Stock_Price_Predictor.ipynb

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   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense, Dropout\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Load training data\n",
    "dataset_train = pd.read_csv(\"Google_Stock_Price_Train.csv\")\n",
    "\n",
    "# Get training data\n",
    "training_set = dataset_train.iloc[:, 1:2].values\n",
    "\n",
    "# Scale the training data\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled_training_set = scaler.fit_transform(training_set)\n",
    "\n",
    "# Prepare training data in sequences\n",
    "X_train = []\n",
    "y_train = []\n",
    "for i in range(60, 1258):\n",
    "    X_train.append(scaled_training_set[i-60:i, 0])\n",
    "    y_train.append(scaled_training_set[i, 0])\n",
    "\n",
    "# Convert to numpy arrays\n",
    "X_train = np.array(X_train)\n",
    "y_train = np.array(y_train)\n",
    "\n",
    "# Reshape X_train for LSTM\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n",
    "\n",
    "# Define the LSTM model\n",
    "regressor = Sequential()\n",
    "regressor.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))\n",
    "regressor.add(Dropout(0.2))\n",
    "regressor.add(LSTM(units=50, return_sequences=True))\n",
    "regressor.add(Dropout(0.2))\n",
    "regressor.add(LSTM(units=50, return_sequences=True))\n",
    "regressor.add(Dropout(0.2))\n",
    "regressor.add(LSTM(units=50))\n",
    "regressor.add(Dropout(0.2))\n",
    "regressor.add(Dense(units=1))\n",
    "\n",
    "# Compile the model\n",
    "regressor.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Train the model\n",
    "regressor.fit(X_train, y_train, epochs=100, batch_size=32)\n",
    "\n",
    "# Load test data\n",
    "dataset_test = pd.read_csv(\"Google_Stock_Price_Test.csv\")\n",
    "actual_stock_price = dataset_test.iloc[:, 1:2].values\n",
    "\n",
    "# Concatenate train and test data for preparing input for prediction\n",
    "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 = scaler.transform(inputs)\n",
    "\n",
    "# Prepare test data in sequences\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",
    "\n",
    "# Predict the stock prices\n",
    "predicted_stock_price = regressor.predict(X_test)\n",
    "predicted_stock_price = scaler.inverse_transform(predicted_stock_price)\n",
    "\n",
    "# Plotting\n",
    "plt.plot(actual_stock_price, color='red', label='Actual Google Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Google Stock Price')\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel('Google Stock Price')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  }
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