bitcoin_lstm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "06d7c62f-0bcb-4bcc-9972-85c85ff4d403",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.models import Sequential, load_model\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import LSTM\n",
    "from tensorflow.keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "77ffa31e-a098-4086-a9f3-2501b6ec4b4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1726, 1)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the training set\n",
    "dataset_train = pd.read_csv('train.csv')\n",
    "training_set = dataset_train.iloc[:, 1:2].values\n",
    "training_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "53a117b4-4201-4bfd-8ac1-286043573074",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1006.210022],\n",
       "       [ 1007.650024],\n",
       "       [ 1026.119995],\n",
       "       ...,\n",
       "       [62941.80469 ],\n",
       "       [61460.07813 ],\n",
       "       [61068.875   ]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d3858b4a-7933-4c15-82fe-8ddeee0369b8",
   "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": 5,
   "id": "a879565f-f719-4d67-9239-e3b5f26ec682",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a data structure with 80 timesteps and 1 output\n",
    "X_train = []\n",
    "y_train = []\n",
    "for i in range(80, len(training_set)):\n",
    "    X_train.append(training_set_scaled[i-80: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": 6,
   "id": "8c3267d9-c2f2-4c59-b77e-1c73a66007f9",
   "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": 7,
   "id": "df2489fb-ac96-49cc-b48a-6fd120ea937d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialising the RNN\n",
    "regressor = tf.keras.Sequential()\n",
    "\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))\n",
    "\n",
    "# Adding a second LSTM layer and some Dropout regularisation\n",
    "regressor.add(LSTM(units = 50, return_sequences = True))\n",
    "regressor.add(Dropout(0.2))\n",
    "\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))\n",
    "\n",
    "# Adding a fourth LSTM layer and some Dropout regularisation\n",
    "regressor.add(LSTM(units = 50))\n",
    "regressor.add(Dropout(0.2))\n",
    "\n",
    "# Adding the output layer\n",
    "regressor.add(Dense(units = 1))\n",
    "\n",
    "# Compiling the RNN\n",
    "regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "83d789ee-3238-43a1-9914-b0d47b15ee1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 157/200\n",
      "52/52 [==============================] - 2s 36ms/step - loss: 9.5322e-04\n",
      "Epoch 158/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 7.8300e-04\n",
      "Epoch 159/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 8.4373e-04\n",
      "Epoch 160/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 7.6648e-04\n",
      "Epoch 161/200\n",
      "52/52 [==============================] - 2s 36ms/step - loss: 6.2104e-04\n",
      "Epoch 162/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 7.0312e-04\n",
      "Epoch 163/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 6.7579e-04\n",
      "Epoch 164/200\n",
      "52/52 [==============================] - 2s 38ms/step - loss: 7.2677e-04\n",
      "Epoch 165/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 8.0693e-04\n",
      "Epoch 166/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 8.7032e-04\n",
      "Epoch 167/200\n",
      "52/52 [==============================] - 2s 38ms/step - loss: 8.3523e-04\n",
      "Epoch 168/200\n",
      "52/52 [==============================] - 2s 38ms/step - loss: 6.7460e-04\n",
      "Epoch 169/200\n",
      "52/52 [==============================] - 2s 36ms/step - loss: 6.1877e-04\n",
      "Epoch 170/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 6.9610e-04\n",
      "Epoch 171/200\n",
      "52/52 [==============================] - 2s 38ms/step - loss: 7.2838e-04\n",
      "Epoch 172/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 8.2315e-04\n",
      "Epoch 173/200\n",
      "52/52 [==============================] - 2s 38ms/step - loss: 7.1831e-04\n",
      "Epoch 174/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 6.8584e-04\n",
      "Epoch 175/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 6.3355e-04\n",
      "Epoch 176/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 6.9466e-04\n",
      "Epoch 177/200\n",
      "52/52 [==============================] - 2s 38ms/step - loss: 8.8064e-04\n",
      "Epoch 178/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 5.9692e-04\n",
      "Epoch 179/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 7.4914e-04\n",
      "Epoch 180/200\n",
      "52/52 [==============================] - 2s 36ms/step - loss: 9.2267e-04\n",
      "Epoch 181/200\n",
      "52/52 [==============================] - 2s 36ms/step - loss: 5.8831e-04\n",
      "Epoch 182/200\n",
      "52/52 [==============================] - 2s 34ms/step - loss: 6.3283e-04\n",
      "Epoch 183/200\n",
      "52/52 [==============================] - 2s 36ms/step - loss: 7.3925e-04\n",
      "Epoch 184/200\n",
      "52/52 [==============================] - 2s 38ms/step - loss: 6.9647e-04\n",
      "Epoch 185/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 7.8920e-04\n",
      "Epoch 186/200\n",
      "52/52 [==============================] - 2s 36ms/step - loss: 6.3316e-04\n",
      "Epoch 187/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 7.2135e-04\n",
      "Epoch 188/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 7.5786e-04\n",
      "Epoch 189/200\n",
      "52/52 [==============================] - 2s 36ms/step - loss: 6.4406e-04\n",
      "Epoch 190/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 6.4613e-04\n",
      "Epoch 191/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 6.8578e-04\n",
      "Epoch 192/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 7.1377e-04\n",
      "Epoch 193/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 6.9000e-04\n",
      "Epoch 194/200\n",
      "52/52 [==============================] - 2s 35ms/step - loss: 8.2548e-04\n",
      "Epoch 195/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 7.4055e-04\n",
      "Epoch 196/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 6.2748e-04\n",
      "Epoch 197/200\n",
      "52/52 [==============================] - 2s 36ms/step - loss: 6.9189e-04\n",
      "Epoch 198/200\n",
      "52/52 [==============================] - 2s 37ms/step - loss: 6.6677e-04\n",
      "Epoch 199/200\n",
      "52/52 [==============================] - 2s 36ms/step - loss: 6.9026e-04\n",
      "Epoch 200/200\n",
      "52/52 [==============================] - 2s 36ms/step - loss: 7.3039e-04\n"
     ]
    }
   ],
   "source": [
    "# Fitting the RNN to the Training set\n",
    "history = regressor.fit(X_train, y_train, epochs = 200, batch_size = 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5d51f4ef-2d24-4d52-9f06-859d826d34f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Loss :  0.0007303872262127697\n"
     ]
    }
   ],
   "source": [
    "print(\"Final Loss : \",history.history['loss'][-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "80422f60-9d3b-4b71-bb83-816be7634d78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the real stock price of 2021\n",
    "dataset_test = pd.read_csv('test.csv')\n",
    "real_stock_price = dataset_test.iloc[:, 1:2].values\n",
    "\n",
    "# Getting the predicted stock price of 2017\n",
    "dataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']), axis = 0)\n",
    "\n",
    "inputs = dataset_total[len(dataset_total) - len(dataset_test) - 80:].values\n",
    "inputs = inputs.reshape(-1,1)\n",
    "inputs = sc.transform(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c5815910-983e-4c87-a054-2e988c6d16fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = []\n",
    "for i in range(80,180):\n",
    "    X_test.append(inputs[i-80:i, 0])\n",
    "    \n",
    "X_test = np.array(X_test)\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c779853f-5fe0-4670-bfef-49d165704835",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_crypto_price = regressor.predict(X_test)\n",
    "predicted_crypto_price = sc.inverse_transform(predicted_crypto_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "baaf435c-f071-4cb4-87d8-15036dd6e036",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualising the results\n",
    "plt.plot(real_stock_price, color = 'red', label = 'Real Bitcoin  Price')\n",
    "plt.plot(predicted_crypto_price, color = 'blue', label = 'Predicted Bitcoin Price')\n",
    "plt.title('Bitcoin  Price Prediction')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Bitcoin Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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