Bitcoin_Price_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bitcoin Price Prediction using LSTM\n",
    "\n",
    "In this project, we aim to predict the future price of Bitcoin using Long Short-Term Memory (LSTM), a type of recurrent neural network (RNN), which is well-suited for time-series data.\n",
    "\n",
    "## Project Overview:\n",
    "\n",
    "- **Data Acquisition**: We collect historical Bitcoin price data from a reliable source such as Yahoo Finance using web scraping techniques.\n",
    "- **Data Preprocessing**: We preprocess the raw data by cleaning, scaling, and structuring it into appropriate input-output pairs for training the LSTM model.\n",
    "- **Model Building**: We construct an LSTM model using the TensorFlow Keras API, comprising input, LSTM, dropout, and dense layers to learn and predict Bitcoin prices based on historical data.\n",
    "- **Model Training**: We train the LSTM model on the prepared data, optimizing it to minimize the mean squared error loss and preventing overfitting using dropout regularization and early stopping.\n",
    "- **Evaluation**: We evaluate the trained model's performance on a separate test set to assess its ability to generalize to unseen data.\n",
    "- **Visualization**: We visualize the predicted Bitcoin prices against the actual prices using Matplotlib, providing insights into the model's performance.\n",
    "- **Prediction for Tomorrow**: Finally, we predict the Bitcoin price for tomorrow using the trained LSTM model, enabling us to forecast future price trends.\n",
    "\n",
    "## Tools and Libraries:\n",
    "\n",
    "- Python: Programming language for data preprocessing, model building, and visualization.\n",
    "- TensorFlow: Deep learning framework for building and training LSTM models.\n",
    "- BeautifulSoup: Python library for web scraping to collect Bitcoin price data.\n",
    "- Pandas: Data manipulation library for handling tabular data efficiently.\n",
    "- Matplotlib: Data visualization library for plotting the predicted vs actual Bitcoin prices.\n",
    "\n",
    "By leveraging LSTM models and deep learning techniques, this project aims to provide valuable insights into Bitcoin price movements and facilitate informed decision-making for investors and traders in the cryptocurrency market.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Required Libraries\n",
    "\n",
    "In this section, we import the necessary libraries for our Bitcoin price prediction model. These include:\n",
    "\n",
    "- `requests` for making HTTP requests to fetch data from the web.\n",
    "- `BeautifulSoup` from the `bs4` package for parsing HTML and extracting data.\n",
    "- `time` for implementing delays in our web scraping process.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting the Current Time\n",
    "\n",
    "In this section, we get the current time in Unix timestamp format (seconds since January 1, 1970). This will be useful when getting data by web scraping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1717785467'"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now = str(int(time.time()))\n",
    "now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fetching Bitcoin Historical Data\n",
    "\n",
    "In this section, we fetch the historical Bitcoin price data from Yahoo Finance. We construct the URL with the desired time period and make an HTTP GET request using the `requests` library. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Request was successful\n"
     ]
    }
   ],
   "source": [
    "url = 'https://finance.yahoo.com/quote/BTC-USD/history/?period1=1585872000&period2=' + now\n",
    "headers = {\n",
    "    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'\n",
    "}\n",
    "response = requests.get(url, headers=headers)\n",
    "\n",
    "# Check if the request was successful\n",
    "if response.status_code == 200:\n",
    "    print('Request was successful')\n",
    "else:\n",
    "    print('Failed to fetch data. Status code:', response.status_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parsing the HTML Response\n",
    "\n",
    "In this section, we use BeautifulSoup to parse the HTML response from Yahoo Finance. By extracting and printing the title of the page, we can confirm that we have successfully fetched and parsed the web page."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Bitcoin USD (BTC-USD) Stock Historical Prices & Data - Yahoo Finance'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soup = BeautifulSoup(response.text,'html.parser')\n",
    "soup.title.text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Viewing the Parsed HTML Content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<!DOCTYPE html>\n",
      "<html class=\"desktop neo-green dock-upscale\" data-color-scheme=\"light\" lang=\"en-US\" theme=\"light\">\n",
      " <head>\n",
      "  <meta charset=\"utf-8\"/>\n",
      "  <meta content=\"width=device-width, initial-scale=1\" name=\"viewport\"/>\n",
      "  <meta content=\"guce.yahoo.com\" name=\"oath:guce:consent-host\"/>\n",
      "  <link href=\"../../../assets/_app/immutable/assets/2.B6ft43yh.css\" rel=\"stylesheet\"/>\n",
      "  <link href=\"../../../assets/_app/immutable/assets/Ads.3RMLh2mX.css\" rel=\"stylesheet\"/>\n",
      "  <link href=\"../../../assets/_app/imm\n"
     ]
    }
   ],
   "source": [
    "print(soup.prettify()[:500])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extracting Table Rows\n",
    "\n",
    "In this section, we extract specific table rows containing Bitcoin price data from the parsed HTML. We use BeautifulSoup's `find_all` method to locate all table rows (`<tr>`) with the specified class attribute.\n",
    "\n",
    "- `records` stores a list of all `<tr>` elements with the class `svelte-ewueuo`.\n",
    "- We print the first five records to verify the extraction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tr class=\"svelte-ewueuo\"><th class=\"svelte-ewueuo\">Date  </th><th class=\"svelte-ewueuo\">Open  </th><th class=\"svelte-ewueuo\">High  </th><th class=\"svelte-ewueuo\">Low  </th><th class=\"svelte-ewueuo\">Close   <span class=\"container svelte-7dju6j\"><div class=\"icon fin-icon primary-icn sz-medium tw-align-text-top svelte-21xhfv\"><!-- HTML_TAG_START --><svg viewbox=\"0 0 24 24\" xmlns=\"http://www.w3.org/2000/svg\"><path d=\"M11 7h2v2h-2zm0 4h2v6h-2zm1-9C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2m0 18c-4.41 0-8-3.59-8-8s3.59-8 8-8 8 3.59 8 8-3.59 8-8 8\"></path></svg><!-- HTML_TAG_END --></div> <div class=\"tooltip al-bottom svelte-7dju6j\" role=\"tooltip\"><div class=\"arrow svelte-7dju6j\"></div> <h3 class=\"title svelte-7dju6j\"></h3> <span class=\"toolTipContent svelte-ewueuo\" slot=\"content\">Close price adjusted for splits. </span></div> </span> </th><th class=\"svelte-ewueuo\">Adj Close   <span class=\"container svelte-7dju6j\"><div class=\"icon fin-icon primary-icn sz-medium tw-align-text-top svelte-21xhfv\"><!-- HTML_TAG_START --><svg viewbox=\"0 0 24 24\" xmlns=\"http://www.w3.org/2000/svg\"><path d=\"M11 7h2v2h-2zm0 4h2v6h-2zm1-9C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2m0 18c-4.41 0-8-3.59-8-8s3.59-8 8-8 8 3.59 8 8-3.59 8-8 8\"></path></svg><!-- HTML_TAG_END --></div> <div class=\"tooltip al-bottom svelte-7dju6j\" role=\"tooltip\"><div class=\"arrow svelte-7dju6j\"></div> <h3 class=\"title svelte-7dju6j\"></h3> <span class=\"toolTipContent svelte-ewueuo\" slot=\"content\">Adjusted close price adjusted for splits and dividend and/or capital gain distributions. </span></div> </span> </th><th class=\"svelte-ewueuo\">Volume  </th></tr>,\n",
       " <tr class=\"svelte-ewueuo\"><td class=\"svelte-ewueuo\">Jun 7, 2024</td> <td class=\"svelte-ewueuo\">70,772.41</td> <td class=\"svelte-ewueuo\">71,907.83</td> <td class=\"svelte-ewueuo\">68,507.26</td> <td class=\"svelte-ewueuo\">69,153.80</td> <td class=\"svelte-ewueuo\">69,153.80</td> <td class=\"svelte-ewueuo\">34,698,813,440</td> </tr>,\n",
       " <tr class=\"svelte-ewueuo\"><td class=\"svelte-ewueuo\">Jun 6, 2024</td> <td class=\"svelte-ewueuo\">71,082.84</td> <td class=\"svelte-ewueuo\">71,625.73</td> <td class=\"svelte-ewueuo\">70,119.13</td> <td class=\"svelte-ewueuo\">70,757.16</td> <td class=\"svelte-ewueuo\">70,757.16</td> <td class=\"svelte-ewueuo\">25,223,152,007</td> </tr>,\n",
       " <tr class=\"svelte-ewueuo\"><td class=\"svelte-ewueuo\">Jun 5, 2024</td> <td class=\"svelte-ewueuo\">70,568.35</td> <td class=\"svelte-ewueuo\">71,735.41</td> <td class=\"svelte-ewueuo\">70,390.71</td> <td class=\"svelte-ewueuo\">71,082.82</td> <td class=\"svelte-ewueuo\">71,082.82</td> <td class=\"svelte-ewueuo\">32,810,771,409</td> </tr>,\n",
       " <tr class=\"svelte-ewueuo\"><td class=\"svelte-ewueuo\">Jun 4, 2024</td> <td class=\"svelte-ewueuo\">68,804.57</td> <td class=\"svelte-ewueuo\">71,047.41</td> <td class=\"svelte-ewueuo\">68,564.64</td> <td class=\"svelte-ewueuo\">70,567.77</td> <td class=\"svelte-ewueuo\">70,567.77</td> <td class=\"svelte-ewueuo\">33,149,696,545</td> </tr>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records = soup.find_all('tr', class_='svelte-ewueuo')\n",
    "records[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing the Extracted Data\n",
    "\n",
    "In this section, we analyze the extracted data by checking the total number of records and estimating the number of years of data available. This helps us understand the scope and coverage of our dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total records count:  1528\n",
      "data of 4.19 years\n"
     ]
    }
   ],
   "source": [
    "print('total records count: ', len(records))\n",
    "print('data of %.2f years' % (len(records)/365))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extracting Dates and Prices\n",
    "\n",
    "In this section, we extract the dates and closing prices from the table rows. We iterate through the extracted records, parsing the necessary data from each row.\n",
    "\n",
    "- `date` list stores the extracted dates.\n",
    "- `price` list stores the extracted closing prices.\n",
    "- We skip the first record if it contains headers.\n",
    "\n",
    "For each record:\n",
    "- We find all `<td>` elements in the row.\n",
    "- We append the text from the first `<td>` element to the `date` list.\n",
    "- We append the text from the fifth `<td>` element to the `price` list.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = []\n",
    "price = []\n",
    "\n",
    "for rec in records[1:]:\n",
    "    tds = rec.find_all('td')\n",
    "    date.append(tds[0].text)\n",
    "    price.append(tds[4].text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Additional Libraries\n",
    "\n",
    "In this section, we import additional libraries that are essential for data manipulation and analysis:\n",
    "\n",
    "- `numpy` \n",
    "- `pandas` \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a DataFrame\n",
    "\n",
    "In this section, we create a Pandas DataFrame from the extracted date and price data. This DataFrame will allow us to perform various data manipulation and analysis tasks efficiently.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jun 7, 2024</td>\n",
       "      <td>69,153.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jun 6, 2024</td>\n",
       "      <td>70,757.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jun 5, 2024</td>\n",
       "      <td>71,082.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jun 4, 2024</td>\n",
       "      <td>70,567.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jun 3, 2024</td>\n",
       "      <td>68,804.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date      price\n",
       "0  Jun 7, 2024  69,153.80\n",
       "1  Jun 6, 2024  70,757.16\n",
       "2  Jun 5, 2024  71,082.82\n",
       "3  Jun 4, 2024  70,567.77\n",
       "4  Jun 3, 2024  68,804.78"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'date':date, 'price':price})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning and Preprocessing\n",
    "\n",
    "In this section, we perform data cleaning and preprocessing on the DataFrame:\n",
    "\n",
    "1. **Cleaning Price Data**: We remove commas from the 'price' column and convert the values to floating-point numbers using the `.str.replace()` and `.astype(float)` methods.\n",
    "\n",
    "2. **Converting Date Data**: We convert the 'date' column to datetime format using the `pd.to_datetime()` function, specifying the format as '%b %d, %Y' to match the date format in the data.\n",
    "\n",
    "3. **Setting Date as Index**: We set the 'date' column as the index of the DataFrame using the `.set_index()` method, ensuring easy access and manipulation of time-series data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-06-07</th>\n",
       "      <td>69153.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-06</th>\n",
       "      <td>70757.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-05</th>\n",
       "      <td>71082.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-04</th>\n",
       "      <td>70567.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-03</th>\n",
       "      <td>68804.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               price\n",
       "date                \n",
       "2024-06-07  69153.80\n",
       "2024-06-06  70757.16\n",
       "2024-06-05  71082.82\n",
       "2024-06-04  70567.77\n",
       "2024-06-03  68804.78"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price'] = df['price'].str.replace(',', '').astype(float)\n",
    "\n",
    "df['date'] = pd.to_datetime(df['date'], format='%b %d, %Y')\n",
    "df.set_index('date', inplace=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Scaling\n",
    "\n",
    "In this section, we scale the 'price' data using Min-Max scaling to normalize it within the range of 0 to 1. This scaling technique is commonly used to standardize numerical features for machine learning models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled_data = scaler.fit_transform(df['price'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.94085464],\n",
       "       [0.96498658],\n",
       "       [0.96988805],\n",
       "       ...,\n",
       "       [0.00224288],\n",
       "       [0.00339276],\n",
       "       [0.00137384]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series Data Preparation\n",
    "\n",
    "In this section, we prepare the time series data for training a machine learning model. We create input-output pairs for a sliding window approach:\n",
    "\n",
    "- We define a `time_step` variable indicating the number of time steps (or lag observations) to use for predicting the next value.\n",
    "- We initialize empty lists `X` and `Y` to store input sequences and corresponding output values.\n",
    "- We loop through the scaled data to create input-output pairs, where each input sequence (`X`) contains `time_step` consecutive values, and the corresponding output (`Y`) is the next value after the sequence.\n",
    "- Finally, we convert the lists to numpy arrays and reshape `X` to match the input shape required by the model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_step = 10\n",
    "X, Y = [], []\n",
    "\n",
    "for i in range(len(scaled_data) - time_step - 1):\n",
    "    X.append(scaled_data[i:(i + time_step), 0])\n",
    "    Y.append(scaled_data[i + time_step, 0])\n",
    "    \n",
    "X, Y = np.array(X), np.array(Y)\n",
    "X = X.reshape(X.shape[0], X.shape[1], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data of previous 10 days :\n",
      " [[0.94085464]\n",
      " [0.96498658]\n",
      " [0.96988805]\n",
      " [0.9621361 ]\n",
      " [0.93560159]\n",
      " [0.91975032]\n",
      " [0.91907815]\n",
      " [0.91583424]\n",
      " [0.92898237]\n",
      " [0.91713885]]\n",
      "\n",
      "label (btc price of today) :\n",
      " 0.9279473231971818\n"
     ]
    }
   ],
   "source": [
    "print('data of previous 10 days :\\n',X[0])\n",
    "print('\\nlabel (btc price of today) :\\n',Y[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the Data into Train, Validation, and Test Sets\n",
    "\n",
    "In this section, we split the prepared data into train, validation, and test sets:\n",
    "\n",
    "1. **Train Set**: The first 70% of the data is allocated for training.\n",
    "2. **Validation Set**: The next 10% of the data is allocated for validation to monitor the model's performance during training.\n",
    "3. **Test Set**: The remaining 20% of the data is allocated for testing to evaluate the final model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train set len:  1061\n",
      "validation set len:  151\n",
      "test set len:  304\n"
     ]
    }
   ],
   "source": [
    "split_point1 = int(0.7 * len(X))\n",
    "split_point2 = int(0.8 * len(X))\n",
    "\n",
    "x_train = X[:split_point1]\n",
    "y_train = Y[:split_point1]\n",
    "\n",
    "x_val = X[split_point1:split_point2]\n",
    "y_val = Y[split_point1:split_point2]\n",
    "\n",
    "x_test = X[split_point2:]\n",
    "y_test = Y[split_point2:]\n",
    "\n",
    "print('train set len: ', len(x_train))\n",
    "print('validation set len: ', len(x_val))\n",
    "print('test set len: ', len(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the LSTM Model\n",
    "\n",
    "In this section, we train the LSTM model using the prepared train and validation sets:\n",
    "\n",
    "- We define the LSTM model architecture with input shape `(time_step, 1)` and layers comprising LSTM, Dropout, and Dense layers.\n",
    "- The model is compiled with the Adam optimizer and mean squared error loss function.\n",
    "- We set up early stopping to monitor the validation loss and restore the best weights when training stops if the validation loss does not decrease for 5 consecutive epochs (`patience=5`).\n",
    "- The model is trained using the `fit` method with the training data (`x_train`, `y_train`) and validation data (`x_val`, `y_val`), with a batch size of 32 and for a maximum of 50 epochs.\n",
    "\n",
    "The training history (`hist`) contains information about the training and validation loss over epochs.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - loss: 0.0696 - val_loss: 0.0051\n",
      "Epoch 2/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0063 - val_loss: 0.0045\n",
      "Epoch 3/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0037 - val_loss: 0.0043\n",
      "Epoch 4/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0036 - val_loss: 0.0043\n",
      "Epoch 5/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0030 - val_loss: 0.0048\n",
      "Epoch 6/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0031 - val_loss: 0.0041\n",
      "Epoch 7/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0027 - val_loss: 0.0041\n",
      "Epoch 8/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0026 - val_loss: 0.0042\n",
      "Epoch 9/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0026 - val_loss: 0.0043\n",
      "Epoch 10/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0022 - val_loss: 0.0056\n",
      "Epoch 11/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0030 - val_loss: 0.0041\n",
      "Epoch 12/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0020 - val_loss: 0.0041\n",
      "Epoch 13/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0024 - val_loss: 0.0051\n",
      "Epoch 14/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0025 - val_loss: 0.0038\n",
      "Epoch 15/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0019 - val_loss: 0.0038\n",
      "Epoch 16/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0018 - val_loss: 0.0035\n",
      "Epoch 17/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0022 - val_loss: 0.0035\n",
      "Epoch 18/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0016 - val_loss: 0.0036\n",
      "Epoch 19/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0024 - val_loss: 0.0033\n",
      "Epoch 20/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0018 - val_loss: 0.0033\n",
      "Epoch 21/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0018 - val_loss: 0.0032\n",
      "Epoch 22/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0020 - val_loss: 0.0034\n",
      "Epoch 23/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0015 - val_loss: 0.0034\n",
      "Epoch 24/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0016 - val_loss: 0.0030\n",
      "Epoch 25/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0014 - val_loss: 0.0032\n",
      "Epoch 26/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0014 - val_loss: 0.0034\n",
      "Epoch 27/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0015 - val_loss: 0.0028\n",
      "Epoch 28/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0015 - val_loss: 0.0035\n",
      "Epoch 29/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0014 - val_loss: 0.0028\n",
      "Epoch 30/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0013 - val_loss: 0.0027\n",
      "Epoch 31/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0016 - val_loss: 0.0026\n",
      "Epoch 32/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0014 - val_loss: 0.0040\n",
      "Epoch 33/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0017 - val_loss: 0.0029\n",
      "Epoch 34/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0012 - val_loss: 0.0025\n",
      "Epoch 35/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0013 - val_loss: 0.0025\n",
      "Epoch 36/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0011 - val_loss: 0.0025\n",
      "Epoch 37/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0012 - val_loss: 0.0024\n",
      "Epoch 38/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0011 - val_loss: 0.0023\n",
      "Epoch 39/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0014 - val_loss: 0.0023\n",
      "Epoch 40/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0010 - val_loss: 0.0023\n",
      "Epoch 41/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0015 - val_loss: 0.0023\n",
      "Epoch 42/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0012 - val_loss: 0.0023\n",
      "Epoch 43/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0011 - val_loss: 0.0024\n",
      "Epoch 44/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0012 - val_loss: 0.0024\n",
      "Epoch 45/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0013 - val_loss: 0.0022\n",
      "Epoch 46/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0013 - val_loss: 0.0021\n",
      "Epoch 47/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 9.7011e-04 - val_loss: 0.0021\n",
      "Epoch 48/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0013 - val_loss: 0.0022\n",
      "Epoch 49/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0011 - val_loss: 0.0021\n",
      "Epoch 50/50\n",
      "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0012 - val_loss: 0.0021\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense, Input, Dropout\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Input(shape=(time_step, 1)))\n",
    "model.add(LSTM(50, return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(50, return_sequences=False))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(25))\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
    "\n",
    "hist = model.fit(x_train, y_train, batch_size=32, epochs=50, validation_data=(x_val, y_val), callbacks=[early_stopping])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing Predicted vs Actual Prices\n",
    "\n",
    "In this section, we visualize the predicted and actual prices using the trained LSTM model:\n",
    "\n",
    "- We make predictions (`pred`) on the entire dataset (`X`) using the trained model.\n",
    "\n",
    "The visualization allows us to visually compare the predicted prices with the actual prices, assessing the performance of the LSTM model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict(X)\n",
    "pred = scaler.inverse_transform(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.plot(df.index[:len(pred)], pred, label='Predicted Price')\n",
    "plt.plot(df.index, df['price'], label='Actual Price')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating Predictions on Test Set\n",
    "\n",
    "In this section, we evaluate the predictions made by the LSTM model on the test set. The visualization allows us to assess how well the LSTM model performs in predicting Bitcoin prices on unseen data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 5.8989e-04\n",
      "Test Loss: 0.0002823187387548387\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = model.predict(x_test)\n",
    "test_loss = model.evaluate(x_test, y_test)\n",
    "\n",
    "print(\"Test Loss:\", test_loss)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(predictions, label='Predicted')\n",
    "plt.plot(y_test, label='Actual')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Bitcoin Price for Tomorrow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "predicted bitcoin price for tomorrow: 66454.773\n"
     ]
    }
   ],
   "source": [
    "tomorrow_pred = model.predict(X[[0]])\n",
    "price = scaler.inverse_transform(tomorrow_pred)\n",
    "\n",
    "print('predicted bitcoin price for tomorrow: %.3f' % float(price.squeeze()))"
   ]
  }
 ],
 "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}