commit 57a0ae1739621f302bf0d06dcfa0970dfe37d73b
Author: harivansh-afk <73809867+harivansh-afk@users.noreply.github.com>
Date: Fri Nov 1 16:13:38 2024 -0400
Base files
diff --git a/3+Crypto_Price_Prediction.ipynb b/3+Crypto_Price_Prediction.ipynb
new file mode 100644
index 0000000..d933a18
--- /dev/null
+++ b/3+Crypto_Price_Prediction.ipynb
@@ -0,0 +1,2285 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "id": "PLZl5KnqIGQ8"
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import sklearn\n",
+ "import matplotlib.pyplot as plt\n",
+ "import sys \n",
+ "import seaborn as sns\n",
+ "\n",
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')\n",
+ "\n",
+ "%matplotlib inline\n",
+ "import plotly.graph_objs as go\n",
+ "#import plotly.plotly as py\n",
+ "import datetime as dt\n",
+ "import matplotlib.dates as mdates"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "id": "RN88EZ6yJYDd"
+ },
+ "outputs": [
+ {
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'tensorflow.python'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32mc:\\Users\\rathi\\OneDrive\\Documents\\-\\Coding\\PYTHON\\CRYPTO PREDICTION\\3+Crypto_Price_Prediction.ipynb Cell 3\u001b[0m in \u001b[0;36m1\n\u001b[1;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mkeras\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmodels\u001b[39;00m \u001b[39mimport\u001b[39;00m Sequential\n\u001b[0;32m 2\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mkeras\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mlayers\u001b[39;00m \u001b[39mimport\u001b[39;00m Dense, LSTM, Dropout, GRU\n\u001b[0;32m 3\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mkeras\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mlayers\u001b[39;00m \u001b[39mimport\u001b[39;00m \u001b[39m*\u001b[39m\n",
+ "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\__init__.py:20\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39m# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[39m#\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[39m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[39m# limitations under the License.\u001b[39;00m\n\u001b[0;32m 14\u001b[0m \u001b[39m# ==============================================================================\u001b[39;00m\n\u001b[0;32m 15\u001b[0m \u001b[39m\"\"\"Implementation of the Keras API, the high-level API of TensorFlow.\u001b[39;00m\n\u001b[0;32m 16\u001b[0m \n\u001b[0;32m 17\u001b[0m \u001b[39mDetailed documentation and user guides are available at\u001b[39;00m\n\u001b[0;32m 18\u001b[0m \u001b[39m[keras.io](https://keras.io).\u001b[39;00m\n\u001b[0;32m 19\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m---> 20\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mkeras\u001b[39;00m \u001b[39mimport\u001b[39;00m distribute\n\u001b[0;32m 21\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mkeras\u001b[39;00m \u001b[39mimport\u001b[39;00m models\n\u001b[0;32m 22\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mkeras\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mengine\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39minput_layer\u001b[39;00m \u001b[39mimport\u001b[39;00m Input\n",
+ "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\distribute\\__init__.py:18\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39m# Copyright 2019 The TensorFlow Authors. All Rights Reserved.\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[39m#\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[39m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[39m# limitations under the License.\u001b[39;00m\n\u001b[0;32m 14\u001b[0m \u001b[39m# ==============================================================================\u001b[39;00m\n\u001b[0;32m 15\u001b[0m \u001b[39m\"\"\"Keras' Distribution Strategy library.\"\"\"\u001b[39;00m\n\u001b[1;32m---> 18\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mkeras\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mdistribute\u001b[39;00m \u001b[39mimport\u001b[39;00m sidecar_evaluator\n",
+ "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\distribute\\sidecar_evaluator.py:17\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39m# Copyright 2020 The TensorFlow Authors. All Rights Reserved.\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[39m#\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[39m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[39m# limitations under the License.\u001b[39;00m\n\u001b[0;32m 14\u001b[0m \u001b[39m# ==============================================================================\u001b[39;00m\n\u001b[0;32m 15\u001b[0m \u001b[39m\"\"\"Python module for evaluation loop.\"\"\"\u001b[39;00m\n\u001b[1;32m---> 17\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtensorflow\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mcompat\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mv2\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mtf\u001b[39;00m\n\u001b[0;32m 19\u001b[0m \u001b[39m# isort: off\u001b[39;00m\n\u001b[0;32m 20\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorflow\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpython\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mplatform\u001b[39;00m \u001b[39mimport\u001b[39;00m tf_logging \u001b[39mas\u001b[39;00m logging\n",
+ "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\tensorflow\\__init__.py:37\u001b[0m\n\u001b[0;32m 34\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39msys\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39m_sys\u001b[39;00m\n\u001b[0;32m 35\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtyping\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39m_typing\u001b[39;00m\n\u001b[1;32m---> 37\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorflow\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpython\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mtools\u001b[39;00m \u001b[39mimport\u001b[39;00m module_util \u001b[39mas\u001b[39;00m _module_util\n\u001b[0;32m 38\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorflow\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpython\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mutil\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mlazy_loader\u001b[39;00m \u001b[39mimport\u001b[39;00m LazyLoader \u001b[39mas\u001b[39;00m _LazyLoader\n\u001b[0;32m 40\u001b[0m \u001b[39m# Make sure code inside the TensorFlow codebase can use tf2.enabled() at import.\u001b[39;00m\n",
+ "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow.python'"
+ ]
+ }
+ ],
+ "source": [
+ "from keras.models import Sequential\n",
+ "from keras.layers import Dense, LSTM, Dropout, GRU\n",
+ "from keras.layers import *\n",
+ "from sklearn.preprocessing import MinMaxScaler\n",
+ "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from keras.callbacks import EarlyStopping\n",
+ "from tensorflow.keras.optimizers import Adam, SGD"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "background_save": true,
+ "base_uri": "https://localhost:8080/",
+ "height": 38,
+ "resources": {
+ "http://localhost:8080/nbextensions/google.colab/files.js": {
+ "data": 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",
+ "headers": [
+ [
+ "content-type",
+ "application/javascript"
+ ]
+ ],
+ "ok": true,
+ "status": 200,
+ "status_text": ""
+ }
+ }
+ },
+ "id": "sOGCxI5MIHJ8",
+ "outputId": "2a391b7b-7b06-4c09-daaa-dfe79a178c84"
+ },
+ "outputs": [],
+ "source": [
+ "# from google.colab import files\n",
+ "# uploaded = files.upload()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "PLXtatQdIKkr"
+ },
+ "outputs": [],
+ "source": [
+ "# import io\n",
+ "# df = pd.read_csv(io.StringIO(uploaded['crypto-markets.csv'].decode('utf-8')))\n",
+ "\n",
+ "df = pd.read_csv(\"crypto-markets.csv\")\n",
+ "\n",
+ "data_copy = df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 206
+ },
+ "id": "vFFnVybLILE8",
+ "outputId": "5bf743d9-a5cb-4184-dd52-0a346bde79f2"
+ },
+ "outputs": [],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "TDuDU-SlIW8s",
+ "outputId": "b5dab737-bce6-4822-c2b5-053c6bc68a26"
+ },
+ "outputs": [],
+ "source": [
+ "df.shape #total number of rows and columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "rSQy0La6Iaez",
+ "outputId": "efe86655-27e4-4773-a87a-523435d68318"
+ },
+ "outputs": [],
+ "source": [
+ "df.isnull().sum() #total of null values column wise "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 300
+ },
+ "id": "HzaCeYB_Ia_j",
+ "outputId": "90fe7dea-0df1-48e2-eb85-e2417ff927d9"
+ },
+ "outputs": [],
+ "source": [
+ "df.describe() #statistical readings of all the columns "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "t2R1q-FAIejT",
+ "outputId": "aebb2a25-de90-4d15-cfe2-011325915a20"
+ },
+ "outputs": [],
+ "source": [
+ "df.info() #info on all the columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "xdF9qRVBM82y"
+ },
+ "outputs": [],
+ "source": [
+ "data = df #copy of dataframe\n",
+ "\n",
+ "#set up a column with an index so I can only sample 1/10th of the dataset \n",
+ "data[\"row_id\"]= range(1, len(data) + 1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 206
+ },
+ "id": "kEwAdAFzNGfa",
+ "outputId": "47586bab-8683-4edf-8f21-475aed383a5a"
+ },
+ "outputs": [],
+ "source": [
+ "#drop columns I'm not using\n",
+ "data_=data.drop(['slug','ranknow','volume','market'], axis=1)\n",
+ "data_.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 238
+ },
+ "id": "txJAiKwmNNg7",
+ "outputId": "6c2070a1-7b4d-4155-f209-0eda8ff7bc41"
+ },
+ "outputs": [],
+ "source": [
+ "data_.set_index('row_id', inplace=True)\n",
+ "data_.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Bynw95JgNUx6",
+ "outputId": "5e4c28c2-3a39-4a64-8106-c00c1d643c9b"
+ },
+ "outputs": [],
+ "source": [
+ "#counitng unique symbols \n",
+ "data[\"name\"].unique() #total number of unique cryptos "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "xjq9b8pRNR9C"
+ },
+ "outputs": [],
+ "source": [
+ "#set date to timestamp format\n",
+ "data_['date'] = pd.to_datetime(data_['date']).dt.date "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "OVpiyV4PS4wY"
+ },
+ "outputs": [],
+ "source": [
+ "#pick out the currency for two years span (2016>)\n",
+ "\n",
+ "date = data_[data_['date'] >= dt.date(2016, 1, 1)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Vvz_kNbgS5dQ"
+ },
+ "outputs": [],
+ "source": [
+ "#show if each one closed up or down each day\n",
+ "date['pos_neg']= date['open']-date['close']\n",
+ "date.head()\n",
+ "#create a binary column - 0 = gain, 1 = loss to have something to predict\n",
+ "date['Up/Down'] = np.where(date['pos_neg']>0, '0', '1')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "uZLR2R9WQyzR"
+ },
+ "outputs": [],
+ "source": [
+ "#create data sets for different crypto currencies\n",
+ "etherdate = date[date['symbol']=='ETH']\n",
+ "ltcdate = date[date['symbol']=='LTC']\n",
+ "tetherdate = date[date['symbol']=='USDT']\n",
+ "cardanodate = date[date['symbol']=='ADA']\n",
+ "rippledate = date[date['symbol']=='XRP']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "TQKkNj3wUM6B"
+ },
+ "outputs": [],
+ "source": [
+ "#concat the different frames into one\n",
+ "frames=[etherdate,ltcdate,tetherdate,cardanodate,rippledate]\n",
+ "five_crypto = pd.concat(frames)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Zjb4kqCzVXwY",
+ "outputId": "66568d7f-6656-4101-913c-11e21f22e7e6"
+ },
+ "outputs": [],
+ "source": [
+ "five_crypto.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 296
+ },
+ "id": "TXNuyzFoVevf",
+ "outputId": "3c7295fd-0ec6-4a3f-e09b-e81d5cc7adf8"
+ },
+ "outputs": [],
+ "source": [
+ "#this chart shows each of the six and the number of times they closed up for the day. \n",
+ "sns.set_style('whitegrid')\n",
+ "sns.countplot(x='symbol',hue='Up/Down',data=five_crypto,palette='rainbow')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 454
+ },
+ "id": "b-KaRiCFVlF3",
+ "outputId": "426f31e8-b3b3-44e6-ea05-641d58318b45"
+ },
+ "outputs": [],
+ "source": [
+ "#a comparison the the up/down ratio at the end of the day for the six.\n",
+ "sns.set(style=\"whitegrid\")\n",
+ "\n",
+ "g = sns.factorplot(\"symbol\", \"close_ratio\", \"Up/Down\",\n",
+ " data=five_crypto, kind=\"bar\",\n",
+ " size=6, palette=\"muted\",\n",
+ " legend_out=True)\n",
+ "g.despine(left=True)\n",
+ "g.set_ylabels(\"Ratio Count\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "S3ZOHw0xW5RK"
+ },
+ "outputs": [],
+ "source": [
+ "data['market_billion'] = data['market'] / 1000000000\n",
+ "data['volume_million'] = data['volume'] / 1000000000\n",
+ "data['volume_billion'] = data['volume']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 338
+ },
+ "id": "s86ZmNTCWvdf",
+ "outputId": "e1b87670-2d40-4b8e-d1c9-378782b9f330"
+ },
+ "outputs": [],
+ "source": [
+ "# Let's prepare one dataframe where we will observe closing prices for each currency\n",
+ "wide_format = data.groupby(['date', 'name'])['close'].last().unstack()\n",
+ "wide_format.head(3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 301
+ },
+ "id": "fKfl6z3bWxMP",
+ "outputId": "010fb6ee-6ac0-4fa5-a6b8-0ff102902d0a"
+ },
+ "outputs": [],
+ "source": [
+ "ax = data.groupby(['name'])['market_billion'].last().sort_values(ascending=False).head(10).sort_values().plot(kind='barh');\n",
+ "ax.set_xlabel(\"Market cap (in billion USD)\");\n",
+ "plt.title(\"Top 10 Currencies by Market Cap\");"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 301
+ },
+ "id": "4MJQipOJXAlD",
+ "outputId": "0b8c53b6-4253-47e0-8c2f-d9f92a8ac7c1"
+ },
+ "outputs": [],
+ "source": [
+ "ax = data.groupby(['name'])['volume_million'].last().sort_values(ascending=False).head(10).sort_values().plot(kind='barh');\n",
+ "ax.set_xlabel(\"Transaction Volume (in million)\");\n",
+ "plt.title(\"Top 10 Currencies by Transaction Volume\");"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 357
+ },
+ "id": "qfrzkuc2XbeX",
+ "outputId": "a182edbc-70a2-4d4d-9f72-3727d9c538fc"
+ },
+ "outputs": [],
+ "source": [
+ "# For sake of convenience, let's define the top 6 currencies\n",
+ "\n",
+ "top_6_currency_names = data.groupby(['name'])['market'].last().sort_values(ascending=False).head(6).index\n",
+ "data_top_6_currencies = data[data['name'].isin(top_6_currency_names)]\n",
+ "data_top_6_currencies.head(5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 301
+ },
+ "id": "X2vurY0xXmff",
+ "outputId": "f08949e0-ec1b-4167-8875-99474ff74d57"
+ },
+ "outputs": [],
+ "source": [
+ "ax = data_top_6_currencies.groupby(['date', 'name'])['close'].mean().unstack().plot();\n",
+ "ax.set_ylabel(\"Price per 1 unit (in USD)\");\n",
+ "plt.title(\"Price per unit currency\");"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 301
+ },
+ "id": "0XQYcAf8Xy2o",
+ "outputId": "5dc80b97-f28a-40b7-f929-13cb06120176"
+ },
+ "outputs": [],
+ "source": [
+ "ax = data_top_6_currencies.groupby(['date', 'name'])['market_billion'].mean().unstack().plot();\n",
+ "ax.set_ylabel(\"Market Cap (in billion USD)\");\n",
+ "plt.title(\"Market cap per unit currency\");"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 506
+ },
+ "id": "_Ezp2S7NX9sI",
+ "outputId": "7da7fcc0-5908-4dd8-f980-bb7f8fce40da"
+ },
+ "outputs": [],
+ "source": [
+ "#co-relation between different cryptos\n",
+ "plt.figure(figsize=(14,8))\n",
+ "sns.heatmap(wide_format[top_6_currency_names].corr(),vmin=0, vmax=1, cmap='coolwarm', annot=True);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 357
+ },
+ "id": "NbAmRhK-CrQV",
+ "outputId": "e8338b77-ce5e-47ba-f17c-1b971f2d4b2b"
+ },
+ "outputs": [],
+ "source": [
+ "data_top_6_currencies.head(5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 661
+ },
+ "id": "yy3-xD2qJ8lI",
+ "outputId": "cf7f6044-f55c-4ee1-c38b-47e99bd705bd"
+ },
+ "outputs": [],
+ "source": [
+ "data_top_6_currencies.drop(['ranknow'],axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "O1V5XiRaF1DU",
+ "outputId": "d551e810-f579-4252-8c89-99a5b97df7ff"
+ },
+ "outputs": [],
+ "source": [
+ "data_top_6_currencies.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TNJ2Lwm3KDAX"
+ },
+ "source": [
+ "#Bitcoin"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "49_WACQuDBll",
+ "outputId": "5f524485-7bfd-4400-ac65-08819492e014"
+ },
+ "outputs": [],
+ "source": [
+ "#bitcoin\n",
+ "df1 = data_top_6_currencies[data_top_6_currencies['name']=='Bitcoin']\n",
+ "\n",
+ "df1.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "JjcbrGKDQTQC",
+ "outputId": "045951bb-0e3a-499b-ed16-8fcba13e5896"
+ },
+ "outputs": [],
+ "source": [
+ "print(df1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 357
+ },
+ "id": "l6R9yP7G_7C9",
+ "outputId": "7ec05f9a-69a0-4b30-9b00-8062d5ab045a"
+ },
+ "outputs": [],
+ "source": [
+ "df1.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "X4QbNB76AUtO",
+ "outputId": "3853da87-8db7-483b-bd83-0d69a36e18bc"
+ },
+ "outputs": [],
+ "source": [
+ "d = df1.iloc[:, 5:10]\n",
+ "print(d.head())\n",
+ "print(d.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 410
+ },
+ "id": "zDuTpvheHFQ-",
+ "outputId": "9c6fdee9-de24-4f66-d1c6-00c12a69dad4"
+ },
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(12,6))\n",
+ "sns.lineplot(x=d.index, y=\"open\", data=d).set_title(\"Price of Bitcoin\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "35cpNfDfHFUN",
+ "outputId": "23b39114-4fc4-42a3-d7a3-aeab60030a3b"
+ },
+ "outputs": [],
+ "source": [
+ "data = d.iloc[:, 0]\n",
+ "print(len(data))\n",
+ "print(data)\n",
+ "hist = []\n",
+ "target = []\n",
+ "length = 90\n",
+ "for i in range(len(data)-length):\n",
+ " x = data[i:i+length]\n",
+ " y = data[i+length]\n",
+ " hist.append(x)\n",
+ " target.append(y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ybiiTeB7HFad",
+ "outputId": "97d5b512-2429-44d3-bc73-5a94b9325916"
+ },
+ "outputs": [],
+ "source": [
+ "print(hist[1][89])\n",
+ "print(data[90])\n",
+ "print(target[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "e5cpw-VsHTGw"
+ },
+ "outputs": [],
+ "source": [
+ "hist = np.array(hist)\n",
+ "target = np.array(target)\n",
+ "target = target.reshape(-1,1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Ook8PYf9HTLD"
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn.preprocessing import MinMaxScaler\n",
+ "sc = MinMaxScaler()\n",
+ "hist_scaled = sc.fit_transform(hist)\n",
+ "target_scaled = sc.fit_transform(target)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "T_-csG_1HTON",
+ "outputId": "5fbb5f68-96e1-4f98-fae9-1dbb1fbf7e47"
+ },
+ "outputs": [],
+ "source": [
+ "hist_scaled = hist_scaled.reshape((len(hist_scaled), length, 1))\n",
+ "print(hist_scaled.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Ui0ppeSGHTRf"
+ },
+ "outputs": [],
+ "source": [
+ "X_train = hist_scaled[:1900,:,:]\n",
+ "X_test = hist_scaled[1900:,:,:]\n",
+ "y_train = target_scaled[:1900,:]\n",
+ "y_test = target_scaled[1900:,:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "T5o66_7XHTUm"
+ },
+ "outputs": [],
+ "source": [
+ "import tensorflow as tf\n",
+ "from tensorflow.keras import layers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "9C8DS-c7Hgp1",
+ "outputId": "e29c0ce0-4ca5-495a-eb3d-3a93be61950b"
+ },
+ "outputs": [],
+ "source": [
+ "model = tf.keras.Sequential()\n",
+ "model.add(layers.LSTM(units=32, return_sequences=True,\n",
+ " input_shape=(90,1), dropout=0.2))\n",
+ "model.add(layers.LSTM(units=32, return_sequences=True,\n",
+ " dropout=0.2))\n",
+ "model.add(layers.LSTM(units=32, dropout=0.2))\n",
+ "model.add(layers.Dense(units=1))\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "P91hyF2GHgzR"
+ },
+ "outputs": [],
+ "source": [
+ "model.compile(optimizer='adam', loss='mean_squared_error')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "I5uoPY81Hmy4",
+ "outputId": "08ad7eed-2807-415b-cca6-b7f1db79ebf9"
+ },
+ "outputs": [],
+ "source": [
+ "history = model.fit(X_train, y_train, epochs=30, batch_size=32)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 502
+ },
+ "id": "_2skqc4uHoo4",
+ "outputId": "9334689b-73a1-48c3-9411-c5eff80540f8"
+ },
+ "outputs": [],
+ "source": [
+ "loss = history.history['loss']\n",
+ "epoch_count = range(1, len(loss) + 1)\n",
+ "plt.figure(figsize=(12,8))\n",
+ "plt.plot(epoch_count, loss, 'r--')\n",
+ "plt.legend(['Training Loss'])\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.ylabel('Loss')\n",
+ "plt.show();"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 501
+ },
+ "id": "XeNxAYOlHg8b",
+ "outputId": "474939e5-3aea-401b-b312-ab3c434ca9b5"
+ },
+ "outputs": [],
+ "source": [
+ "pred = model.predict(X_test)\n",
+ "plt.figure(figsize=(12,8))\n",
+ "plt.plot(y_test, color='blue', label='Real')\n",
+ "plt.plot(pred, color='red', label='Prediction')\n",
+ "plt.title('Bitcoin Price Prediction')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "I7VuAnbfJByj"
+ },
+ "source": [
+ "#XRP (Ripple)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ZK38bhh6JByj",
+ "outputId": "022a7b36-5a24-43b5-bfb1-e14b29dc6af1"
+ },
+ "outputs": [],
+ "source": [
+ "#bitcoin\n",
+ "df2 = data_top_6_currencies[data_top_6_currencies['name']=='XRP']\n",
+ "\n",
+ "df2.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "jV8de94uJByj",
+ "outputId": "16f68219-e348-47fe-ec42-156427813871"
+ },
+ "outputs": [],
+ "source": [
+ "print(df2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 357
+ },
+ "id": "vT1hHSzbJByk",
+ "outputId": "0c15cfeb-ac61-446b-97ae-91a31b813235"
+ },
+ "outputs": [],
+ "source": [
+ "df2.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "bOfzu0IhJByk",
+ "outputId": "99041c2b-6683-45d9-fee7-3d9e2ba89101"
+ },
+ "outputs": [],
+ "source": [
+ "d2 = df2.iloc[:, 5:10]\n",
+ "print(d2.head())\n",
+ "print(d2.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 410
+ },
+ "id": "hSNYPEm7JByk",
+ "outputId": "b1bb1d23-7a54-4439-d8fa-f204886cab79"
+ },
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(12,6))\n",
+ "sns.lineplot(x=d2.index, y=\"open\", data=d2).set_title(\"Price of Ripple\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "pcllm_j2JByk",
+ "outputId": "effc2f69-4149-4390-be0e-f260f5ae6ec2"
+ },
+ "outputs": [],
+ "source": [
+ "data = d2.iloc[:, 0]\n",
+ "\n",
+ "print(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "JoAgH76ZLPjI"
+ },
+ "outputs": [],
+ "source": [
+ "hist = []\n",
+ "target = []\n",
+ "length = 90\n",
+ "for i in range(2042,len(data)-length):\n",
+ " x = data[2042:i+length]\n",
+ " y = data[i+length]\n",
+ " hist.append(x)\n",
+ " target.append(y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "jGSDstNAJByl"
+ },
+ "outputs": [],
+ "source": [
+ "hist = np.array(hist)\n",
+ "target = np.array(target)\n",
+ "target = target.reshape(-1,1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "FpugPkG9NAtl"
+ },
+ "outputs": [],
+ "source": [
+ "hist = hist.reshape(-1,1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 381
+ },
+ "id": "icGR5bLNJByl",
+ "outputId": "2e8b762b-8ce2-4d91-bd44-a006a6fe559e"
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn.preprocessing import MinMaxScaler\n",
+ "sc = MinMaxScaler()\n",
+ "hist_scaled = sc.fit_transform(hist)\n",
+ "target_scaled = sc.fit_transform(target)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "AXyWLswgJByl",
+ "outputId": "27002554-6aff-4173-bfcc-dff86d04a1d2"
+ },
+ "outputs": [],
+ "source": [
+ "hist_scaled = hist_scaled.reshape((len(hist_scaled), length, 1))\n",
+ "print(hist_scaled.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "j9I5SQkPJByl"
+ },
+ "outputs": [],
+ "source": [
+ "X_train = hist_scaled[:1900,:,:]\n",
+ "X_test = hist_scaled[1900:,:,:]\n",
+ "y_train = target_scaled[:1900,:]\n",
+ "y_test = target_scaled[1900:,:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "wMmrZ7hFJByl"
+ },
+ "outputs": [],
+ "source": [
+ "import tensorflow as tf\n",
+ "from tensorflow.keras import layers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Tfb5_8l6JByl",
+ "outputId": "c59503b3-e2cf-4ccc-ab5a-3d487da4d767"
+ },
+ "outputs": [],
+ "source": [
+ "model = tf.keras.Sequential()\n",
+ "model.add(layers.LSTM(units=32, return_sequences=True,\n",
+ " input_shape=(90,1), dropout=0.2))\n",
+ "model.add(layers.LSTM(units=32, return_sequences=True,\n",
+ " dropout=0.2))\n",
+ "model.add(layers.LSTM(units=32, dropout=0.2))\n",
+ "model.add(layers.Dense(units=1))\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "XblbdTVWJByl"
+ },
+ "outputs": [],
+ "source": [
+ "model.compile(optimizer='adam', loss='mean_squared_error')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "wCu3FeoiJBym",
+ "outputId": "9e95db9e-0329-42e8-a740-f56c4c8f1ce1"
+ },
+ "outputs": [],
+ "source": [
+ "history = model.fit(X_train, y_train, epochs=30, batch_size=32)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 501
+ },
+ "id": "jqnFts9HJBym",
+ "outputId": "4d9124d3-4f27-4678-d728-7037c6b723fa"
+ },
+ "outputs": [],
+ "source": [
+ "pred = model.predict(X_test)\n",
+ "plt.figure(figsize=(12,8))\n",
+ "plt.plot(y_test, color='blue', label='Real')\n",
+ "plt.plot(pred, color='red', label='Prediction')\n",
+ "plt.title('Ripple Price Prediction')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HVw69BHsP-SH"
+ },
+ "source": [
+ "#Ethereum"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "6Gf_B5zeP-SI",
+ "outputId": "f8b449f2-2861-485b-a856-d34e57c17867"
+ },
+ "outputs": [],
+ "source": [
+ "#bitcoin\n",
+ "df3 = data_top_6_currencies[data_top_6_currencies['slug']=='ethereum']\n",
+ "\n",
+ "df3.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "wTrWsHkZP-SI",
+ "outputId": "5790481a-51d7-4fcf-c069-acb926892bcb"
+ },
+ "outputs": [],
+ "source": [
+ "print(df3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 357
+ },
+ "id": "zsvIQYO3P-SI",
+ "outputId": "57dc9c1a-5cb2-47d2-9a82-b99842248a94"
+ },
+ "outputs": [],
+ "source": [
+ "df3.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "TnheAbnAP-SI",
+ "outputId": "f3948958-bb36-4936-8eac-86179eaf36f2"
+ },
+ "outputs": [],
+ "source": [
+ "d3 = df3.iloc[:, 5:10]\n",
+ "print(d3.head())\n",
+ "print(d3.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 410
+ },
+ "id": "T3xP1Eo8P-SJ",
+ "outputId": "e6c83d5e-0291-41a2-94c7-91cc5afb6104"
+ },
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(12,6))\n",
+ "sns.lineplot(x=d3.index, y=\"open\", data=d3).set_title(\"Price of Ethereum\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "kbwR0m7RP-SJ",
+ "outputId": "4880a7a3-544e-4a9c-c33d-ca7f1e7d0221"
+ },
+ "outputs": [],
+ "source": [
+ "data = d3.iloc[:, 0]\n",
+ "\n",
+ "print(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "KPzSexH2P-SJ"
+ },
+ "outputs": [],
+ "source": [
+ "hist = []\n",
+ "target = []\n",
+ "length = 90\n",
+ "for i in range(3986,len(data)-length):\n",
+ " x = data[3986:i+length]\n",
+ " y = data[i+length]\n",
+ " hist.append(x)\n",
+ " target.append(y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "I5QVQECtP-SJ"
+ },
+ "outputs": [],
+ "source": [
+ "hist = np.array(hist)\n",
+ "target = np.array(target)\n",
+ "target = target.reshape(-1,1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "rhe9sKH3P-SJ"
+ },
+ "outputs": [],
+ "source": [
+ "hist = hist.reshape(-1,1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "FNSDMq0mP-SK",
+ "outputId": "e90ec60d-7d53-468e-cc15-43f4f25b1dba"
+ },
+ "outputs": [],
+ "source": [
+ "hist_scaled = hist_scaled.reshape((len(hist_scaled), length, 1))\n",
+ "print(hist_scaled.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "bBrwRQLsP-SK"
+ },
+ "outputs": [],
+ "source": [
+ "X_train = hist_scaled[:1900,:,:]\n",
+ "X_test = hist_scaled[1900:,:,:]\n",
+ "y_train = target_scaled[:1900,:]\n",
+ "y_test = target_scaled[1900:,:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "zvGPHlb2P-SK"
+ },
+ "outputs": [],
+ "source": [
+ "import tensorflow as tf\n",
+ "from tensorflow.keras import layers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "aWFjURq-P-SK",
+ "outputId": "f941254d-4964-4573-85c7-1ff0fe062ca8"
+ },
+ "outputs": [],
+ "source": [
+ "model = tf.keras.Sequential()\n",
+ "model.add(layers.LSTM(units=32, return_sequences=True,\n",
+ " input_shape=(90,1), dropout=0.2))\n",
+ "model.add(layers.LSTM(units=32, return_sequences=True,\n",
+ " dropout=0.2))\n",
+ "model.add(layers.LSTM(units=32, dropout=0.2))\n",
+ "model.add(layers.Dense(units=1))\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "gcUc1ZBHP-SK"
+ },
+ "outputs": [],
+ "source": [
+ "model.compile(optimizer='adam', loss='mean_squared_error')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ZfAB0JWTP-SK",
+ "outputId": "45985017-af61-43c6-cc29-d85d10c38a68"
+ },
+ "outputs": [],
+ "source": [
+ "history = model.fit(X_train, y_train, epochs=30, batch_size=32)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 504
+ },
+ "id": "DT2SuvDdP-SK",
+ "outputId": "1aa682fa-9709-457f-845d-a6c5b4b8d126"
+ },
+ "outputs": [],
+ "source": [
+ "loss = history.history['loss']\n",
+ "epoch_count = range(1, len(loss) + 1)\n",
+ "plt.figure(figsize=(12,8))\n",
+ "plt.plot(epoch_count, loss, 'r--')\n",
+ "plt.legend(['Training Loss'])\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.ylabel('Loss')\n",
+ "plt.show();"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 501
+ },
+ "id": "eArbMF4TP-SL",
+ "outputId": "306dcd95-93db-47f1-8c32-c97757c9b584"
+ },
+ "outputs": [],
+ "source": [
+ "pred = model.predict(X_test)\n",
+ "plt.figure(figsize=(12,8))\n",
+ "plt.plot(y_test, color='blue', label='Real')\n",
+ "plt.plot(pred, color='red', label='Prediction')\n",
+ "plt.title('Ethereum Price Prediction')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "k4_p4ZjbUJWo"
+ },
+ "source": [
+ "#Bitcoin Cash (BCH)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "O4H46vFAUJWo",
+ "outputId": "515cdde8-18ad-4f16-effa-dd66d0ac6380"
+ },
+ "outputs": [],
+ "source": [
+ "\n",
+ "df4 = data_top_6_currencies[data_top_6_currencies['symbol']=='BCH']\n",
+ "\n",
+ "df4.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "5-4itcj_UJWo",
+ "outputId": "35e6401a-92fb-4f3d-d845-4246d7e5c05f"
+ },
+ "outputs": [],
+ "source": [
+ "print(df4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 357
+ },
+ "id": "mn_n52xrUJWo",
+ "outputId": "b2088956-9bf1-4b69-ade6-ef1049fd8f15"
+ },
+ "outputs": [],
+ "source": [
+ "df4.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "lNOi437hUJWp",
+ "outputId": "4acc7d16-f0b8-4458-86b6-8b895f60776b"
+ },
+ "outputs": [],
+ "source": [
+ "d4 = df4.iloc[:, 5:10]\n",
+ "print(d4.head())\n",
+ "print(d4.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 410
+ },
+ "id": "WHrBesaXUJWp",
+ "outputId": "5a43e17c-b851-4460-a112-9e7f950c115f"
+ },
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(12,6))\n",
+ "sns.lineplot(x=d4.index, y=\"open\", data=d4).set_title(\"Price of Bitcoin Cash\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "YIlXzSaUUJWp",
+ "outputId": "e4214da2-9147-4e4f-ddbf-60224a7ab83e"
+ },
+ "outputs": [],
+ "source": [
+ "data = d4.iloc[:, 0]\n",
+ "\n",
+ "print(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "sZeAotUnUJWp"
+ },
+ "outputs": [],
+ "source": [
+ "hist = []\n",
+ "target = []\n",
+ "length = 90\n",
+ "for i in range(6775,len(data)-length):\n",
+ " x = data[6775:i+length]\n",
+ " y = data[i+length]\n",
+ " hist.append(x)\n",
+ " target.append(y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "SBJAZqjGUJWp"
+ },
+ "outputs": [],
+ "source": [
+ "hist = np.array(hist)\n",
+ "target = np.array(target)\n",
+ "target = target.reshape(-1,1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "jLGddq22UJWp"
+ },
+ "outputs": [],
+ "source": [
+ "hist = hist.reshape(1,-1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "hC4XeVpMUJWq",
+ "outputId": "d822ff1e-cb56-4e83-cb99-c7792309c96c"
+ },
+ "outputs": [],
+ "source": [
+ "hist_scaled = hist_scaled.reshape((len(hist_scaled), length, 1))\n",
+ "print(hist_scaled.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "xctoaT_KUJWq"
+ },
+ "outputs": [],
+ "source": [
+ "X_train = hist_scaled[:1900,:,:]\n",
+ "X_test = hist_scaled[1900:,:,:]\n",
+ "y_train = target_scaled[:1900,:]\n",
+ "y_test = target_scaled[1900:,:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "kTzfwpunUJWq"
+ },
+ "outputs": [],
+ "source": [
+ "import tensorflow as tf\n",
+ "from tensorflow.keras import layers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "RoZrj801UJWq",
+ "outputId": "5bcea4f2-4c03-4c59-d37f-1330e4e8795b"
+ },
+ "outputs": [],
+ "source": [
+ "model = tf.keras.Sequential()\n",
+ "model.add(layers.LSTM(units=32, return_sequences=True,\n",
+ " input_shape=(90,1), dropout=0.2))\n",
+ "model.add(layers.LSTM(units=32, return_sequences=True,\n",
+ " dropout=0.2))\n",
+ "model.add(layers.LSTM(units=32, dropout=0.2))\n",
+ "model.add(layers.Dense(units=1))\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "bb-ArUEIUJWq"
+ },
+ "outputs": [],
+ "source": [
+ "model.compile(optimizer='adam', loss='mean_squared_error')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "bQebGMcsUJWq",
+ "outputId": "aa752aaa-dbd6-4aac-cae6-42f18e6f7b1d"
+ },
+ "outputs": [],
+ "source": [
+ "history = model.fit(X_train, y_train, epochs=30, batch_size=32)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 501
+ },
+ "id": "UH77ijSZUJWq",
+ "outputId": "9009ae84-499d-4c8d-d18e-dc8fc479ecb7"
+ },
+ "outputs": [],
+ "source": [
+ "pred = model.predict(X_test)\n",
+ "plt.figure(figsize=(12,8))\n",
+ "plt.plot(y_test, color='blue', label='Real')\n",
+ "plt.plot(pred, color='red', label='Prediction')\n",
+ "plt.title('Bitcoin Cash Prediction')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "zIyf6qzoX2oL"
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pBEwefqtX3kW"
+ },
+ "source": [
+ "#EOS"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "TjCsw9DNX3kW",
+ "outputId": "c44e67f8-b66c-4c41-c898-73f7821629aa"
+ },
+ "outputs": [],
+ "source": [
+ "df5 = data_top_6_currencies[data_top_6_currencies['symbol']=='EOS']\n",
+ "\n",
+ "df5.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7VzpsN7TX3kW",
+ "outputId": "81f3990c-1939-4fe5-818f-8e597166f8f6"
+ },
+ "outputs": [],
+ "source": [
+ "print(df5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 357
+ },
+ "id": "e-d8UPiHX3kW",
+ "outputId": "253e678c-66a2-4f72-d8b9-5422a9fee9e6"
+ },
+ "outputs": [],
+ "source": [
+ "df5.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "stGfWD9YX3kX",
+ "outputId": "cbaee387-a35d-483d-d0b4-3c4c6d1293e8"
+ },
+ "outputs": [],
+ "source": [
+ "d5 = df5.iloc[:, 5:10]\n",
+ "print(d5.head())\n",
+ "print(d5.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 410
+ },
+ "id": "Y8SdLCNNX3kX",
+ "outputId": "2e8edc3b-5e7a-4684-8318-4e6229b17166"
+ },
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(12,6))\n",
+ "sns.lineplot(x=d5.index, y=\"open\", data=d5).set_title(\"Price of EOS\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "wvdmLlFsX3kX",
+ "outputId": "6a0d3dad-bb9c-4325-cae5-959f222c9ac3"
+ },
+ "outputs": [],
+ "source": [
+ "data = d5.iloc[:, 0]\n",
+ "\n",
+ "print(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "yawhGJ_rX3kX"
+ },
+ "outputs": [],
+ "source": [
+ "hist = []\n",
+ "target = []\n",
+ "length = 90\n",
+ "for i in range(7270,len(data)-length):\n",
+ " x = data[7270:i+length]\n",
+ " y = data[i+length]\n",
+ " hist.append(x)\n",
+ " target.append(y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ldt2BGLhX3kX"
+ },
+ "outputs": [],
+ "source": [
+ "hist = np.array(hist)\n",
+ "target = np.array(target)\n",
+ "target = target.reshape(-1,1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ylr7fwbAX3kX"
+ },
+ "outputs": [],
+ "source": [
+ "hist = hist.reshape(1,-1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ejlHo8paX3kX",
+ "outputId": "49925969-2cfb-42de-9d6e-c1e695866eec"
+ },
+ "outputs": [],
+ "source": [
+ "hist_scaled = hist_scaled.reshape((len(hist_scaled), length, 1))\n",
+ "print(hist_scaled.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "TOFbdITuX3kX"
+ },
+ "outputs": [],
+ "source": [
+ "X_train = hist_scaled[:1900,:,:]\n",
+ "X_test = hist_scaled[1900:,:,:]\n",
+ "y_train = target_scaled[:1900,:]\n",
+ "y_test = target_scaled[1900:,:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "2q1aJEr5X3kY"
+ },
+ "outputs": [],
+ "source": [
+ "import tensorflow as tf\n",
+ "from tensorflow.keras import layers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "zAml_KZ4X3kY",
+ "outputId": "0c411e7b-36d4-4ac5-9f14-b2bdac508d0b"
+ },
+ "outputs": [],
+ "source": [
+ "model = tf.keras.Sequential()\n",
+ "model.add(layers.LSTM(units=32, return_sequences=True,\n",
+ " input_shape=(90,1), dropout=0.2))\n",
+ "model.add(layers.LSTM(units=32, return_sequences=True,\n",
+ " dropout=0.2))\n",
+ "model.add(layers.LSTM(units=32, dropout=0.2))\n",
+ "model.add(layers.Dense(units=1))\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "blIv5kwJX3kY"
+ },
+ "outputs": [],
+ "source": [
+ "model.compile(optimizer='adam', loss='mean_squared_error')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "PYbQNxSRX3kY",
+ "outputId": "9a4cbc6e-4075-47e9-820f-083cdc6c59f5"
+ },
+ "outputs": [],
+ "source": [
+ "history = model.fit(X_train, y_train, epochs=30, batch_size=32)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 501
+ },
+ "id": "TT6qgo_iX3kY",
+ "outputId": "cb71873e-b886-4e41-9947-f2b3765d2f6e"
+ },
+ "outputs": [],
+ "source": [
+ "pred = model.predict(X_test)\n",
+ "plt.figure(figsize=(12,8))\n",
+ "plt.plot(y_test, color='blue', label='Real')\n",
+ "plt.plot(pred, color='red', label='Prediction')\n",
+ "plt.title('EOS Prediction')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "F087Ykqwazgx"
+ },
+ "source": [
+ "#Stellar"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gSZO1_fBa5Oe",
+ "outputId": "e7db4e69-1838-409d-e2fe-1c8c92614a2c"
+ },
+ "outputs": [],
+ "source": [
+ "df6 = data_top_6_currencies[data_top_6_currencies['name']=='Stellar']\n",
+ "\n",
+ "df6.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "mejmybDCa5Of",
+ "outputId": "573b4195-a4fd-47c3-a924-2579a992512e"
+ },
+ "outputs": [],
+ "source": [
+ "print(df6)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 357
+ },
+ "id": "6jooY2Gza5Of",
+ "outputId": "cb00a2dd-c0ba-4ade-9a3f-5f769a78a2bb"
+ },
+ "outputs": [],
+ "source": [
+ "df6.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "u4YVMNKha5Of",
+ "outputId": "33a752e7-ee5a-4f3b-be07-032b4400973e"
+ },
+ "outputs": [],
+ "source": [
+ "d = df6.iloc[:, 5:10]\n",
+ "print(d.head())\n",
+ "print(d.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 410
+ },
+ "id": "Pib0FOL3a5Of",
+ "outputId": "64bbcef9-615a-45c8-817c-d607016ab130"
+ },
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(12,6))\n",
+ "sns.lineplot(x=d.index, y=\"open\", data=d).set_title(\"Price of Stellar\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "1FPPamzibSiL",
+ "outputId": "05da7d73-8e57-4f94-b52d-da5d4cf97102"
+ },
+ "outputs": [],
+ "source": [
+ "print(d)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "O5snSz7Aa5Of",
+ "outputId": "83f62e46-1b7f-4825-cc84-2128c9d2c5be"
+ },
+ "outputs": [],
+ "source": [
+ "data = d.iloc[:, 0]\n",
+ "print(len(data))\n",
+ "print(data)\n",
+ "hist = []\n",
+ "target = []\n",
+ "length = 90\n",
+ "for i in range(5197, len(data)-length):\n",
+ " x = data[5197,:i+length]\n",
+ " y = data[i+length]\n",
+ " hist.append(x)\n",
+ " target.append(y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "5wCLydLha5Og"
+ },
+ "outputs": [],
+ "source": [
+ "hist = np.array(hist)\n",
+ "target = np.array(target)\n",
+ "target = target.reshape(-1,1)\n",
+ "hist=hist.reshape(-1,1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "PxEu7xN0a5Og"
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn.preprocessing import MinMaxScaler\n",
+ "sc = MinMaxScaler()\n",
+ "hist_scaled = sc.fit_transform(hist)\n",
+ "target_scaled = sc.fit_transform(target)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "JBgXLlZAa5Og",
+ "outputId": "371d59f2-bdfe-4e90-b328-3ba17be8f29f"
+ },
+ "outputs": [],
+ "source": [
+ "hist_scaled = hist_scaled.reshape((len(hist_scaled), length, 1))\n",
+ "print(hist_scaled.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "wmqhRFVUa5Og"
+ },
+ "outputs": [],
+ "source": [
+ "X_train = hist_scaled[:1900,:,:]\n",
+ "X_test = hist_scaled[1900:,:,:]\n",
+ "y_train = target_scaled[:1900,:]\n",
+ "y_test = target_scaled[1900:,:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "hKMNQyGna5Og"
+ },
+ "outputs": [],
+ "source": [
+ "import tensorflow as tf\n",
+ "from tensorflow.keras import layers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "wIUvXFUCa5Og",
+ "outputId": "7a104795-05f8-411f-94f9-314eb6c46b84"
+ },
+ "outputs": [],
+ "source": [
+ "model = tf.keras.Sequential()\n",
+ "model.add(layers.LSTM(units=32, return_sequences=True,\n",
+ " input_shape=(90,1), dropout=0.2))\n",
+ "model.add(layers.LSTM(units=32, return_sequences=True,\n",
+ " dropout=0.2))\n",
+ "model.add(layers.LSTM(units=32, dropout=0.2))\n",
+ "model.add(layers.Dense(units=1))\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "2JVd7TxPa5Og"
+ },
+ "outputs": [],
+ "source": [
+ "model.compile(optimizer='adam', loss='mean_squared_error')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "m5lWh3Yba5Oh",
+ "outputId": "2846ed4f-6ace-4a2f-b80c-b580aba9a176"
+ },
+ "outputs": [],
+ "source": [
+ "history = model.fit(X_train, y_train, epochs=30, batch_size=32)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 501
+ },
+ "id": "bCmQ1HDRa5Oh",
+ "outputId": "ed8ad8b9-4bb2-4563-f756-26dde67ca6eb"
+ },
+ "outputs": [],
+ "source": [
+ "pred = model.predict(X_test)\n",
+ "plt.figure(figsize=(12,8))\n",
+ "plt.plot(y_test, color='blue', label='Real')\n",
+ "plt.plot(pred, color='red', label='Prediction')\n",
+ "plt.title('Stellar Price Prediction')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "collapsed_sections": [
+ "W06FRpNCHKCo",
+ "sQ_p1oakHNPn",
+ "bGQUmu60NwRD",
+ "VJIl1DwwN43f"
+ ],
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3.10.7 64-bit (microsoft store)",
+ "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": "3.10.11"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "94348be3325f1ae160a67cb39ab80a1e458b4a3911d8ece2b1e6db7371f6660e"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}