Bitcoin_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "Zaqs1_CZIr9R"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, LSTM\n",
    "import math\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "-KFkSkY1rKBH"
   },
   "outputs": [],
   "source": [
    "data=pd.read_excel(\"BTC2.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "R7-4VIy65NYG"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tarih</th>\n",
       "      <th>Açılış</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-05-06</td>\n",
       "      <td>3.3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-05-07</td>\n",
       "      <td>3.5</td>\n",
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       "      <th>2</th>\n",
       "      <td>2011-05-08</td>\n",
       "      <td>3.6</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-05-09</td>\n",
       "      <td>3.9</td>\n",
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       "      <th>4</th>\n",
       "      <td>2011-05-10</td>\n",
       "      <td>3.8</td>\n",
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       "      <td>2021-04-15</td>\n",
       "      <td>62978.6</td>\n",
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       "      <th>3633</th>\n",
       "      <td>2021-04-16</td>\n",
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       "    <tr>\n",
       "      <th>3634</th>\n",
       "      <td>2021-04-17</td>\n",
       "      <td>61358.3</td>\n",
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       "    <tr>\n",
       "      <th>3635</th>\n",
       "      <td>2021-04-18</td>\n",
       "      <td>60043.2</td>\n",
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       "    <tr>\n",
       "      <th>3636</th>\n",
       "      <td>2021-04-19</td>\n",
       "      <td>56208.1</td>\n",
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       "<p>3637 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "          Tarih   Açılış\n",
       "0    2011-05-06      3.3\n",
       "1    2011-05-07      3.5\n",
       "2    2011-05-08      3.6\n",
       "3    2011-05-09      3.9\n",
       "4    2011-05-10      3.8\n",
       "...         ...      ...\n",
       "3632 2021-04-15  62978.6\n",
       "3633 2021-04-16  63211.6\n",
       "3634 2021-04-17  61358.3\n",
       "3635 2021-04-18  60043.2\n",
       "3636 2021-04-19  56208.1\n",
       "\n",
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  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "4uRzAZ0OrM7C"
   },
   "outputs": [],
   "source": [
    "data=data.set_index('Tarih')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Açılış</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tarih</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-05-06</th>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-07</th>\n",
       "      <td>3.5</td>\n",
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       "      <td>3.6</td>\n",
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       "    <tr>\n",
       "      <th>2011-05-09</th>\n",
       "      <td>3.9</td>\n",
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       "    <tr>\n",
       "      <th>2011-05-10</th>\n",
       "      <td>3.8</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-15</th>\n",
       "      <td>62978.6</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-16</th>\n",
       "      <td>63211.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-17</th>\n",
       "      <td>61358.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-18</th>\n",
       "      <td>60043.2</td>\n",
       "    </tr>\n",
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       "      <th>2021-04-19</th>\n",
       "      <td>56208.1</td>\n",
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      "text/plain": [
       "             Açılış\n",
       "Tarih              \n",
       "2011-05-06      3.3\n",
       "2011-05-07      3.5\n",
       "2011-05-08      3.6\n",
       "2011-05-09      3.9\n",
       "2011-05-10      3.8\n",
       "...             ...\n",
       "2021-04-15  62978.6\n",
       "2021-04-16  63211.6\n",
       "2021-04-17  61358.3\n",
       "2021-04-18  60043.2\n",
       "2021-04-19  56208.1\n",
       "\n",
       "[3637 rows x 1 columns]"
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  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "cNMyjZYrwjxW"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "72/72 [==============================] - 20s 225ms/step - loss: 9.0355e-05 - accuracy: 3.0365e-05 - val_loss: 3.6567e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/100\n",
      "72/72 [==============================] - 15s 205ms/step - loss: 4.5532e-06 - accuracy: 6.6642e-04 - val_loss: 2.5107e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 3/100\n",
      "72/72 [==============================] - 13s 185ms/step - loss: 4.1805e-06 - accuracy: 2.0550e-04 - val_loss: 5.0165e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 4/100\n",
      "72/72 [==============================] - 13s 180ms/step - loss: 4.1957e-06 - accuracy: 1.7817e-04 - val_loss: 5.2269e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 5/100\n",
      "72/72 [==============================] - 13s 178ms/step - loss: 6.0835e-06 - accuracy: 4.7048e-04 - val_loss: 2.0675e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 6/100\n",
      "72/72 [==============================] - 13s 180ms/step - loss: 5.1048e-06 - accuracy: 1.8708e-04 - val_loss: 1.5960e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 7/100\n",
      "72/72 [==============================] - 13s 185ms/step - loss: 3.4053e-06 - accuracy: 1.2045e-04 - val_loss: 1.6503e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 8/100\n",
      "72/72 [==============================] - 14s 198ms/step - loss: 3.2557e-06 - accuracy: 8.3306e-04 - val_loss: 1.4376e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 9/100\n",
      "72/72 [==============================] - 15s 204ms/step - loss: 3.0745e-06 - accuracy: 1.4424e-04 - val_loss: 2.9380e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 10/100\n",
      "72/72 [==============================] - 14s 195ms/step - loss: 4.5847e-06 - accuracy: 1.8046e-05 - val_loss: 1.4048e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 11/100\n",
      "72/72 [==============================] - 13s 178ms/step - loss: 4.3117e-06 - accuracy: 9.7918e-05 - val_loss: 1.2797e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 12/100\n",
      "72/72 [==============================] - 14s 190ms/step - loss: 4.4851e-06 - accuracy: 7.5847e-04 - val_loss: 1.2351e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 13/100\n",
      "72/72 [==============================] - 13s 183ms/step - loss: 3.2203e-06 - accuracy: 1.2045e-04 - val_loss: 2.0134e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 14/100\n",
      "72/72 [==============================] - 16s 225ms/step - loss: 3.3063e-06 - accuracy: 9.7918e-05 - val_loss: 1.1847e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 15/100\n",
      "72/72 [==============================] - 16s 217ms/step - loss: 3.6236e-06 - accuracy: 1.0530e-04 - val_loss: 1.1617e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 16/100\n",
      "72/72 [==============================] - 15s 204ms/step - loss: 4.2533e-06 - accuracy: 5.9070e-04 - val_loss: 1.1452e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 17/100\n",
      "72/72 [==============================] - 15s 210ms/step - loss: 3.5936e-06 - accuracy: 1.0530e-04 - val_loss: 1.6418e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 18/100\n",
      "72/72 [==============================] - 15s 203ms/step - loss: 2.2390e-06 - accuracy: 4.3689e-04 - val_loss: 1.1592e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 19/100\n",
      "72/72 [==============================] - 15s 204ms/step - loss: 2.9744e-06 - accuracy: 1.7817e-04 - val_loss: 1.1466e-04 - val_accuracy: 0.0000e+00\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "dataset = data.values\n",
    "training_data_len = math.ceil(len(dataset) * .8)\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "scaled_data = scaler.fit_transform(dataset)\n",
    "\n",
    " \n",
    "train_data = scaled_data[0:training_data_len, :]\n",
    "\n",
    "\n",
    "x_train = []\n",
    "y_train = []\n",
    "\n",
    "for i in range(60, len(train_data)):\n",
    "    x_train.append(train_data[i-60:i, 0])\n",
    "    y_train.append(train_data[i, 0])\n",
    "  \n",
    "\n",
    "x_train, y_train = np.array(x_train), np.array(y_train)\n",
    "\n",
    "x_train = np.reshape(x_train,(x_train.shape[0],x_train.shape[1],1))\n",
    "tf.keras.callbacks.EarlyStopping(\n",
    "    'val_loss',\n",
    "                          min_delta = 0,\n",
    "                          patience = 3,\n",
    "                          verbose = 1,\n",
    "                          restore_best_weights = True)\n",
    "\n",
    "es_callback = keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)\n",
    "\n",
    "# Model\n",
    "model =Sequential() \n",
    "model.add(LSTM(256, return_sequences=True, input_shape =(x_train.shape[1],1)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(128, return_sequences=False))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer='adam', loss='mean_squared_error',metrics =[\"accuracy\"])\n",
    "model.fit(x_train,y_train,epochs=100,validation_split=0.2,callbacks=[es_callback])\n",
    "\n",
    "test_data=scaled_data[training_data_len - 60:,:]\n",
    "x_test= []\n",
    "y_test= dataset[training_data_len:,:]\n",
    "for y in range(60,len(test_data)):\n",
    "  x_test.append(test_data[y-60:y,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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9922831321556826\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(x_test)\n",
    "predictions = scaler.inverse_transform(predictions)\n",
    "print(r2_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "aZLuSNWxq744"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-12-6f5fcde5fa0a>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  valid['Tahminler']= predictions\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train = data[:training_data_len]\n",
    "valid = data[training_data_len:]\n",
    "valid['Tahminler']= predictions\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.title('model')\n",
    "plt.xlabel('Tarih', fontsize=18)\n",
    "plt.ylabel('Açılış Fiyatları', fontsize=18)\n",
    "plt.plot(data['Açılış'],color=\"purple\")\n",
    "plt.plot(valid[['Açılış','Tahminler']])\n",
    "plt.legend(['Eğitim','Değer','Tahminler'],loc='lower right')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "XdwxADABq_kn"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-13-f4cd7592530c>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  valid['Tahminler']= predictions\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ded0d118b0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train = data[:training_data_len]\n",
    "valid = data[training_data_len:]\n",
    "valid['Tahminler']= predictions\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.title('model')\n",
    "plt.xlabel('Tarih', fontsize=18)\n",
    "plt.ylabel('Açılış Fiyatları', fontsize=18)\n",
    "plt.plot(valid['Açılış'])\n",
    "plt.plot(valid[[\"Tahminler\"]],color=\"Red\")\n",
    "plt.legend(['Değerler','Tahminler'],loc='lower right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "dmN9MKj1rBen"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction for next day :[[61387.773]]\n"
     ]
    }
   ],
   "source": [
    "dataset = data.values\n",
    "training_data_len = math.ceil(len(dataset) * .8)\n",
    "train_dataa= dataset[0:training_data_len, :]\n",
    "test_dataa=dataset[training_data_len - 60:,:]\n",
    "train_dataa=pd.DataFrame(train_dataa)\n",
    "train_dataa.rename(columns={0:'Açılış'}, inplace=True)\n",
    "test_dataa=pd.DataFrame(test_dataa)\n",
    "test_dataa.rename(columns={0:'Açılış'}, inplace=True)\n",
    "total_dataset=pd.concat((train_dataa[\"Açılış\"],test_dataa[\"Açılış\"]),axis=0)\n",
    "\n",
    "model_inputs=total_dataset[len(total_dataset)-len(test_data)-60:].values\n",
    "model_inputs=model_inputs.reshape(-1,1)\n",
    "model_inputs=scaler.transform(model_inputs)\n",
    "\n",
    "\n",
    "\n",
    "real_data=[model_inputs[len(model_inputs) +1-60:len(model_inputs+1),0]]\n",
    "real_data=np.array(real_data)\n",
    "real_data=np.reshape(real_data,(real_data.shape[0],real_data.shape[1],1))\n",
    "\n",
    "prediction=model.predict(real_data)\n",
    "prediction=scaler.inverse_transform(prediction)\n",
    "\n",
    "print(f\"prediction for next day :{prediction}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "76wiyt8La51Z"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Pi (Deneme)",
   "private_outputs": true,
   "provenance": []
  },
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}