TATA .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stock Prices Prediction Using Keras Long Term Short Memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Part 1 - Data Preprocessing\n",
    "\n",
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the training set\n",
    "dataset_train = pd.read_csv('NSE-TATAGLOBAL.csv')\n",
    "training_set = dataset_train.iloc[:, 1:2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Close</th>\n",
       "      <th>Total Trade Quantity</th>\n",
       "      <th>Turnover (Lacs)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-09-28</td>\n",
       "      <td>234.05</td>\n",
       "      <td>235.95</td>\n",
       "      <td>230.20</td>\n",
       "      <td>233.50</td>\n",
       "      <td>233.75</td>\n",
       "      <td>3069914</td>\n",
       "      <td>7162.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>234.55</td>\n",
       "      <td>236.80</td>\n",
       "      <td>231.10</td>\n",
       "      <td>233.80</td>\n",
       "      <td>233.25</td>\n",
       "      <td>5082859</td>\n",
       "      <td>11859.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-09-26</td>\n",
       "      <td>240.00</td>\n",
       "      <td>240.00</td>\n",
       "      <td>232.50</td>\n",
       "      <td>235.00</td>\n",
       "      <td>234.25</td>\n",
       "      <td>2240909</td>\n",
       "      <td>5248.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-09-25</td>\n",
       "      <td>233.30</td>\n",
       "      <td>236.75</td>\n",
       "      <td>232.00</td>\n",
       "      <td>236.25</td>\n",
       "      <td>236.10</td>\n",
       "      <td>2349368</td>\n",
       "      <td>5503.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-09-24</td>\n",
       "      <td>233.55</td>\n",
       "      <td>239.20</td>\n",
       "      <td>230.75</td>\n",
       "      <td>234.00</td>\n",
       "      <td>233.30</td>\n",
       "      <td>3423509</td>\n",
       "      <td>7999.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date    Open    High     Low    Last   Close  Total Trade Quantity  \\\n",
       "0  2018-09-28  234.05  235.95  230.20  233.50  233.75               3069914   \n",
       "1  2018-09-27  234.55  236.80  231.10  233.80  233.25               5082859   \n",
       "2  2018-09-26  240.00  240.00  232.50  235.00  234.25               2240909   \n",
       "3  2018-09-25  233.30  236.75  232.00  236.25  236.10               2349368   \n",
       "4  2018-09-24  233.55  239.20  230.75  234.00  233.30               3423509   \n",
       "\n",
       "   Turnover (Lacs)  \n",
       "0          7162.35  \n",
       "1         11859.95  \n",
       "2          5248.60  \n",
       "3          5503.90  \n",
       "4          7999.55  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler(feature_range = (0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a data structure with 60 timesteps and 1 output\n",
    "X_train = []\n",
    "y_train = []\n",
    "for i in range(60, 2035):\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshaping\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Part 2 - Building the RNN\n",
    "\n",
    "# Importing the Keras libraries and packages\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialising the RNN\n",
    "regressor = Sequential()\n",
    "\n",
    "# Adding the first LSTM layer and some Dropout regularisation\n",
    "regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding a second LSTM layer and some Dropout regularisation\n",
    "regressor.add(LSTM(units = 50, return_sequences = True))\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Adding a third LSTM layer and some Dropout regularisation\n",
    "regressor.add(LSTM(units = 50, return_sequences = True))\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding a fourth LSTM layer and some Dropout regularisation\n",
    "regressor.add(LSTM(units = 50))\n",
    "regressor.add(Dropout(0.2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1975/1975 [==============================] - 10s 5ms/step - loss: 0.0138\n",
      "Epoch 2/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0031\n",
      "Epoch 3/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0028\n",
      "Epoch 4/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0025\n",
      "Epoch 5/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0022\n",
      "Epoch 6/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0024\n",
      "Epoch 7/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0023\n",
      "Epoch 8/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0023\n",
      "Epoch 9/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0019\n",
      "Epoch 10/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0017\n",
      "Epoch 11/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0019\n",
      "Epoch 12/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0016\n",
      "Epoch 13/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0016\n",
      "Epoch 14/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0017\n",
      "Epoch 15/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0016\n",
      "Epoch 16/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0015\n",
      "Epoch 17/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0014\n",
      "Epoch 18/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0014\n",
      "Epoch 19/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0015\n",
      "Epoch 20/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0015\n",
      "Epoch 21/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0014\n",
      "Epoch 22/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0014\n",
      "Epoch 23/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0013\n",
      "Epoch 24/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0014\n",
      "Epoch 25/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0014\n",
      "Epoch 26/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0013\n",
      "Epoch 27/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0011\n",
      "Epoch 28/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0012\n",
      "Epoch 29/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0010\n",
      "Epoch 30/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0013\n",
      "Epoch 31/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0011\n",
      "Epoch 32/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0010\n",
      "Epoch 33/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0010\n",
      "Epoch 34/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0011\n",
      "Epoch 35/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0011\n",
      "Epoch 36/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 9.7196e-04\n",
      "Epoch 37/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 9.1687e-04\n",
      "Epoch 38/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 8.9500e-04\n",
      "Epoch 39/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0010\n",
      "Epoch 40/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0010\n",
      "Epoch 41/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 8.6827e-04\n",
      "Epoch 42/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 8.7459e-04\n",
      "Epoch 43/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 0.0010\n",
      "Epoch 44/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 9.7468e-04\n",
      "Epoch 45/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 8.9057e-04\n",
      "Epoch 46/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 8.8332e-04\n",
      "Epoch 47/100\n",
      "1975/1975 [==============================] - 9s 5ms/step - loss: 8.1873e-04\n",
      "Epoch 48/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 8.8087e-04\n",
      "Epoch 49/100\n",
      "1975/1975 [==============================] - 9s 4ms/step - loss: 8.6222e-04\n",
      "Epoch 50/100\n",
      "1975/1975 [==============================] - 9s 4ms/step - loss: 7.5982e-04\n",
      "Epoch 51/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 8.2238e-04\n",
      "Epoch 52/100\n",
      "1975/1975 [==============================] - 9s 4ms/step - loss: 7.3647e-04\n",
      "Epoch 53/100\n",
      "1975/1975 [==============================] - 9s 4ms/step - loss: 8.4500e-04\n",
      "Epoch 54/100\n",
      "1975/1975 [==============================] - 9s 5ms/step - loss: 8.2432e-04\n",
      "Epoch 55/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 7.7150e-04\n",
      "Epoch 56/100\n",
      "1975/1975 [==============================] - 9s 4ms/step - loss: 7.5736e-04\n",
      "Epoch 57/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 9.3101e-04\n",
      "Epoch 58/100\n",
      "1975/1975 [==============================] - 9s 4ms/step - loss: 7.1523e-04\n",
      "Epoch 59/100\n",
      "1975/1975 [==============================] - 9s 4ms/step - loss: 7.8731e-04\n",
      "Epoch 60/100\n",
      "1975/1975 [==============================] - 9s 4ms/step - loss: 8.1616e-04\n",
      "Epoch 61/100\n",
      "1975/1975 [==============================] - 9s 5ms/step - loss: 7.4150e-04\n",
      "Epoch 62/100\n",
      "1975/1975 [==============================] - 10s 5ms/step - loss: 8.8965e-04\n",
      "Epoch 63/100\n",
      "1975/1975 [==============================] - 10s 5ms/step - loss: 8.8672e-04\n",
      "Epoch 64/100\n",
      "1975/1975 [==============================] - 10s 5ms/step - loss: 8.4897e-04\n",
      "Epoch 65/100\n",
      "1975/1975 [==============================] - 9s 5ms/step - loss: 6.5017e-04\n",
      "Epoch 66/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 7.6919e-04\n",
      "Epoch 67/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 9.2458e-04\n",
      "Epoch 68/100\n",
      "1975/1975 [==============================] - 9s 4ms/step - loss: 6.5442e-04\n",
      "Epoch 69/100\n",
      "1975/1975 [==============================] - 9s 4ms/step - loss: 7.0389e-04\n",
      "Epoch 70/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 7.2696e-04\n",
      "Epoch 71/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 7.8870e-04\n",
      "Epoch 72/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 6.5917e-04\n",
      "Epoch 73/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 6.3472e-04\n",
      "Epoch 74/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 6.5746e-04\n",
      "Epoch 75/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 6.4122e-04\n",
      "Epoch 76/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 7.0383e-04\n",
      "Epoch 77/100\n",
      "1975/1975 [==============================] - 9s 4ms/step - loss: 7.1377e-04\n",
      "Epoch 78/100\n",
      "1975/1975 [==============================] - 9s 5ms/step - loss: 6.9999e-04\n",
      "Epoch 79/100\n",
      "1975/1975 [==============================] - 9s 5ms/step - loss: 6.7806e-04\n",
      "Epoch 80/100\n",
      "1975/1975 [==============================] - 8s 4ms/step - loss: 8.1988e-04\n",
      "Epoch 81/100\n",
      "1975/1975 [==============================] - 10s 5ms/step - loss: 6.0649e-04\n",
      "Epoch 82/100\n",
      "1975/1975 [==============================] - 11s 5ms/step - loss: 7.3045e-04\n",
      "Epoch 83/100\n",
      "1975/1975 [==============================] - 10s 5ms/step - loss: 7.0614e-04\n",
      "Epoch 84/100\n",
      "1975/1975 [==============================] - 10s 5ms/step - loss: 6.4810e-04\n",
      "Epoch 85/100\n",
      "1975/1975 [==============================] - 10s 5ms/step - loss: 6.4796e-04\n",
      "Epoch 86/100\n",
      "1975/1975 [==============================] - 11s 5ms/step - loss: 6.0073e-04\n",
      "Epoch 87/100\n",
      "1975/1975 [==============================] - 10s 5ms/step - loss: 5.9841e-04\n",
      "Epoch 88/100\n",
      "1975/1975 [==============================] - 9s 5ms/step - loss: 8.0090e-04\n",
      "Epoch 89/100\n",
      "1975/1975 [==============================] - 10s 5ms/step - loss: 6.9936e-04\n",
      "Epoch 90/100\n",
      "1975/1975 [==============================] - 10s 5ms/step - loss: 6.6105e-04\n",
      "Epoch 91/100\n",
      "1975/1975 [==============================] - 10s 5ms/step - loss: 6.1603e-04\n",
      "Epoch 92/100\n",
      "1975/1975 [==============================] - 12s 6ms/step - loss: 5.9800e-04\n",
      "Epoch 93/100\n",
      "1975/1975 [==============================] - 12s 6ms/step - loss: 8.1203e-04\n",
      "Epoch 94/100\n",
      "1975/1975 [==============================] - 11s 6ms/step - loss: 6.3649e-04\n",
      "Epoch 95/100\n",
      "1975/1975 [==============================] - 10s 5ms/step - loss: 6.7637e-04\n",
      "Epoch 96/100\n",
      "1975/1975 [==============================] - 11s 5ms/step - loss: 6.2025e-04\n",
      "Epoch 97/100\n",
      "1975/1975 [==============================] - 13s 6ms/step - loss: 6.8713e-04\n",
      "Epoch 98/100\n",
      "1975/1975 [==============================] - 11s 6ms/step - loss: 6.1618e-04\n",
      "Epoch 99/100\n",
      "1975/1975 [==============================] - 10s 5ms/step - loss: 5.8578e-04\n",
      "Epoch 100/100\n",
      "1975/1975 [==============================] - 11s 6ms/step - loss: 6.8071e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f82f016edd8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding the output layer\n",
    "regressor.add(Dense(units = 1))\n",
    "\n",
    "# Compiling the RNN\n",
    "regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
    "\n",
    "# Fitting the RNN to the Training set\n",
    "regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Part 3 - Making the predictions and visualising the results\n",
    "\n",
    "# Getting the real stock price of 2017\n",
    "dataset_test = pd.read_csv('tatatest.csv')\n",
    "real_stock_price = dataset_test.iloc[:, 1:2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the predicted stock price of 2017\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 = sc.transform(inputs)\n",
    "X_test = []\n",
    "for i in range(60, 76):\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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f82c3014208>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the results\n",
    "plt.plot(real_stock_price, color = 'red', label = 'Real TATA Stock Price')\n",
    "plt.plot(predicted_stock_price, color = 'blue', label = 'Predicted TAT Stock Price')\n",
    "plt.title('TATA Stock Price Prediction')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('TATA Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}