RNN_StockPredictor (1).ipynb

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      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xpAuMVCwfWs8"
      },
      "source": [
        "# RNN - Stock Predictor\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nxJfRe4bfYVA"
      },
      "source": [
        "## i) Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ir9zwETrfbrp"
      },
      "source": [
        "### Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZT1f24vHffuf"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nQ47JAxrgmaL"
      },
      "source": [
        "### Importing the training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1xiv3pJOgqY3"
      },
      "source": [
        "dataset_train = pd.read_csv('Google_Stock_Price_Train.csv')\n",
        "training_set = dataset_train.iloc[:, 1:2].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HT8_2UJegtG5"
      },
      "source": [
        "### Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTrF2kR7gx9x"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "sc = MinMaxScaler(feature_range = (0, 1))\n",
        "training_set_scaled = sc.fit_transform(training_set)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JyYgYocqhNUg"
      },
      "source": [
        "### Creating a data structure with 60 timesteps and 1 output"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iofU21B0i6ST"
      },
      "source": [
        "X_train = []\n",
        "y_train = []\n",
        "for i in range(60, 1258):\n",
        "    X_train.append(training_set_scaled[i-60:i, 0])\n",
        "    y_train.append(training_set_scaled[i, 0])\n",
        "X_train, y_train = np.array(X_train), np.array(y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D8yaN7Zvi95l"
      },
      "source": [
        "### Reshaping"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FOXqJHmNjBkz"
      },
      "source": [
        "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZRRSOJeVjEWV"
      },
      "source": [
        "## ii) Building and Training the RNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k4XV88JMjHXG"
      },
      "source": [
        "### Importing the Keras libraries and packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9JRnqsxEjKsD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b3e23e78-18aa-4396-c923-c877b8de60e4"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dropout"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FEIE-1s9jNzC"
      },
      "source": [
        "### Initialising the RNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1338dJ0UjRKH"
      },
      "source": [
        "regressor = Sequential()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "62eg1OPGjT8z"
      },
      "source": [
        "### Adding the first LSTM layer and some Dropout regularisation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2hIinyXUjbVU"
      },
      "source": [
        "regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3XBIYLyOjlMx"
      },
      "source": [
        "### Adding a second LSTM layer and some Dropout regularisation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UG7nrVaSjuZ2"
      },
      "source": [
        "regressor.add(LSTM(units = 50, return_sequences = True))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ey3fHVnGj1cu"
      },
      "source": [
        "### Adding a third LSTM layer and some Dropout regularisation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PuNi6PgFj7jO"
      },
      "source": [
        "regressor.add(LSTM(units = 50, return_sequences = True))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SYTrtfTmj933"
      },
      "source": [
        "### Adding a fourth LSTM layer and some Dropout regularisation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jp4Ty8fRkBYV"
      },
      "source": [
        "regressor.add(LSTM(units = 50))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ABI6rOIkHhk"
      },
      "source": [
        "### Adding the output layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aM6R1z4WkME8"
      },
      "source": [
        "regressor.add(Dense(units = 1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zLx4K7uUkPSh"
      },
      "source": [
        "### Compiling the RNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XTrhVN-tkbko"
      },
      "source": [
        "regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-mPhwKGkkebi"
      },
      "source": [
        "### Fitting the RNN to the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I06Nkrz5kkb-"
      },
      "source": [
        "regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4hRau_lIkrE8"
      },
      "source": [
        "## iii) Making the predictions and visualising the results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SgJO6qEDksxD"
      },
      "source": [
        "### Getting the real stock price of 2017"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FmBT2zqukxTz"
      },
      "source": [
        "dataset_test = pd.read_csv('Google_Stock_Price_Test.csv')\n",
        "real_stock_price = dataset_test.iloc[:, 1:2].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GrvrLblxkz42"
      },
      "source": [
        "### Getting the predicted stock price of 2017"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "emikTvUpk3Ck"
      },
      "source": [
        "dataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']), axis = 0)\n",
        "inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values\n",
        "inputs = inputs.reshape(-1,1)\n",
        "inputs = sc.transform(inputs)\n",
        "X_test = []\n",
        "for i in range(60, 80):\n",
        "    X_test.append(inputs[i-60: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))\n",
        "predicted_stock_price = regressor.predict(X_test)\n",
        "predicted_stock_price = sc.inverse_transform(predicted_stock_price)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iFTNs3YHk6FQ"
      },
      "source": [
        "### Visualising the results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8OUI8U49k9tH"
      },
      "source": [
        "plt.plot(real_stock_price, color = 'red', label = 'Real Google Stock Price')\n",
        "plt.plot(predicted_stock_price, color = 'blue', label = 'Predicted Google Stock Price')\n",
        "plt.title('Google Stock Price Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Google Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}