Apple_Stock_Price_Prediction_with_LSTM_using_Python.ipynb

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        "# Importing necessary libraries\n",
        "\n",
        "# pandas is used for data manipulation and analysis.\n",
        "import pandas as pd\n",
        "\n",
        "# yfinance is a library to retrieve financial data from Yahoo Finance.\n",
        "import yfinance as yf\n",
        "\n",
        "# datetime is a module for manipulating dates and times.\n",
        "import datetime\n",
        "\n",
        "# Importing specific classes from the datetime module:\n",
        "# 'date' for working with dates and 'timedelta' for working with time differences.\n",
        "from datetime import date, timedelta"
      ]
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        "# Setting up the end date as today's date.\n",
        "end_date = date.today()"
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        "# Get today date and format it as a string in the format 'YYYY-MM-DD'.\n",
        "d1 = today.strftime(\"%Y-%m-%d\")\n",
        "\n",
        "# Assign the formatted date string to the variable 'end_date'.\n",
        "# This will represent the end date for any data analysis or retrieval.\n",
        "end_date = d1\n",
        "\n",
        "# Calculate the date 5000 days before today using timedelta.\n",
        "d2 = date.today() - timedelta(days=5000)\n",
        "\n",
        "# Format the calculated date as a string in the format 'YYYY-MM-DD'.\n",
        "d2 = d2.strftime(\"%Y-%m-%d\")\n",
        "\n",
        "# Assign the formatted date string to the variable 'start_date'.\n",
        "# This will represent the start date for any data analysis or retrieval.\n",
        "start_date = d2\n"
      ],
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        "# Download historical market data for Apple stock ('AAPL') using yfinance.\n",
        "# The data is retrieved from 'start_date' to 'end_date'.\n",
        "# 'progress=False' disables the progress bar during data download.\n",
        "data = yf.download('AAPL',\n",
        "                   start=start_date,\n",
        "                   end=end_date,\n",
        "                   progress=False)\n",
        "\n",
        "# Extract the index (which is the date) and store it in a new column called \"Date\".\n",
        "data[\"Date\"] = data.index\n",
        "\n",
        "# Reorder the columns to display \"Date\", \"Open\", \"High\", \"Low\", \"Close\", \"Adj Close\", and \"Volume\".\n",
        "data = data[[\"Date\", \"Open\", \"High\", \"Low\", \"Close\",\n",
        "             \"Adj Close\", \"Volume\"]]\n",
        "\n",
        "# Reset the index of the DataFrame and drop the old index column.\n",
        "# This makes \"Date\" a regular column instead of the index.\n",
        "data.reset_index(drop=True, inplace=True)\n",
        "\n",
        "# Display the last few rows of the DataFrame to review the final data structure and content.\n",
        "data.tail()\n"
      ],
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              "           Date        Open        High         Low       Close   Adj Close  \\\n",
              "3437 2024-08-12  216.070007  219.509995  215.600006  217.529999  217.529999   \n",
              "3438 2024-08-13  219.009995  221.889999  219.009995  221.270004  221.270004   \n",
              "3439 2024-08-14  220.570007  223.029999  219.699997  221.720001  221.720001   \n",
              "3440 2024-08-15  224.600006  225.350006  222.759995  224.720001  224.720001   \n",
              "3441 2024-08-16  223.919998  226.830002  223.649994  226.050003  226.050003   \n",
              "\n",
              "        Volume  \n",
              "3437  38028100  \n",
              "3438  44155300  \n",
              "3439  41960600  \n",
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        "# Import the necessary module from Plotly for creating graphical visualizations.\n",
        "# 'graph_objects' is a part of Plotly that allows the creation of various types of charts, including candlestick charts.\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "# Create a figure object using Plotly's Candlestick chart type.\n",
        "# The x-axis represents the dates, and the y-axis represents stock prices (open, high, low, and close prices).\n",
        "figure = go.Figure(data=[go.Candlestick(x=data[\"Date\"],    # Set the x-axis to the dates\n",
        "                                        open=data[\"Open\"],  # Set the opening prices on the y-axis\n",
        "                                        high=data[\"High\"],  # Set the highest prices on the y-axis\n",
        "                                        low=data[\"Low\"],    # Set the lowest prices on the y-axis\n",
        "                                        close=data[\"Close\"] # Set the closing prices on the y-axis\n",
        "                                       )])\n",
        "\n",
        "# Update the layout of the figure to include a title and hide the range slider on the x-axis.\n",
        "figure.update_layout(title=\"Apple Stock Price Analysis\",  # Set the title of the chart\n",
        "                     xaxis_rangeslider_visible=False)     # Disable the x-axis range slider, which is often included by default in candlestick charts\n",
        "\n",
        "# Display the figure in an interactive window.\n",
        "figure.show()\n"
      ],
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        {
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/_plotly_utils/basevalidators.py:105: FutureWarning: The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray. To retain the old behavior, call `np.array` on the result\n",
            "  v = v.dt.to_pydatetime()\n"
          ]
        },
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          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the correlation matrix for the DataFrame 'data'.\n",
        "# The correlation matrix shows the pairwise correlation between all columns in the DataFrame.\n",
        "correlation = data.corr()\n",
        "\n",
        "# Print the correlation values of the 'Close' price with all other columns in descending order.\n",
        "# 'sort_values(ascending=False)' sorts these correlation values from highest to lowest.\n",
        "print(correlation[\"Close\"].sort_values(ascending=False))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2SwjxBqUX-an",
        "outputId": "298d2960-eefe-40c9-ff05-a03cd2c9ec69"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Close        1.000000\n",
            "Adj Close    0.999960\n",
            "High         0.999891\n",
            "Low          0.999888\n",
            "Open         0.999762\n",
            "Date         0.903697\n",
            "Volume      -0.526661\n",
            "Name: Close, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Select the \"Open\", \"High\", \"Low\", and \"Volume\" columns from the DataFrame 'data'.\n",
        "# These columns will be used as the features (input variables) for a machine learning model.\n",
        "x = data[[\"Open\", \"High\", \"Low\", \"Volume\"]]\n",
        "\n",
        "# Select the \"Close\" column from the DataFrame 'data'.\n",
        "# This column will be used as the target variable (output) that we want to predict.\n",
        "y = data[\"Close\"]\n",
        "\n",
        "\n",
        "# Import the train_test_split function from scikit-learn's model_selection module.\n",
        "# This function is used to split the dataset into training and testing sets.\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the features (x) and target variable (y) into training and testing sets.\n",
        "# 'test_size=0.2' means 20% of the data will be used for testing, and 80% will be used for training.\n",
        "# 'random_state=42' ensures that the split is reproducible, meaning you'll get the same split every time.\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "0QMItKTuYAny"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# Initialize the MinMaxScaler for scaling the data to a range of 0 to 1.\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "\n",
        "# Fit the scaler on the training data and transform it.\n",
        "# Only the features (x) are scaled, while y remains as is for now.\n",
        "xtrain = scaler.fit_transform(xtrain)\n",
        "\n",
        "# Transform the test data using the same scaler fitted on the training data.\n",
        "xtest = scaler.transform(xtest)\n",
        "\n",
        "# It's also common to scale the target variable y for LSTM models.\n",
        "# However, if you do this, remember to inverse the scaling after predictions to get actual values.\n",
        "ytrain = scaler.fit_transform(ytrain)\n",
        "ytest = scaler.transform(ytest)\n"
      ],
      "metadata": {
        "id": "b2IQSgH2SE8i",
        "outputId": "6bd7e17a-022f-4d2e-afa6-23e16b1f6e35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Expected 2D array, got 1D array instead:\narray=[ 88.40750122 147.41000366  47.03749847 ...  24.14999962  21.02928543\n 194.5       ].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-28d9491553cc>\u001b[0m in \u001b[0;36m<cell line: 15>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;31m# It's also common to scale the target variable y for LSTM models.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;31m# However, if you do this, remember to inverse the scaling after predictions to get actual values.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mytrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mytrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0mytest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mytest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/_set_output.py\u001b[0m in \u001b[0;36mwrapped\u001b[0;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[1;32m    155\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 157\u001b[0;31m         \u001b[0mdata_to_wrap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    158\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_to_wrap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m             \u001b[0;31m# only wrap the first output for cross decomposition\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    914\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    915\u001b[0m             \u001b[0;31m# fit method of arity 1 (unsupervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 916\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    917\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    918\u001b[0m             \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_data.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    433\u001b[0m         \u001b[0;31m# Reset internal state before fitting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    434\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 435\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartial_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0m_fit_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefer_skip_nested_validation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1150\u001b[0m                 )\n\u001b[1;32m   1151\u001b[0m             ):\n\u001b[0;32m-> 1152\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfit_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1154\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_data.py\u001b[0m in \u001b[0;36mpartial_fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    471\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    472\u001b[0m         \u001b[0mfirst_pass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"n_samples_seen_\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 473\u001b[0;31m         X = self._validate_data(\n\u001b[0m\u001b[1;32m    474\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    475\u001b[0m             \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfirst_pass\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[1;32m    603\u001b[0m                 \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"X\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    606\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    936\u001b[0m             \u001b[0;31m# If input is 1D raise error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    937\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 938\u001b[0;31m                 raise ValueError(\n\u001b[0m\u001b[1;32m    939\u001b[0m                     \u001b[0;34m\"Expected 2D array, got 1D array instead:\\narray={}.\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    940\u001b[0m                     \u001b[0;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[ 88.40750122 147.41000366  47.03749847 ...  24.14999962  21.02928543\n 194.5       ].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xtrain"
      ],
      "metadata": {
        "id": "mraL6dQVRreM",
        "outputId": "4807a2ca-6898-47b7-a939-5af2ceea9d95",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.3543656 , 0.35409204, 0.34759218, 0.09758396],\n",
              "       [0.60283344, 0.60975473, 0.61144271, 0.01427001],\n",
              "       [0.15935738, 0.15877016, 0.1597437 , 0.03709724],\n",
              "       ...,\n",
              "       [0.05620198, 0.05749011, 0.05724403, 0.08715287],\n",
              "       [0.04409684, 0.04409667, 0.04464915, 0.11136419],\n",
              "       [0.80987699, 0.81587861, 0.82085648, 0.01261402]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ytrain"
      ],
      "metadata": {
        "id": "PKN0ZkOrRzgN",
        "outputId": "0151532a-4c94-48d8-ea18-b6c29bd573ea",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.34508214],\n",
              "       [0.60900626],\n",
              "       [0.16002996],\n",
              "       ...,\n",
              "       [0.05765187],\n",
              "       [0.0436926 ],\n",
              "       [0.81964455]])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM\n",
        "model = Sequential()\n",
        "model.add(LSTM(128, return_sequences=True, input_shape= (xtrain.shape[1], 1)))\n",
        "model.add(LSTM(64, return_sequences=False))\n",
        "model.add(Dense(25))\n",
        "model.add(Dense(1))\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8KCqHG4AYDg7",
        "outputId": "8c014869-8512-4b3f-e172-e50852ac8689"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 4, 128)            66560     \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 64)                49408     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 25)                1625      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 26        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 117,619\n",
            "Trainable params: 117,619\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "model.fit(xtrain, ytrain, batch_size=1, epochs=30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zvWpF3g_YFJQ",
        "outputId": "b46c045e-8ffd-4f89-821b-d04b72c1d306"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "2757/2757 [==============================] - 25s 7ms/step - loss: 265.1997\n",
            "Epoch 2/30\n",
            "2757/2757 [==============================] - 18s 7ms/step - loss: 16.4243\n",
            "Epoch 3/30\n",
            "1120/2757 [===========>..................] - ETA: 10s - loss: 10.1200"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "#features = [Open, High, Low, Adj Close, Volume]\n",
        "features = np.array([[177.089996, 180.419998, 177.070007, 74919600]])\n",
        "model.predict(features)"
      ],
      "metadata": {
        "id": "mNcY8nKTYHYJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Jd3ntzykYJ6o"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}