Create notebook stock predictor

stock_predictor.ipynb

@@ -0,0 +1,363 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyNi+Ewkxp2IZ8viyYUSIC21",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "gpuClass": "standard"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/jsebdev/Stock_Predictor/blob/main/stock_predictor.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n",
+        "project_path = '/content/drive/MyDrive/projects/Stock_Predicter'\n",
+        "%cd $project_path"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Xr3Qozgfktoc",
+        "outputId": "78396a70-6eaa-462b-f7ca-75e282dab940"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n",
+            "/content/drive/MyDrive/projects/Stock_Predicter\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# install dotenv\n",
+        "!pip install python-dotenv"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "E0itUkoVeKYn",
+        "outputId": "a876789d-096c-4301-e316-023f87e2e5de"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Collecting python-dotenv\n",
+            "  Downloading python_dotenv-1.0.0-py3-none-any.whl (19 kB)\n",
+            "Installing collected packages: python-dotenv\n",
+            "Successfully installed python-dotenv-1.0.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# install polygon client\n",
+        "!pip install polygon-api-client"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2bylenpXc1oB",
+        "outputId": "c47ad32c-3c50-41d9-a6ce-c051fb6639b5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Collecting polygon-api-client\n",
+            "  Downloading polygon_api_client-1.8.5-py3-none-any.whl (38 kB)\n",
+            "Requirement already satisfied: urllib3<2.0.0,>=1.26.9 in /usr/local/lib/python3.9/dist-packages (from polygon-api-client) (1.26.15)\n",
+            "Collecting websockets<11.0,>=10.3\n",
+            "  Downloading websockets-10.4-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (106 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.5/106.5 KB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: certifi<2023.0.0,>=2022.5.18 in /usr/local/lib/python3.9/dist-packages (from polygon-api-client) (2022.12.7)\n",
+            "Installing collected packages: websockets, polygon-api-client\n",
+            "Successfully installed polygon-api-client-1.8.5 websockets-10.4\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "e8SQqogMQYLh"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import pandas as pd\n",
+        "import pandas_datareader as web\n",
+        "import datetime as dt\n",
+        "import yfinance as yfin\n",
+        "\n",
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Dense, Dropout, LSTM\n",
+        "from dotenv import dotenv_values\n",
+        "from polygon import RESTClient\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "config = dotenv_values(\"env_stock_predictor\")\n",
+        "POLIGON_API_KEY = config['POLIGON_API_KEY']"
+      ],
+      "metadata": {
+        "id": "MwIQIS6GeSJr"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Select a company for now\n",
+        "ticker = 'AAPL'\n",
+        "\n",
+        "start = dt.datetime(2013,1,1)\n",
+        "end = dt.date.today()\n",
+        "# end = dt.datetime(2023,3,15)\n",
+        "\n",
+        "# data = web.DataReader(ticker, 'yahoo', start, end) # This trows \"TypeError: string indices must be integers\"\n",
+        "\n",
+        "yfin.pdr_override()\n",
+        "data = web.data.get_data_yahoo(ticker, start, end)\n",
+        "print(data.tail())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "O6dtJpJwS5Eg",
+        "outputId": "8782cb37-06ce-47c0-b352-f1f82a6db7de"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\r[*********************100%***********************]  1 of 1 completed\n",
+            "                  Open        High         Low       Close   Adj Close  \\\n",
+            "Date                                                                     \n",
+            "2023-03-29  159.369995  161.050003  159.350006  160.770004  160.770004   \n",
+            "2023-03-30  161.529999  162.470001  161.270004  162.360001  162.360001   \n",
+            "2023-03-31  162.440002  165.000000  161.910004  164.899994  164.899994   \n",
+            "2023-04-03  164.270004  166.289993  164.220001  166.169998  166.169998   \n",
+            "2023-04-04  166.600006  166.839996  165.110001  165.630005  165.630005   \n",
+            "\n",
+            "              Volume  \n",
+            "Date                  \n",
+            "2023-03-29  51305700  \n",
+            "2023-03-30  49501700  \n",
+            "2023-03-31  68694700  \n",
+            "2023-04-03  56976200  \n",
+            "2023-04-04  46237900  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# using the poligon API\n",
+        "poligon_client = RESTClient(api_key=POLIGON_API_KEY)"
+      ],
+      "metadata": {
+        "id": "LEfjQ4cZi0tn"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# bars = poligon_client.get_aggs(ticker=ticker, multiplier=1, timespan=\"day\", from_=\"2023-01-09\", to=\"2023-01-15\")\n",
+        "bars = poligon_client.get_aggs(ticker=ticker, multiplier=1, timespan=\"day\", from_=start, to=end)\n"
+      ],
+      "metadata": {
+        "id": "edWz4rdxdwqh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for bar in bars[-5:]:\n",
+        "  print(type(bar))\n",
+        "  print(bar)\n",
+        "  print(bar.timestamp)\n",
+        "  print(dt.date.fromtimestamp(bar.timestamp/1000))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IX_o3NTggblq",
+        "outputId": "7a974d77-952e-425b-c702-e9a60fbb89be"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'polygon.rest.models.aggs.Agg'>\n",
+            "Agg(open=152.81, high=153.47, low=151.83, close=152.87, volume=47204791.0, vwap=152.6973, timestamp=1678251600000, transactions=405203, otc=None)\n",
+            "1678251600000\n",
+            "2023-03-08\n",
+            "<class 'polygon.rest.models.aggs.Agg'>\n",
+            "Agg(open=153.559, high=154.535, low=150.225, close=150.59, volume=53833122.0, vwap=152.4689, timestamp=1678338000000, transactions=480909, otc=None)\n",
+            "1678338000000\n",
+            "2023-03-09\n",
+            "<class 'polygon.rest.models.aggs.Agg'>\n",
+            "Agg(open=150.21, high=150.94, low=147.6096, close=148.5, volume=68559600.0, vwap=149.0716, timestamp=1678424400000, transactions=611457, otc=None)\n",
+            "1678424400000\n",
+            "2023-03-10\n",
+            "<class 'polygon.rest.models.aggs.Agg'>\n",
+            "Agg(open=147.805, high=153.14, low=147.7, close=150.47, volume=84457122.0, vwap=151.1835, timestamp=1678680000000, transactions=760660, otc=None)\n",
+            "1678680000000\n",
+            "2023-03-13\n",
+            "<class 'polygon.rest.models.aggs.Agg'>\n",
+            "Agg(open=151.28, high=153.4, low=150.1, close=152.59, volume=72045893.0, vwap=152.1061, timestamp=1678766400000, transactions=565196, otc=None)\n",
+            "1678766400000\n",
+            "2023-03-14\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(type(spy))\n",
+        "print(spy.head())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EMoXLT5vd8Ex",
+        "outputId": "d3c00e06-bf0a-4384-a21d-643d72a6848c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "                  Open        High         Low       Close   Adj Close  \\\n",
+            "Date                                                                     \n",
+            "2022-10-24  375.890015  380.059998  373.109985  378.869995  375.704315   \n",
+            "2022-10-25  378.790009  385.250000  378.670013  384.920013  381.703735   \n",
+            "2022-10-26  381.619995  387.579987  381.350006  382.019989  378.827972   \n",
+            "2022-10-27  383.070007  385.000000  379.329987  379.980011  376.805023   \n",
+            "2022-10-28  379.869995  389.519989  379.679993  389.019989  385.769470   \n",
+            "\n",
+            "               Volume  \n",
+            "Date                   \n",
+            "2022-10-24   85436900  \n",
+            "2022-10-25   78846300  \n",
+            "2022-10-26  104087300  \n",
+            "2022-10-27   81971800  \n",
+            "2022-10-28  100302000  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = web.DataReader('GE', 'yahoo', start='2019-09-10', end='2019-10-09')\n",
+        "print(start)\n",
+        "print(end)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 353
+        },
+        "id": "THGxnQbSUgvw",
+        "outputId": "82234614-328b-40b7-9024-fa32e20b2858"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "TypeError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-17-078ffcb02a17>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'GE'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'yahoo'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'2019-09-10'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'2019-10-09'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    205\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 207\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas_datareader/data.py\u001b[0m in \u001b[0;36mDataReader\u001b[0;34m(name, data_source, start, end, retry_count, pause, session, api_key)\u001b[0m\n\u001b[1;32m    368\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdata_source\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"yahoo\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 370\u001b[0;31m         return YahooDailyReader(\n\u001b[0m\u001b[1;32m    371\u001b[0m             \u001b[0msymbols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    372\u001b[0m             \u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas_datareader/base.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    251\u001b[0m         \u001b[0;31m# If a single symbol, (e.g., 'GOOG')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m             \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_one_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    254\u001b[0m         \u001b[0;31m# Or multiple symbols, (e.g., ['GOOG', 'AAPL', 'MSFT'])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas_datareader/yahoo/daily.py\u001b[0m in \u001b[0;36m_read_one_data\u001b[0;34m(self, url, params)\u001b[0m\n\u001b[1;32m    151\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m             \u001b[0mj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mptrn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDOTALL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"context\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"dispatcher\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"stores\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"HistoricalPriceStore\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"No data fetched for symbol {} using {}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mTypeError\u001b[0m: string indices must be integers"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "scaler = MinMaxScaler(feature_range=(0,1))\n",
+        "scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1,1))\n",
+        "prediction_days = 60\n",
+        "\n",
+        "x_train = []\n",
+        "y_train = []\n",
+        "\n",
+        "for x in range()"
+      ],
+      "metadata": {
+        "id": "ccV59ukvXaNF"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}