Day 38.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Google Stock Price Prediction(Stanford Paper implementation) using Recurrent Neural Network in Keras and Tensorflow¶"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 1: Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import training set\n",
    "training_set=pd.read_csv('dataset\\Google_Stock_Price_Train.csv')\n",
    "training_set=training_set.iloc[:,1:2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature Scaling\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc= MinMaxScaler()\n",
    "training_set=sc.fit_transform(training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Geting the input and output\n",
    "X_train= training_set[0:1257]\n",
    "y_train= training_set[1:1258]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshaping\n",
    "X_train=np.reshape(X_train, (1257 , 1 , 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 2: Building the RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\amira\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# importing the Keras libraries and Packages\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize the RNN\n",
    "regressor = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding the input layer and LSTM layer\n",
    "regressor.add(LSTM(units=4, activation= 'sigmoid', input_shape= (None,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding the output layer\n",
    "regressor.add(Dense( units=1 ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compiling the RNN\n",
    "regressor.compile(optimizer='adam', loss='mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "1257/1257 [==============================] - 0s 96us/step - loss: 2.5369e-04\n",
      "Epoch 160/200\n",
      "1257/1257 [==============================] - 0s 94us/step - loss: 2.5442e-04\n",
      "Epoch 161/200\n",
      "1257/1257 [==============================] - 0s 97us/step - loss: 2.5412e-04\n",
      "Epoch 162/200\n",
      "1257/1257 [==============================] - 0s 95us/step - loss: 2.5620e-04\n",
      "Epoch 163/200\n",
      "1257/1257 [==============================] - 0s 97us/step - loss: 2.5446e-04\n",
      "Epoch 164/200\n",
      "1257/1257 [==============================] - 0s 103us/step - loss: 2.5370e-04\n",
      "Epoch 165/200\n",
      "1257/1257 [==============================] - 0s 96us/step - loss: 2.5355e-04\n",
      "Epoch 166/200\n",
      "1257/1257 [==============================] - 0s 97us/step - loss: 2.5389e-04\n",
      "Epoch 167/200\n",
      "1257/1257 [==============================] - 0s 88us/step - loss: 2.5419e-04\n",
      "Epoch 168/200\n",
      "1257/1257 [==============================] - 0s 94us/step - loss: 2.5690e-04\n",
      "Epoch 169/200\n",
      "1257/1257 [==============================] - 0s 89us/step - loss: 2.5622e-04\n",
      "Epoch 170/200\n",
      "1257/1257 [==============================] - 0s 99us/step - loss: 2.5459e-04\n",
      "Epoch 171/200\n",
      "1257/1257 [==============================] - 0s 95us/step - loss: 2.5325e-04\n",
      "Epoch 172/200\n",
      "1257/1257 [==============================] - 0s 94us/step - loss: 2.5363e-04\n",
      "Epoch 173/200\n",
      "1257/1257 [==============================] - 0s 91us/step - loss: 2.5323e-04\n",
      "Epoch 174/200\n",
      "1257/1257 [==============================] - 0s 91us/step - loss: 2.5508e-04\n",
      "Epoch 175/200\n",
      "1257/1257 [==============================] - 0s 95us/step - loss: 2.5691e-04\n",
      "Epoch 176/200\n",
      "1257/1257 [==============================] - 0s 91us/step - loss: 2.5263e-04\n",
      "Epoch 177/200\n",
      "1257/1257 [==============================] - 0s 97us/step - loss: 2.5365e-04\n",
      "Epoch 178/200\n",
      "1257/1257 [==============================] - 0s 91us/step - loss: 2.5569e-04\n",
      "Epoch 179/200\n",
      "1257/1257 [==============================] - 0s 105us/step - loss: 2.5347e-04\n",
      "Epoch 180/200\n",
      "1257/1257 [==============================] - 0s 92us/step - loss: 2.5399e-04\n",
      "Epoch 181/200\n",
      "1257/1257 [==============================] - 0s 99us/step - loss: 2.5280e-04\n",
      "Epoch 182/200\n",
      "1257/1257 [==============================] - 0s 103us/step - loss: 2.5514e-04\n",
      "Epoch 183/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1257/1257 [==============================] - 0s 91us/step - loss: 2.5586e-04\n",
      "Epoch 184/200\n",
      "1257/1257 [==============================] - 0s 84us/step - loss: 2.5802e-04\n",
      "Epoch 185/200\n",
      "1257/1257 [==============================] - 0s 92us/step - loss: 2.5568e-04\n",
      "Epoch 186/200\n",
      "1257/1257 [==============================] - 0s 81us/step - loss: 2.5516e-04\n",
      "Epoch 187/200\n",
      "1257/1257 [==============================] - 0s 98us/step - loss: 2.5141e-04\n",
      "Epoch 188/200\n",
      "1257/1257 [==============================] - 0s 90us/step - loss: 2.5528e-04\n",
      "Epoch 189/200\n",
      "1257/1257 [==============================] - 0s 90us/step - loss: 2.5740e-04\n",
      "Epoch 190/200\n",
      "1257/1257 [==============================] - 0s 93us/step - loss: 2.5476e-04\n",
      "Epoch 191/200\n",
      "1257/1257 [==============================] - 0s 90us/step - loss: 2.5128e-04\n",
      "Epoch 192/200\n",
      "1257/1257 [==============================] - 0s 82us/step - loss: 2.5257e-04\n",
      "Epoch 193/200\n",
      "1257/1257 [==============================] - 0s 83us/step - loss: 2.5193e-04\n",
      "Epoch 194/200\n",
      "1257/1257 [==============================] - 0s 95us/step - loss: 2.5222e-04\n",
      "Epoch 195/200\n",
      "1257/1257 [==============================] - 0s 94us/step - loss: 2.5199e-04\n",
      "Epoch 196/200\n",
      "1257/1257 [==============================] - 0s 85us/step - loss: 2.5210e-04\n",
      "Epoch 197/200\n",
      "1257/1257 [==============================] - 0s 103us/step - loss: 2.5457e-04\n",
      "Epoch 198/200\n",
      "1257/1257 [==============================] - 0s 92us/step - loss: 2.5225e-04\n",
      "Epoch 199/200\n",
      "1257/1257 [==============================] - 0s 92us/step - loss: 2.5444e-04\n",
      "Epoch 200/200\n",
      "1257/1257 [==============================] - 0s 92us/step - loss: 2.5848e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x21d53dfaf98>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the RNN to the training set\n",
    "regressor.fit(X_train, y_train, batch_size=32, epochs=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 3: Making the Prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Geting the real stock price of 2017\n",
    "test_set = pd.read_csv('dataset\\Google_Stock_Price_Test.csv')\n",
    "real_stock_price = test_set.iloc[:,1:2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Geting the Predicted Stock Price of 2017\n",
    "inputs = real_stock_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = sc.transform(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = np.reshape(inputs, (20 , 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_stock_price = regressor.predict(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[417606.72],\n",
       "       [422131.28],\n",
       "       [421051.97],\n",
       "       [425394.1 ],\n",
       "       [430650.6 ],\n",
       "       [431338.4 ],\n",
       "       [429990.7 ],\n",
       "       [430999.28],\n",
       "       [431159.4 ],\n",
       "       [430971.  ],\n",
       "       [430372.53],\n",
       "       [430047.28],\n",
       "       [430890.88],\n",
       "       [431051.1 ],\n",
       "       [438129.47],\n",
       "       [441563.28],\n",
       "       [445398.34],\n",
       "       [443947.6 ],\n",
       "       [434539.3 ],\n",
       "       [426149.84]], dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_stock_price = sc.inverse_transform(predicted_stock_price)\n",
    "predicted_stock_price"
   ]
  }
 ],
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