id": "d7dafcf6",
        "outputId": "17635385-8b15-49ed-a96c-dba80f3df973",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 60, 100)           40800     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 60, 100)           0         \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 60, 100)           80400     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 60, 100)           0         \n",
            "                                                                 \n",
            " lstm_2 (LSTM)               (None, 60, 100)           80400     \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 60, 100)           0         \n",
            "                                                                 \n",
            " lstm_3 (LSTM)               (None, 60, 100)           80400     \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 60, 100)           0         \n",
            "                                                                 \n",
            " lstm_4 (LSTM)               (None, 100)               80400     \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 100)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 101       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 362,501\n",
            "Trainable params: 362,501\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c112f265",
      "metadata": {
        "id": "c112f265"
      },
      "source": [
        "**We use Dropout layers to avoid Overfitting problems, and besides that, we use the parameter “return_sequences” to determine if the layer will return a sequence compatible with a LSTM. We use “return_sequences=True” when we have a LSTM layer after**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "fa362c9e",
      "metadata": {
        "id": "fa362c9e",
        "outputId": "61953347-3e8e-4b9c-8125-6082f375f686",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 8.1280e-04\n",
            "Epoch 2/5\n",
            "46/46 [==============================] - 1s 20ms/step - loss: 8.3766e-04\n",
            "Epoch 3/5\n",
            "46/46 [==============================] - 1s 20ms/step - loss: 8.3867e-04\n",
            "Epoch 4/5\n",
            "46/46 [==============================] - 1s 20ms/step - loss: 9.9270e-04\n",
            "Epoch 5/5\n",
            "46/46 [==============================] - 1s 20ms/step - loss: 8.2833e-04\n"
          ]
        }
      ],
      "source": [
        "# Fitting the model to the Training set\n",
        "history=model.fit(X_train,y_train,epochs=5,batch_size=32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "2366716c",
      "metadata": {
        "id": "2366716c",
        "outputId": "48fec2f6-1bae-4ec4-ade4-351f1965b7b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Evaluating The Model\n",
        "plt.figure(figsize=(15,6))\n",
        "plt.plot(history.history['loss'])\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3d1fd1b1",
      "metadata": {
        "id": "3d1fd1b1"
      },
      "source": [
        "## Making Predictions\n",
        "To make predictions, we need to use our Test Dataset and format him like we did in the Train Dataset before."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "eb7ea4e5",
      "metadata": {
        "id": "eb7ea4e5"
      },
      "outputs": [],
      "source": [
        "#GEtting ready both train and est data set\n",
        "train_data= pd.read_csv('train_data.csv')\n",
        "test_data= pd.read_csv('test_data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "28e48056",
      "metadata": {
        "id": "28e48056"
      },
      "outputs": [],
      "source": [
        "real_stock_price = test_data.iloc[:, 3:4].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "fa48413d",
      "metadata": {
        "id": "fa48413d",
        "outputId": "27737dab-3291-48ed-835c-d4f539a9c616",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(169, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "real_stock_price.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "f9f9682e",
      "metadata": {
        "id": "f9f9682e",
        "outputId": "0e5d0871-d37c-4b2a-ebe3-06ad69d1df18",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(169, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "test_set.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "19dc34f9",
      "metadata": {
        "id": "19dc34f9",
        "outputId": "233532c4-93b9-4b50-b920-d904039fbe08",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-50-96e15a4a0e8a>:3: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
            "  inputs= data_total[len(data_total)-len(test_data)-60:].values\n"
          ]
        }
      ],
      "source": [
        "# Hence we will concatenate the dataset and then scale them\n",
        "data_total= pd.concat([train_data['Close'], test_data['Close']],  axis=0)\n",
        "inputs= data_total[len(data_total)-len(test_data)-60:].values \n",
        "inputs = inputs.reshape(-1,1) \n",
        "inputs = sc.transform(inputs)\n",
        "\n",
        "X_test = []\n",
        "for i in range(60, 230):\n",
        "    X_test.append(inputs[i-60:i, 0])\n",
        "\n",
        "X_test = np.array(X_test)\n",
        "# 3D format\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "d358bbc1",
      "metadata": {
        "id": "d358bbc1",
        "outputId": "3cf876d4-0f0e-41eb-d674-9344e82e87ba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(229, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "inputs.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "ced5aec5",
      "metadata": {
        "id": "ced5aec5",
        "outputId": "4dbc60dd-287c-4b0c-d25f-73f058fd52c5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1699,)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "data_total.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "cb614e94",
      "metadata": {
        "id": "cb614e94",
        "outputId": "ac53e9cb-6dfc-4793-a649-eb4b7d43b6f6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(170, 60, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "X_test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b1ee27e3",
      "metadata": {
        "id": "b1ee27e3"
      },
      "source": [
        "#### After format the Test Data, we can make predictions in our X_test."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "a143e7d4",
      "metadata": {
        "id": "a143e7d4",
        "outputId": "a828f2d4-172e-4c51-b62d-76adaf0980b4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6/6 [==============================] - 0s 10ms/step\n"
          ]
        }
      ],
      "source": [
        "#preict the model\n",
        "predicted_stock_price = model.predict(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5e1caabf",
      "metadata": {
        "id": "5e1caabf"
      },
      "source": [
        "#### But before plot our predictions, we need to make a inverse_transform() in the predictions array, because we make predictions using the Scale, so our predictions are between 0 and 1."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "5df60d60",
      "metadata": {
        "id": "5df60d60"
      },
      "outputs": [],
      "source": [
        "# Inverse the scaling\n",
        "predicted_stock_price = sc.inverse_transform(predicted_stock_price)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "ef62b524",
      "metadata": {
        "id": "ef62b524",
        "outputId": "84f1867e-b868-47c8-ac9c-6ac930980876",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Visualising the results\n",
        "plt.figure(figsize=(15,8))\n",
        "plt.plot(real_stock_price, color='Red', label='Real Apple Stock Price')\n",
        "plt.plot(predicted_stock_price, color='Blue', label='Predicted Apple Stock Price')\n",
        "plt.title('Apple Stock Price Prediction',fontsize=20)\n",
        "plt.xlabel('Time', fontsize=15)\n",
        "plt.ylabel('Apple Stock Price',fontsize=15)\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "90645265",
      "metadata": {
        "id": "90645265",
        "outputId": "8155ed5c-89d6-4a32-b7bb-6c05b87b6378",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Visualising the results\n",
        "plt.figure(figsize=(15,8))\n",
        "plt.plot(test_set, color='Red', label='Real Apple Stock Price')\n",
        "plt.plot(predicted_stock_price, color='Blue', label='Predicted Apple Stock Price')\n",
        "plt.title('Apple Stock Price Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Apple Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7c9645d2",
      "metadata": {
        "id": "7c9645d2"
      },
      "source": [
        "### Conclusion:"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "23790188",
      "metadata": {
        "id": "23790188"
      },
      "source": [
        "Our model performed admirably, as can be seen. It can accurately follow most unusual jumps/drops; however, we can observe that the model expected (predicted) lower values than the actual stock price for the most recent date stamps."
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}