StockPrediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "baa46960",
   "metadata": {},
   "source": [
    "### Stock Prediction Using Machine learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "77f76433",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pandas_datareader as web\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7401210d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e59807a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f4b1e21d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "#loading data\n",
    "import yfinance as yf\n",
    "\n",
    "import datetime as dt\n",
    "import pandas_datareader as web\n",
    "\n",
    "company = 'FB'\n",
    "\n",
    "\n",
    "tz = pytz.timezone(\"America/New_York\")\n",
    "start = tz.localize(dt.datetime(2013,1,1))\n",
    "end = tz.localize(dt.datetime.now())\n",
    "\n",
    "tickers = \"AMZN\".split(\",\")\n",
    "data = yf.download(tickers,start, end, auto_adjust=True)['Close']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4ea79fe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#preparing data\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled_data = scaler.fit_transform(data.values.reshape(-1, 1))\n",
    "\n",
    "prediction_days = 60\n",
    "\n",
    "x_train = []\n",
    "y_train = []\n",
    "\n",
    "for x in range(prediction_days, len(scaled_data)):\n",
    "    x_train.append(scaled_data[x-prediction_days:x, 0])\n",
    "    y_train.append(scaled_data[x, 0])\n",
    "\n",
    "x_train, y_train = np.array(x_train), np.array(y_train)\n",
    "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1],1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6f61c8ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "85/85 [==============================] - 10s 41ms/step - loss: 0.0173\n",
      "Epoch 2/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 0.0021\n",
      "Epoch 3/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 0.0014\n",
      "Epoch 4/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 0.0012\n",
      "Epoch 5/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 0.0012\n",
      "Epoch 6/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 9.7829e-04\n",
      "Epoch 7/25\n",
      "85/85 [==============================] - 3s 29ms/step - loss: 9.0853e-04\n",
      "Epoch 8/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 9.7563e-04\n",
      "Epoch 9/25\n",
      "85/85 [==============================] - 3s 31ms/step - loss: 7.5984e-04\n",
      "Epoch 10/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 7.6213e-04\n",
      "Epoch 11/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 7.9900e-04\n",
      "Epoch 12/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 7.2168e-04\n",
      "Epoch 13/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 6.6182e-04\n",
      "Epoch 14/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 6.7278e-04\n",
      "Epoch 15/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 6.2958e-04\n",
      "Epoch 16/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 5.8501e-04\n",
      "Epoch 17/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 6.6698e-04\n",
      "Epoch 18/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 5.8386e-04\n",
      "Epoch 19/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 5.5368e-04\n",
      "Epoch 20/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 5.1673e-04\n",
      "Epoch 21/25\n",
      "85/85 [==============================] - 3s 31ms/step - loss: 5.8977e-04\n",
      "Epoch 22/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 4.9809e-04\n",
      "Epoch 23/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 6.3180e-04\n",
      "Epoch 24/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 5.0187e-04\n",
      "Epoch 25/25\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 5.8256e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x292e1c7c0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#building Model\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(LSTM(units=50,return_sequences=True, input_shape=(x_train. shape[1], 1)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(units=50, return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(units=50))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(units=1)) #prediction of the next closing values\n",
    "\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "model.fit(x_train, y_train, epochs=25, batch_size=32)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "0db0bedf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "156/156 [==============================] - 2s 10ms/step\n"
     ]
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "import datetime as dt\n",
    "import pytz\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Function to create sequences from data\n",
    "def create_sequences(data, prediction_days):\n",
    "    sequences = []\n",
    "    for i in range(prediction_days, len(data)):\n",
    "        sequences.append(data[i - prediction_days:i, 0])\n",
    "    return np.array(sequences)\n",
    "\n",
    "# Load Training Data\n",
    "tz = pytz.timezone(\"America/New_York\")\n",
    "start_train = tz.localize(dt.datetime(2015, 1, 1))\n",
    "end_train = tz.localize(dt.datetime.now())\n",
    "tickers = \"AMZN\".split(\",\")\n",
    "train_data = yf.download(tickers, start_train, end_train, auto_adjust=True)['Close']\n",
    "\n",
    "# Create MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "\n",
    "# Scale the training data\n",
    "train_data_scaled = scaler.fit_transform(train_data.values.reshape(-1, 1))\n",
    "\n",
    "# Number of days for prediction\n",
    "prediction_days = 60\n",
    "\n",
    "# Create sequences for training\n",
    "x_train = create_sequences(train_data_scaled, prediction_days)\n",
    "\n",
    "# Load Test Data\n",
    "start_test = tz.localize(dt.datetime(2013, 1, 1))\n",
    "end_test = tz.localize(dt.datetime.now())\n",
    "test_data = yf.download(tickers, start_test, end_test, auto_adjust=True)['Close']\n",
    "\n",
    "# Scale the test data\n",
    "test_data_scaled = scaler.transform(test_data.values.reshape(-1, 1))\n",
    "\n",
    "# Create sequences for testing\n",
    "x_test = create_sequences(np.concatenate((train_data_scaled, test_data_scaled), axis=0), prediction_days)\n",
    "\n",
    "#making prediction on test data\n",
    "\n",
    "#making prediction on test data\n",
    "\n",
    "# Corrected: use x_test instead of model_inputs\n",
    "for x in range(prediction_days, len(x_test)):\n",
    "    x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))\n",
    "\n",
    "\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",
    "predicted_prices = model.predict(x_test)\n",
    "predicted_prices = scaler.inverse_transform(predicted_prices)\n",
    "\n",
    "\n",
    "# Concatenate training and test data for visualization\n",
    "total_dataset = pd.concat((pd.Series(train_data_scaled.flatten(), index=train_data.index),\n",
    "                          pd.Series(test_data_scaled.flatten(), index=test_data.index)), axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "1b17ef54",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the test predictions\n",
    "\n",
    "plt.plot(actual_prices, color=\"black\", label=f\"Actual {company} Price\")\n",
    "plt.plot(predicted_prices, color='green', label=f\"Predicted {company} Price\")\n",
    "plt.title(f\"{company} Share Price\")\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel(f'{company} Share Price')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}