diff --git "a/Obtain crypto price.ipynb" "b/Obtain crypto price.ipynb"
new file mode 100644--- /dev/null
+++ "b/Obtain crypto price.ipynb"
@@ -0,0 +1,832 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e5d444d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cryptocmd import CmcScraper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "dff71998",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scraper = CmcScraper(\"BTC\")\n",
+    "headers, data = scraper.get_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e49bacac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Market Cap']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(headers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "01a804c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "dd50e2fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "28-06-2023\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data[0][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3dea5eb6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start_date = datetime.strptime(\"01-01-2022\", r\"%d-%m-%Y\")\n",
+    "end_date = datetime.strptime(\"31-12-2022\", r\"%d-%m-%Y\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "b2f02394",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bitcoin_price_list = []\n",
+    "date_list = []\n",
+    "\n",
+    "for record in data:\n",
+    "    date = datetime.strptime(record[0], r\"%d-%m-%Y\")\n",
+    "    if(date >= start_date and date <= end_date):\n",
+    "        date_list.append(date)\n",
+    "        bitcoin_price_list.append(record[1])\n",
+    "\n",
+    "        \n",
+    "date_list.reverse()\n",
+    "bitcoin_price_list.reverse()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "174de348",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "9d37a363",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(date_list, bitcoin_price_list)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "349f4335",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_size = int(len(bitcoin_price_list) * 0.75)\n",
+    "bitcoin_train, bitcoin_test = bitcoin_price_list[:train_size], bitcoin_price_list[train_size:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "d8d8a562",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from statsmodels.tsa.arima.model import ARIMA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "8dcbdfcd",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=19607.325454, expected=19431.104946\n",
+      "predicted=19539.559515, expected=19311.848708\n",
+      "predicted=19331.813520, expected=19044.067850\n",
+      "predicted=19103.313161, expected=19623.584689\n",
+      "predicted=19653.221452, expected=20335.899579\n",
+      "predicted=20425.017613, expected=20161.038438\n",
+      "predicted=20256.273363, expected=19957.559080\n",
+      "predicted=20056.401614, expected=19546.328838\n",
+      "predicted=19514.302446, expected=19417.479411\n",
+      "predicted=19458.938428, expected=19446.416225\n",
+      "predicted=19438.070582, expected=19138.999877\n",
+      "predicted=19228.289530, expected=19052.645786\n",
+      "predicted=19084.118005, expected=19156.966613\n",
+      "predicted=19226.203955, expected=19382.533972\n",
+      "predicted=19423.958917, expected=19185.437304\n",
+      "predicted=19280.725080, expected=19068.913560\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=19113.713441, expected=19268.562102\n",
+      "predicted=19324.271179, expected=19550.466743\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=19588.544258, expected=19335.026441\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=19423.415524, expected=19138.085057\n",
+      "predicted=19182.396723, expected=19053.203046\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=19085.645927, expected=19172.380609\n",
+      "predicted=19199.419394, expected=19207.734651\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=19272.270280, expected=19567.769580\n",
+      "predicted=19635.396423, expected=19344.964420\n",
+      "predicted=19399.397713, expected=20092.237188\n",
+      "predicted=20178.022491, expected=20772.803027\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=20801.216005, expected=20287.956095\n",
+      "predicted=20397.848293, expected=20595.103842\n",
+      "predicted=20637.595298, expected=20817.982252\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=20801.599452, expected=20633.696071\n",
+      "predicted=20682.312892, expected=20494.897623\n",
+      "predicted=20524.291022, expected=20482.959694\n",
+      "predicted=20494.414650, expected=20162.689228\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=20175.121642, expected=20208.769498\n",
+      "predicted=20250.169057, expected=21144.831466\n",
+      "predicted=21179.372964, expected=21285.056664\n",
+      "predicted=21357.231426, expected=20924.620484\n",
+      "predicted=21024.692877, expected=20600.672747\n",
+      "predicted=20596.645951, expected=18543.761250\n",
+      "predicted=18478.667244, expected=15883.158227\n",
+      "predicted=15769.965258, expected=17583.252431\n",
+      "predicted=17546.601744, expected=17036.875408\n",
+      "predicted=16992.424248, expected=16799.722291\n",
+      "predicted=17195.576581, expected=16352.028563\n",
+      "predicted=16302.988357, expected=16617.484738\n",
+      "predicted=16813.001102, expected=16884.341188\n",
+      "predicted=16827.629088, expected=16670.426756\n",
+      "predicted=16914.922077, expected=16687.911575\n",
+      "predicted=16657.974924, expected=16696.219905\n",
+      "predicted=16866.324547, expected=16712.920458\n",
+      "predicted=16662.098388, expected=16291.223800\n",
+      "predicted=16458.957081, expected=15782.301231\n",
+      "predicted=15714.874694, expected=16195.588670\n",
+      "predicted=16339.474488, expected=16611.637672\n",
+      "predicted=16548.679421, expected=16602.269516\n",
+      "predicted=16830.409215, expected=16521.577025\n",
+      "predicted=16483.134965, expected=16463.883172\n",
+      "predicted=16612.483559, expected=16440.222088\n",
+      "predicted=16355.794859, expected=16217.639904\n",
+      "predicted=16366.882131, expected=16445.477489\n",
+      "predicted=16385.059200, expected=17168.002138\n",
+      "predicted=17328.089534, expected=16968.683261\n",
+      "predicted=16928.325978, expected=17090.098485\n",
+      "predicted=17289.439897, expected=16908.170477\n",
+      "predicted=16783.659901, expected=17128.894080\n",
+      "predicted=17294.537313, expected=16975.239124\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=16857.296614, expected=17089.506270\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=17282.241417, expected=16847.350250\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=16723.143444, expected=17232.148003\n",
+      "predicted=17427.324256, expected=17134.221365\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=17003.046801, expected=17129.711333\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=17351.590324, expected=17102.500649\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=16962.592568, expected=17206.440484\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=17401.905293, expected=17782.066878\n",
+      "predicted=17663.331601, expected=17813.644123\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=18021.678889, expected=17364.546443\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=17270.145970, expected=16646.982567\n",
+      "predicted=16792.392317, expected=16795.609226\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=16633.835849, expected=16759.040927\n",
+      "predicted=16900.014564, expected=16441.786800\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=16375.201096, expected=16904.527354\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=17094.668855, expected=16818.380288\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=16711.954318, expected=16829.643586\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=17051.054581, expected=16796.976620\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=16678.890822, expected=16847.505364\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=17028.401729, expected=16842.249311\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=16805.146331, expected=16919.291650\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=17026.309792, expected=16716.400221\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=16650.544419, expected=16552.322491\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted=16698.758954, expected=16641.329824\n",
+      "predicted=16564.978027, expected=16603.674703\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = [x for x in bitcoin_train]\n",
+    "predictions = []\n",
+    "for t in range(len(bitcoin_test)):\n",
+    "    model = ARIMA(history, order=(3, 0, 5))\n",
+    "    model_fit = model.fit()\n",
+    "    output = model_fit.forecast()\n",
+    "    y_hat = output[0]\n",
+    "    predictions.append(y_hat)\n",
+    "    obs = bitcoin_test[t]\n",
+    "    history.append(obs) # this is very important, to include the next date's data into historical data to make prediction\n",
+    "    print('predicted=%f, expected=%f' % (y_hat, obs))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "1c0ba1c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from math import sqrt\n",
+    "from sklearn.metrics import mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "41d82249",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test RMSE: 518.410\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse = sqrt(mean_squared_error(bitcoin_test, predictions))\n",
+    "print('Test RMSE: %.3f' % rmse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "533ddcee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(bitcoin_test)\n",
+    "plt.plot(predictions, color='red')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "68c765a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get previous day's opening price\n",
+    "bitcoin_test_previous_price = bitcoin_test.copy()[:-1]\n",
+    "bitcoin_test_previous_price.insert(0, bitcoin_train[-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "f944d6cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bitcoin_true_label = []\n",
+    "for i, _ in enumerate(bitcoin_test):\n",
+    "    if(bitcoin_test[i] > bitcoin_test_previous_price[i]):\n",
+    "        bitcoin_true_label.append(\"Increase\")\n",
+    "    else:\n",
+    "        bitcoin_true_label.append(\"Decrease\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "249518dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bitcoin_predict_label = []\n",
+    "for i, _ in enumerate(predictions):\n",
+    "    if(predictions[i] > bitcoin_test_previous_price[i]):\n",
+    "        bitcoin_predict_label.append(\"Increase\")\n",
+    "    else:\n",
+    "        bitcoin_predict_label.append(\"Decrease\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "e6841185",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import precision_recall_fscore_support\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.metrics import classification_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "ef984d67",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "    Decrease       0.53      0.33      0.40        52\n",
+      "    Increase       0.42      0.62      0.50        40\n",
+      "\n",
+      "    accuracy                           0.46        92\n",
+      "   macro avg       0.47      0.48      0.45        92\n",
+      "weighted avg       0.48      0.46      0.45        92\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(bitcoin_true_label, bitcoin_predict_label))"
+   ]
+  }
+ ],
+ "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}