BlockReward.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMqLyoakBQTvydh09NH6uLd"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "# Importing Libraries\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "BS5Y5ziino5S"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "OnJCeihamkV3"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.ensemble import RandomForestRegressor"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"block_data.csv\")\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eOqdlsjQovma",
        "outputId": "980aecc0-c362-4421-c753-21790128ea18"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      timeStamp  blockReward\n",
            "0    1686530987     0.013346\n",
            "1    1686530999     0.009191\n",
            "2    1686531011     0.055999\n",
            "3    1686531023     0.027110\n",
            "4    1686531035     0.009815\n",
            "..          ...          ...\n",
            "295  1686534563     0.282281\n",
            "296  1686534575     0.015875\n",
            "297  1686534587     0.062831\n",
            "298  1686534599     0.049868\n",
            "299  1686534611     0.132992\n",
            "\n",
            "[300 rows x 2 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "plt.xlabel('timestamp(seconds)')\n",
        "plt.ylabel('blockreward(ether)')\n",
        "plt.scatter(df.timeStamp,df.blockReward,marker=\".\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "xL_YTmRLpFt1",
        "outputId": "abb0bd2e-19ca-4e1f-aec9-d301154cb2a9"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7faac6dd12d0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model=RandomForestRegressor(n_estimators=100,random_state=42)\n",
        "model.fit(df.timeStamp.values.reshape(-1,1),df.blockReward.values.ravel())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "pA2p1WVPrh_P",
        "outputId": "867d1393-067a-421d-fd79-baeb920bd872"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_timeStamp=np.array([[1686534888]])\n",
        "predicted_blockReward=model.predict(new_timeStamp)\n",
        "\n",
        "plt.scatter(df.timeStamp,df.blockReward,label='Data Points')\n",
        "plt.scatter(new_timeStamp,predicted_blockReward,color=\"red\",label='Predicted Future Block Reward')\n",
        "plt.xlabel('TimeStamp')\n",
        "plt.ylabel('Block Reward')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "print(\"Predicated Block Reward:\",predicted_blockReward[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "gavvRYYBtmPP",
        "outputId": "ef99fb66-69cd-4f31-e665-8c4dc0f9b167"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicated Block Reward: 0.09039861972264897\n"
          ]
        }
      ]
    }
  ]
}