Earthquake_Prediction_(SC).ipynb

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  "metadata": {
    "colab": {
      "name": "Earthquake Prediction (SC).ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/avsd47/Earthquake_Prediction/blob/main/Earthquake_Prediction_(SC).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GfR4uOJOaZym"
      },
      "source": [
        "### Southern California Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F_-RdjUSacZX"
      },
      "source": [
        "# Imports\n",
        "import numpy as np \n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set(style=\"darkgrid\")\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7cSzVwnefxG4",
        "outputId": "85abeaea-e66a-44bc-ac21-6cbfb0b8d2f7"
      },
      "source": [
        "pip install skfeature-chappers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting skfeature-chappers\n",
            "  Downloading https://files.pythonhosted.org/packages/e6/45/19bb801eb3b4a892534ab86468ad0669a68ff63578610f90051190e3622f/skfeature-chappers-1.0.3.tar.gz\n",
            "Building wheels for collected packages: skfeature-chappers\n",
            "  Building wheel for skfeature-chappers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for skfeature-chappers: filename=skfeature_chappers-1.0.3-py2.py3-none-any.whl size=59510 sha256=4e150fcc72468f382e987dedb0569179720f2eedc551e4f53933b923eb2cf911\n",
            "  Stored in directory: /root/.cache/pip/wheels/ac/61/bf/1b3a8c232a0072409508c2ec4c12f316e95681ae72ba7315d2\n",
            "Successfully built skfeature-chappers\n",
            "Installing collected packages: skfeature-chappers\n",
            "Successfully installed skfeature-chappers-1.0.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
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              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "bxtHyZWxanSt",
        "outputId": "7ec3f088-27cf-419f-fd24-65d6845e4653"
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-a2ba2d97-97e5-48c7-aab2-b12465962970\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-a2ba2d97-97e5-48c7-aab2-b12465962970\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving Southern California,nM=50,dtp=15.xlsx to Southern California,nM=50,dtp=15.xlsx\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dApvJjNUaqzD"
      },
      "source": [
        "sc_df = pd.read_excel(\"Southern California,nM=50,dtp=15.xlsx\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "MoGpU0-Ycdvu",
        "outputId": "b8ff7b16-e11d-48b3-9950-df379d3c0245"
      },
      "source": [
        "sc_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>T_Days</th>\n",
              "      <th>b_lik</th>\n",
              "      <th>a_lik</th>\n",
              "      <th>b_lsq</th>\n",
              "      <th>a_lsq</th>\n",
              "      <th>std_b_lik</th>\n",
              "      <th>std_b_lsq</th>\n",
              "      <th>T_Rec_lik4.0</th>\n",
              "      <th>T_Rec_lik4.1</th>\n",
              "      <th>T_Rec_lik4.2</th>\n",
              "      <th>T_Rec_lik4.3</th>\n",
              "      <th>T_Rec_lik4.4</th>\n",
              "      <th>T_Rec_lik4.5</th>\n",
              "      <th>T_Rec_lik4.6</th>\n",
              "      <th>T_Rec_lik4.7</th>\n",
              "      <th>T_Rec_lik4.8</th>\n",
              "      <th>T_Rec_lik4.9</th>\n",
              "      <th>T_Rec_lik5.0</th>\n",
              "      <th>T_Rec_lik5.1</th>\n",
              "      <th>T_Rec_lik5.2</th>\n",
              "      <th>T_Rec_lik5.3</th>\n",
              "      <th>T_Rec_lik5.4</th>\n",
              "      <th>T_Rec_lik5.5</th>\n",
              "      <th>T_Rec_lik5.6</th>\n",
              "      <th>T_Rec_lik5.7</th>\n",
              "      <th>T_Rec_lik5.8</th>\n",
              "      <th>T_Rec_lik5.9</th>\n",
              "      <th>T_Rec_lik6.0</th>\n",
              "      <th>T_Rec_lsq4.0</th>\n",
              "      <th>T_Rec_lsq4.1</th>\n",
              "      <th>T_Rec_lsq4.2</th>\n",
              "      <th>T_Rec_lsq4.3</th>\n",
              "      <th>T_Rec_lsq4.4</th>\n",
              "      <th>T_Rec_lsq4.5</th>\n",
              "      <th>T_Rec_lsq4.6</th>\n",
              "      <th>T_Rec_lsq4.7</th>\n",
              "      <th>T_Rec_lsq4.8</th>\n",
              "      <th>T_Rec_lsq4.9</th>\n",
              "      <th>T_Rec_lsq5.0</th>\n",
              "      <th>T_Rec_lsq5.1</th>\n",
              "      <th>T_Rec_lsq5.2</th>\n",
              "      <th>T_lsq_lsq5.3</th>\n",
              "      <th>T_Rec_lsq5.4</th>\n",
              "      <th>T_Rec_lsq5.5</th>\n",
              "      <th>T_Rec_lsq5.6</th>\n",
              "      <th>T_Rec_lsq5.7</th>\n",
              "      <th>T_Rec_lsq5.8</th>\n",
              "      <th>T_Rec_lsq5.9</th>\n",
              "      <th>T_Rec_lsq6.0</th>\n",
              "      <th>Beta Value</th>\n",
              "      <th>Z Value</th>\n",
              "      <th>Mean_Mag</th>\n",
              "      <th>Energy</th>\n",
              "      <th>M_def_Lik</th>\n",
              "      <th>M_def_lsq</th>\n",
              "      <th>NU_mlik</th>\n",
              "      <th>NU_lsq</th>\n",
              "      <th>x6</th>\n",
              "      <th>x7_mlk</th>\n",
              "      <th>x7_lsq</th>\n",
              "      <th>Actual Mag</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>33</td>\n",
              "      <td>1.219928</td>\n",
              "      <td>4.870784</td>\n",
              "      <td>1.124103</td>\n",
              "      <td>4.584905</td>\n",
              "      <td>0.173615</td>\n",
              "      <td>0.147411</td>\n",
              "      <td>33.685545</td>\n",
              "      <td>44.610431</td>\n",
              "      <td>59.078471</td>\n",
              "      <td>78.238782</td>\n",
              "      <td>103.613158</td>\n",
              "      <td>137.216944</td>\n",
              "      <td>181.719099</td>\n",
              "      <td>240.654179</td>\n",
              "      <td>318.703064</td>\n",
              "      <td>422.064739</td>\n",
              "      <td>558.948640</td>\n",
              "      <td>740.226682</td>\n",
              "      <td>980.296760</td>\n",
              "      <td>1298.226289</td>\n",
              "      <td>1719.266620</td>\n",
              "      <td>2276.858616</td>\n",
              "      <td>3015.288667</td>\n",
              "      <td>3993.206114</td>\n",
              "      <td>5288.281432</td>\n",
              "      <td>7003.375159</td>\n",
              "      <td>9274.707530</td>\n",
              "      <td>26.916753</td>\n",
              "      <td>34.868480</td>\n",
              "      <td>45.169300</td>\n",
              "      <td>58.513181</td>\n",
              "      <td>75.799101</td>\n",
              "      <td>98.191615</td>\n",
              "      <td>127.199309</td>\n",
              "      <td>164.776433</td>\n",
              "      <td>213.454564</td>\n",
              "      <td>276.513152</td>\n",
              "      <td>358.200460</td>\n",
              "      <td>464.019773</td>\n",
              "      <td>601.100147</td>\n",
              "      <td>778.676703</td>\n",
              "      <td>1008.712792</td>\n",
              "      <td>1306.705971</td>\n",
              "      <td>1692.732072</td>\n",
              "      <td>2192.797715</td>\n",
              "      <td>2840.592377</td>\n",
              "      <td>3679.758053</td>\n",
              "      <td>4766.829426</td>\n",
              "      <td>-3.540903</td>\n",
              "      <td>5.872981</td>\n",
              "      <td>2.9560</td>\n",
              "      <td>2.586651e+08</td>\n",
              "      <td>0.247320</td>\n",
              "      <td>0.161278</td>\n",
              "      <td>28.089850</td>\n",
              "      <td>24.368150</td>\n",
              "      <td>4.24</td>\n",
              "      <td>0.000219</td>\n",
              "      <td>0.000424</td>\n",
              "      <td>3.80</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>35</td>\n",
              "      <td>1.180148</td>\n",
              "      <td>4.767355</td>\n",
              "      <td>1.104731</td>\n",
              "      <td>4.549281</td>\n",
              "      <td>0.170595</td>\n",
              "      <td>0.149488</td>\n",
              "      <td>31.427172</td>\n",
              "      <td>41.240138</td>\n",
              "      <td>54.117150</td>\n",
              "      <td>71.014940</td>\n",
              "      <td>93.188973</td>\n",
              "      <td>122.286730</td>\n",
              "      <td>160.470105</td>\n",
              "      <td>210.576034</td>\n",
              "      <td>276.327270</td>\n",
              "      <td>362.608977</td>\n",
              "      <td>475.831684</td>\n",
              "      <td>624.407573</td>\n",
              "      <td>819.375486</td>\n",
              "      <td>1075.221083</td>\n",
              "      <td>1410.953094</td>\n",
              "      <td>1851.515624</td>\n",
              "      <td>2429.641438</td>\n",
              "      <td>3188.283935</td>\n",
              "      <td>4183.808479</td>\n",
              "      <td>5490.180217</td>\n",
              "      <td>7204.459519</td>\n",
              "      <td>25.924596</td>\n",
              "      <td>33.433752</td>\n",
              "      <td>43.117964</td>\n",
              "      <td>55.607244</td>\n",
              "      <td>71.714092</td>\n",
              "      <td>92.486348</td>\n",
              "      <td>119.275366</td>\n",
              "      <td>153.823923</td>\n",
              "      <td>198.379601</td>\n",
              "      <td>255.840997</td>\n",
              "      <td>329.946303</td>\n",
              "      <td>425.516489</td>\n",
              "      <td>548.768938</td>\n",
              "      <td>707.721922</td>\n",
              "      <td>912.716235</td>\n",
              "      <td>1177.087920</td>\n",
              "      <td>1518.035857</td>\n",
              "      <td>1957.740645</td>\n",
              "      <td>2524.807576</td>\n",
              "      <td>3256.127574</td>\n",
              "      <td>4199.277156</td>\n",
              "      <td>-3.972195</td>\n",
              "      <td>6.401932</td>\n",
              "      <td>2.9680</td>\n",
              "      <td>2.542504e+08</td>\n",
              "      <td>0.200376</td>\n",
              "      <td>0.122002</td>\n",
              "      <td>26.605017</td>\n",
              "      <td>23.774643</td>\n",
              "      <td>4.24</td>\n",
              "      <td>0.000288</td>\n",
              "      <td>0.000485</td>\n",
              "      <td>3.73</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>34</td>\n",
              "      <td>1.208388</td>\n",
              "      <td>4.840778</td>\n",
              "      <td>1.093218</td>\n",
              "      <td>4.503974</td>\n",
              "      <td>0.179506</td>\n",
              "      <td>0.146919</td>\n",
              "      <td>33.438860</td>\n",
              "      <td>44.166219</td>\n",
              "      <td>58.334970</td>\n",
              "      <td>77.049129</td>\n",
              "      <td>101.766886</td>\n",
              "      <td>134.414227</td>\n",
              "      <td>177.535003</td>\n",
              "      <td>234.489147</td>\n",
              "      <td>309.714473</td>\n",
              "      <td>409.072472</td>\n",
              "      <td>540.305028</td>\n",
              "      <td>713.637664</td>\n",
              "      <td>942.576303</td>\n",
              "      <td>1244.959636</td>\n",
              "      <td>1644.349100</td>\n",
              "      <td>2171.864761</td>\n",
              "      <td>2868.610164</td>\n",
              "      <td>3788.875082</td>\n",
              "      <td>5004.365725</td>\n",
              "      <td>6609.792028</td>\n",
              "      <td>8730.247358</td>\n",
              "      <td>25.140652</td>\n",
              "      <td>32.336896</td>\n",
              "      <td>41.592988</td>\n",
              "      <td>53.498538</td>\n",
              "      <td>68.811924</td>\n",
              "      <td>88.508605</td>\n",
              "      <td>113.843250</td>\n",
              "      <td>146.429667</td>\n",
              "      <td>188.343600</td>\n",
              "      <td>242.254949</td>\n",
              "      <td>311.597847</td>\n",
              "      <td>400.789412</td>\n",
              "      <td>515.511111</td>\n",
              "      <td>663.070675</td>\n",
              "      <td>852.867592</td>\n",
              "      <td>1096.991855</td>\n",
              "      <td>1410.994088</td>\n",
              "      <td>1814.876114</td>\n",
              "      <td>2334.365068</td>\n",
              "      <td>3002.552202</td>\n",
              "      <td>3862.000783</td>\n",
              "      <td>-3.759304</td>\n",
              "      <td>6.074159</td>\n",
              "      <td>2.9594</td>\n",
              "      <td>2.590495e+08</td>\n",
              "      <td>0.234019</td>\n",
              "      <td>0.120077</td>\n",
              "      <td>27.654928</td>\n",
              "      <td>23.288424</td>\n",
              "      <td>4.24</td>\n",
              "      <td>0.000237</td>\n",
              "      <td>0.000525</td>\n",
              "      <td>3.73</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>34</td>\n",
              "      <td>1.226821</td>\n",
              "      <td>4.888703</td>\n",
              "      <td>1.088908</td>\n",
              "      <td>4.487710</td>\n",
              "      <td>0.186599</td>\n",
              "      <td>0.147005</td>\n",
              "      <td>35.486054</td>\n",
              "      <td>47.069520</td>\n",
              "      <td>62.434096</td>\n",
              "      <td>82.814023</td>\n",
              "      <td>109.846428</td>\n",
              "      <td>145.702833</td>\n",
              "      <td>193.263595</td>\n",
              "      <td>256.349285</td>\n",
              "      <td>340.027598</td>\n",
              "      <td>451.020440</td>\n",
              "      <td>598.243903</td>\n",
              "      <td>793.524497</td>\n",
              "      <td>1052.549176</td>\n",
              "      <td>1396.125478</td>\n",
              "      <td>1851.853001</td>\n",
              "      <td>2456.340488</td>\n",
              "      <td>3258.146617</td>\n",
              "      <td>4321.680741</td>\n",
              "      <td>5732.376908</td>\n",
              "      <td>7603.556805</td>\n",
              "      <td>10085.532934</td>\n",
              "      <td>25.084245</td>\n",
              "      <td>32.232341</td>\n",
              "      <td>41.417384</td>\n",
              "      <td>53.219829</td>\n",
              "      <td>68.385542</td>\n",
              "      <td>87.872930</td>\n",
              "      <td>112.913513</td>\n",
              "      <td>145.089750</td>\n",
              "      <td>186.435043</td>\n",
              "      <td>239.562237</td>\n",
              "      <td>307.828747</td>\n",
              "      <td>395.548724</td>\n",
              "      <td>508.265699</td>\n",
              "      <td>653.102904</td>\n",
              "      <td>839.213437</td>\n",
              "      <td>1078.358690</td>\n",
              "      <td>1385.651627</td>\n",
              "      <td>1780.511855</td>\n",
              "      <td>2287.892862</td>\n",
              "      <td>2939.858970</td>\n",
              "      <td>3777.611664</td>\n",
              "      <td>-3.759304</td>\n",
              "      <td>6.074159</td>\n",
              "      <td>2.9540</td>\n",
              "      <td>2.578125e+08</td>\n",
              "      <td>0.255144</td>\n",
              "      <td>0.118706</td>\n",
              "      <td>28.351208</td>\n",
              "      <td>23.111640</td>\n",
              "      <td>4.24</td>\n",
              "      <td>0.000209</td>\n",
              "      <td>0.000541</td>\n",
              "      <td>3.73</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>37</td>\n",
              "      <td>1.235898</td>\n",
              "      <td>4.912304</td>\n",
              "      <td>1.086130</td>\n",
              "      <td>4.476040</td>\n",
              "      <td>0.188913</td>\n",
              "      <td>0.145901</td>\n",
              "      <td>39.763868</td>\n",
              "      <td>52.854065</td>\n",
              "      <td>70.253534</td>\n",
              "      <td>93.380879</td>\n",
              "      <td>124.121705</td>\n",
              "      <td>164.982360</td>\n",
              "      <td>219.294274</td>\n",
              "      <td>291.485577</td>\n",
              "      <td>387.442134</td>\n",
              "      <td>514.987428</td>\n",
              "      <td>684.520416</td>\n",
              "      <td>909.863376</td>\n",
              "      <td>1209.388856</td>\n",
              "      <td>1607.517617</td>\n",
              "      <td>2136.709691</td>\n",
              "      <td>2840.110898</td>\n",
              "      <td>3775.070590</td>\n",
              "      <td>5017.817427</td>\n",
              "      <td>6669.674415</td>\n",
              "      <td>8865.319923</td>\n",
              "      <td>11783.768209</td>\n",
              "      <td>27.332513</td>\n",
              "      <td>35.098820</td>\n",
              "      <td>45.071859</td>\n",
              "      <td>57.878655</td>\n",
              "      <td>74.324395</td>\n",
              "      <td>95.443057</td>\n",
              "      <td>122.562412</td>\n",
              "      <td>157.387506</td>\n",
              "      <td>202.107864</td>\n",
              "      <td>259.535140</td>\n",
              "      <td>333.279902</td>\n",
              "      <td>427.978627</td>\n",
              "      <td>549.585211</td>\n",
              "      <td>705.745300</td>\n",
              "      <td>906.276985</td>\n",
              "      <td>1163.788088</td>\n",
              "      <td>1494.468839</td>\n",
              "      <td>1919.109789</td>\n",
              "      <td>2464.408950</td>\n",
              "      <td>3164.650353</td>\n",
              "      <td>4063.859555</td>\n",
              "      <td>-4.382740</td>\n",
              "      <td>6.770086</td>\n",
              "      <td>2.9514</td>\n",
              "      <td>2.359142e+08</td>\n",
              "      <td>0.265315</td>\n",
              "      <td>0.118907</td>\n",
              "      <td>28.697267</td>\n",
              "      <td>22.991352</td>\n",
              "      <td>3.80</td>\n",
              "      <td>0.000196</td>\n",
              "      <td>0.000552</td>\n",
              "      <td>3.88</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   T_Days     b_lik     a_lik     b_lsq  ...   x7_mlk     x7_lsq  Actual Mag    \n",
              "0      33  1.219928  4.870784  1.124103  ...  0.000219  0.000424        3.80 NaN\n",
              "1      35  1.180148  4.767355  1.104731  ...  0.000288  0.000485        3.73 NaN\n",
              "2      34  1.208388  4.840778  1.093218  ...  0.000237  0.000525        3.73 NaN\n",
              "3      34  1.226821  4.888703  1.088908  ...  0.000209  0.000541        3.73 NaN\n",
              "4      37  1.235898  4.912304  1.086130  ...  0.000196  0.000552        3.88 NaN\n",
              "\n",
              "[5 rows x 62 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfS6lXvaQe9P"
      },
      "source": [
        "sc_df = sc_df.iloc[:,0:61]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wW-_yObXOplK"
      },
      "source": [
        "sc_df = sc_df[sc_df[\"Actual Mag\"]>2.6] #Applying cutoff magnitude"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sg_7bcycOx_6"
      },
      "source": [
        "def label(row):\n",
        "    if row[\"Actual Mag\"] >5:  #Threshold magnitude\n",
        "        return 1\n",
        "    else:\n",
        "        return 0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VQA0uFBtO0LN"
      },
      "source": [
        "sc_df['mag'] = sc_df.apply (lambda row: label(row), axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "09ue9Lq8O5eZ",
        "outputId": "b0865cc8-40e1-4671-f7f3-513de01366cb"
      },
      "source": [
        "pd.options.display.max_rows = 400\n",
        "sc_df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "T_Days             0\n",
              "b_lik              0\n",
              "a_lik              0\n",
              "b_lsq              0\n",
              "a_lsq              0\n",
              "std_b_lik          0\n",
              "std_b_lsq          0\n",
              "T_Rec_lik4.0       0\n",
              "T_Rec_lik4.1       0\n",
              "T_Rec_lik4.2       0\n",
              "T_Rec_lik4.3       0\n",
              "T_Rec_lik4.4       0\n",
              "T_Rec_lik4.5       0\n",
              "T_Rec_lik4.6       0\n",
              "T_Rec_lik4.7       0\n",
              "T_Rec_lik4.8       0\n",
              "T_Rec_lik4.9       0\n",
              "T_Rec_lik5.0       0\n",
              "T_Rec_lik5.1       0\n",
              "T_Rec_lik5.2       0\n",
              "T_Rec_lik5.3       0\n",
              "T_Rec_lik5.4       0\n",
              "T_Rec_lik5.5       0\n",
              "T_Rec_lik5.6       0\n",
              "T_Rec_lik5.7       0\n",
              "T_Rec_lik5.8       0\n",
              "T_Rec_lik5.9       0\n",
              "T_Rec_lik6.0       0\n",
              "T_Rec_lsq4.0       0\n",
              "T_Rec_lsq4.1       0\n",
              "T_Rec_lsq4.2       0\n",
              "T_Rec_lsq4.3       0\n",
              "T_Rec_lsq4.4       0\n",
              "T_Rec_lsq4.5       0\n",
              "T_Rec_lsq4.6       0\n",
              "T_Rec_lsq4.7       0\n",
              "T_Rec_lsq4.8       0\n",
              "T_Rec_lsq4.9       0\n",
              "T_Rec_lsq5.0       0\n",
              "T_Rec_lsq5.1       0\n",
              "T_Rec_lsq5.2       0\n",
              "T_lsq_lsq5.3       0\n",
              "T_Rec_lsq5.4       0\n",
              "T_Rec_lsq5.5       0\n",
              "T_Rec_lsq5.6       0\n",
              "T_Rec_lsq5.7       0\n",
              "T_Rec_lsq5.8       0\n",
              "T_Rec_lsq5.9       0\n",
              "T_Rec_lsq6.0       0\n",
              "Beta Value         0\n",
              "Z Value         4418\n",
              "Mean_Mag           0\n",
              "Energy             0\n",
              "M_def_Lik          0\n",
              "M_def_lsq          0\n",
              "NU_mlik            0\n",
              "NU_lsq             0\n",
              "x6                 0\n",
              "x7_mlk             0\n",
              "x7_lsq             0\n",
              "Actual Mag         0\n",
              "mag                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YfYEKNpSO87q"
      },
      "source": [
        "sc_df = sc_df.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0EHJ-kbdPLBo",
        "outputId": "9581ced6-2b93-4b08-f57d-1bb84848e894"
      },
      "source": [
        "pd.options.display.max_rows = 400\n",
        "sc_df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "T_Days          0\n",
              "b_lik           0\n",
              "a_lik           0\n",
              "b_lsq           0\n",
              "a_lsq           0\n",
              "std_b_lik       0\n",
              "std_b_lsq       0\n",
              "T_Rec_lik4.0    0\n",
              "T_Rec_lik4.1    0\n",
              "T_Rec_lik4.2    0\n",
              "T_Rec_lik4.3    0\n",
              "T_Rec_lik4.4    0\n",
              "T_Rec_lik4.5    0\n",
              "T_Rec_lik4.6    0\n",
              "T_Rec_lik4.7    0\n",
              "T_Rec_lik4.8    0\n",
              "T_Rec_lik4.9    0\n",
              "T_Rec_lik5.0    0\n",
              "T_Rec_lik5.1    0\n",
              "T_Rec_lik5.2    0\n",
              "T_Rec_lik5.3    0\n",
              "T_Rec_lik5.4    0\n",
              "T_Rec_lik5.5    0\n",
              "T_Rec_lik5.6    0\n",
              "T_Rec_lik5.7    0\n",
              "T_Rec_lik5.8    0\n",
              "T_Rec_lik5.9    0\n",
              "T_Rec_lik6.0    0\n",
              "T_Rec_lsq4.0    0\n",
              "T_Rec_lsq4.1    0\n",
              "T_Rec_lsq4.2    0\n",
              "T_Rec_lsq4.3    0\n",
              "T_Rec_lsq4.4    0\n",
              "T_Rec_lsq4.5    0\n",
              "T_Rec_lsq4.6    0\n",
              "T_Rec_lsq4.7    0\n",
              "T_Rec_lsq4.8    0\n",
              "T_Rec_lsq4.9    0\n",
              "T_Rec_lsq5.0    0\n",
              "T_Rec_lsq5.1    0\n",
              "T_Rec_lsq5.2    0\n",
              "T_lsq_lsq5.3    0\n",
              "T_Rec_lsq5.4    0\n",
              "T_Rec_lsq5.5    0\n",
              "T_Rec_lsq5.6    0\n",
              "T_Rec_lsq5.7    0\n",
              "T_Rec_lsq5.8    0\n",
              "T_Rec_lsq5.9    0\n",
              "T_Rec_lsq6.0    0\n",
              "Beta Value      0\n",
              "Z Value         0\n",
              "Mean_Mag        0\n",
              "Energy          0\n",
              "M_def_Lik       0\n",
              "M_def_lsq       0\n",
              "NU_mlik         0\n",
              "NU_lsq          0\n",
              "x6              0\n",
              "x7_mlk          0\n",
              "x7_lsq          0\n",
              "Actual Mag      0\n",
              "mag             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GpOxxo3qPM3T"
      },
      "source": [
        "features = sc_df.iloc[:,0:60].to_numpy()\n",
        "target = sc_df[\"mag\"].to_numpy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9yh1PfXcPUSj",
        "outputId": "d6a408c7-1005-44c9-9082-39219606bdd5"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler=StandardScaler()\n",
        "scaler.fit(features)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StandardScaler(copy=True, with_mean=True, with_std=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "6iOBxt73PWXZ",
        "outputId": "6a1bf129-4704-4d2e-9419-1fe3994a1260"
      },
      "source": [
        "sc_df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>T_Days</th>\n",
              "      <th>b_lik</th>\n",
              "      <th>a_lik</th>\n",
              "      <th>b_lsq</th>\n",
              "      <th>a_lsq</th>\n",
              "      <th>std_b_lik</th>\n",
              "      <th>std_b_lsq</th>\n",
              "      <th>T_Rec_lik4.0</th>\n",
              "      <th>T_Rec_lik4.1</th>\n",
              "      <th>T_Rec_lik4.2</th>\n",
              "      <th>T_Rec_lik4.3</th>\n",
              "      <th>T_Rec_lik4.4</th>\n",
              "      <th>T_Rec_lik4.5</th>\n",
              "      <th>T_Rec_lik4.6</th>\n",
              "      <th>T_Rec_lik4.7</th>\n",
              "      <th>T_Rec_lik4.8</th>\n",
              "      <th>T_Rec_lik4.9</th>\n",
              "      <th>T_Rec_lik5.0</th>\n",
              "      <th>T_Rec_lik5.1</th>\n",
              "      <th>T_Rec_lik5.2</th>\n",
              "      <th>T_Rec_lik5.3</th>\n",
              "      <th>T_Rec_lik5.4</th>\n",
              "      <th>T_Rec_lik5.5</th>\n",
              "      <th>T_Rec_lik5.6</th>\n",
              "      <th>T_Rec_lik5.7</th>\n",
              "      <th>T_Rec_lik5.8</th>\n",
              "      <th>T_Rec_lik5.9</th>\n",
              "      <th>T_Rec_lik6.0</th>\n",
              "      <th>T_Rec_lsq4.0</th>\n",
              "      <th>T_Rec_lsq4.1</th>\n",
              "      <th>T_Rec_lsq4.2</th>\n",
              "      <th>T_Rec_lsq4.3</th>\n",
              "      <th>T_Rec_lsq4.4</th>\n",
              "      <th>T_Rec_lsq4.5</th>\n",
              "      <th>T_Rec_lsq4.6</th>\n",
              "      <th>T_Rec_lsq4.7</th>\n",
              "      <th>T_Rec_lsq4.8</th>\n",
              "      <th>T_Rec_lsq4.9</th>\n",
              "      <th>T_Rec_lsq5.0</th>\n",
              "      <th>T_Rec_lsq5.1</th>\n",
              "      <th>T_Rec_lsq5.2</th>\n",
              "      <th>T_lsq_lsq5.3</th>\n",
              "      <th>T_Rec_lsq5.4</th>\n",
              "      <th>T_Rec_lsq5.5</th>\n",
              "      <th>T_Rec_lsq5.6</th>\n",
              "      <th>T_Rec_lsq5.7</th>\n",
              "      <th>T_Rec_lsq5.8</th>\n",
              "      <th>T_Rec_lsq5.9</th>\n",
              "      <th>T_Rec_lsq6.0</th>\n",
              "      <th>Beta Value</th>\n",
              "      <th>Z Value</th>\n",
              "      <th>Mean_Mag</th>\n",
              "      <th>Energy</th>\n",
              "      <th>M_def_Lik</th>\n",
              "      <th>M_def_lsq</th>\n",
              "      <th>NU_mlik</th>\n",
              "      <th>NU_lsq</th>\n",
              "      <th>x6</th>\n",
              "      <th>x7_mlk</th>\n",
              "      <th>x7_lsq</th>\n",
              "      <th>Actual Mag</th>\n",
              "      <th>mag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>2.912400e+04</td>\n",
              "      <td>29124.000000</td>\n",
              "      <td>29124.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>22.685586</td>\n",
              "      <td>1.182601</td>\n",
              "      <td>4.778312</td>\n",
              "      <td>1.070275</td>\n",
              "      <td>4.422168</td>\n",
              "      <td>0.169179</td>\n",
              "      <td>0.137315</td>\n",
              "      <td>31.250638</td>\n",
              "      <td>44.513047</td>\n",
              "      <td>64.008407</td>\n",
              "      <td>92.977919</td>\n",
              "      <td>136.519183</td>\n",
              "      <td>202.748986</td>\n",
              "      <td>304.755879</td>\n",
              "      <td>463.918877</td>\n",
              "      <td>715.616222</td>\n",
              "      <td>1.119162e+03</td>\n",
              "      <td>1.775304e+03</td>\n",
              "      <td>2.857375e+03</td>\n",
              "      <td>4.667359e+03</td>\n",
              "      <td>7.737822e+03</td>\n",
              "      <td>1.301899e+04</td>\n",
              "      <td>2.222523e+04</td>\n",
              "      <td>3.848200e+04</td>\n",
              "      <td>6.754333e+04</td>\n",
              "      <td>1.200986e+05</td>\n",
              "      <td>2.161718e+05</td>\n",
              "      <td>3.935596e+05</td>\n",
              "      <td>29.047126</td>\n",
              "      <td>41.832265</td>\n",
              "      <td>61.101581</td>\n",
              "      <td>90.562340</td>\n",
              "      <td>136.262883</td>\n",
              "      <td>208.198840</td>\n",
              "      <td>323.101593</td>\n",
              "      <td>509.332790</td>\n",
              "      <td>8.155682e+02</td>\n",
              "      <td>1.326364e+03</td>\n",
              "      <td>2.190341e+03</td>\n",
              "      <td>3.671684e+03</td>\n",
              "      <td>6.245105e+03</td>\n",
              "      <td>1.077237e+04</td>\n",
              "      <td>1.883297e+04</td>\n",
              "      <td>3.334806e+04</td>\n",
              "      <td>5.976503e+04</td>\n",
              "      <td>1.083204e+05</td>\n",
              "      <td>1.983852e+05</td>\n",
              "      <td>3.668505e+05</td>\n",
              "      <td>6.843795e+05</td>\n",
              "      <td>3.695434</td>\n",
              "      <td>-5.902582</td>\n",
              "      <td>2.993179</td>\n",
              "      <td>2.298395e+09</td>\n",
              "      <td>0.247192</td>\n",
              "      <td>0.106248</td>\n",
              "      <td>27.344614</td>\n",
              "      <td>21.522420</td>\n",
              "      <td>4.294080</td>\n",
              "      <td>1.254642e-03</td>\n",
              "      <td>3.798533e-03</td>\n",
              "      <td>4.291723</td>\n",
              "      <td>0.151490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>17.180184</td>\n",
              "      <td>0.284475</td>\n",
              "      <td>0.739559</td>\n",
              "      <td>0.341084</td>\n",
              "      <td>0.974580</td>\n",
              "      <td>0.050529</td>\n",
              "      <td>0.053087</td>\n",
              "      <td>59.574644</td>\n",
              "      <td>98.730340</td>\n",
              "      <td>166.735009</td>\n",
              "      <td>287.084975</td>\n",
              "      <td>504.072720</td>\n",
              "      <td>902.293965</td>\n",
              "      <td>1645.076678</td>\n",
              "      <td>3050.515653</td>\n",
              "      <td>5742.449840</td>\n",
              "      <td>1.095114e+04</td>\n",
              "      <td>2.111358e+04</td>\n",
              "      <td>4.107441e+04</td>\n",
              "      <td>8.049256e+04</td>\n",
              "      <td>1.586705e+05</td>\n",
              "      <td>3.142562e+05</td>\n",
              "      <td>6.247525e+05</td>\n",
              "      <td>1.245779e+06</td>\n",
              "      <td>2.490137e+06</td>\n",
              "      <td>4.987111e+06</td>\n",
              "      <td>1.000359e+07</td>\n",
              "      <td>2.009165e+07</td>\n",
              "      <td>78.435399</td>\n",
              "      <td>139.344886</td>\n",
              "      <td>252.977083</td>\n",
              "      <td>468.312210</td>\n",
              "      <td>881.718064</td>\n",
              "      <td>1683.755767</td>\n",
              "      <td>3252.741945</td>\n",
              "      <td>6342.112197</td>\n",
              "      <td>1.245619e+04</td>\n",
              "      <td>2.460476e+04</td>\n",
              "      <td>4.881988e+04</td>\n",
              "      <td>9.720775e+04</td>\n",
              "      <td>1.940935e+05</td>\n",
              "      <td>3.884020e+05</td>\n",
              "      <td>7.786164e+05</td>\n",
              "      <td>1.563118e+06</td>\n",
              "      <td>3.141756e+06</td>\n",
              "      <td>6.320866e+06</td>\n",
              "      <td>1.272722e+07</td>\n",
              "      <td>2.564405e+07</td>\n",
              "      <td>5.169990e+07</td>\n",
              "      <td>10.936390</td>\n",
              "      <td>42.339733</td>\n",
              "      <td>0.101993</td>\n",
              "      <td>7.162784e+09</td>\n",
              "      <td>0.425650</td>\n",
              "      <td>0.249960</td>\n",
              "      <td>9.481180</td>\n",
              "      <td>5.031766</td>\n",
              "      <td>1.038057</td>\n",
              "      <td>4.459168e-03</td>\n",
              "      <td>8.856625e-03</td>\n",
              "      <td>0.683513</td>\n",
              "      <td>0.358532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.325363</td>\n",
              "      <td>2.548167</td>\n",
              "      <td>0.292237</td>\n",
              "      <td>1.739567</td>\n",
              "      <td>0.029117</td>\n",
              "      <td>0.019321</td>\n",
              "      <td>0.056661</td>\n",
              "      <td>0.061069</td>\n",
              "      <td>0.065820</td>\n",
              "      <td>0.070940</td>\n",
              "      <td>0.076459</td>\n",
              "      <td>0.082407</td>\n",
              "      <td>0.088818</td>\n",
              "      <td>0.095728</td>\n",
              "      <td>0.103175</td>\n",
              "      <td>1.112016e-01</td>\n",
              "      <td>1.198526e-01</td>\n",
              "      <td>1.291766e-01</td>\n",
              "      <td>1.392259e-01</td>\n",
              "      <td>1.500570e-01</td>\n",
              "      <td>1.617308e-01</td>\n",
              "      <td>1.743127e-01</td>\n",
              "      <td>1.878734e-01</td>\n",
              "      <td>2.024891e-01</td>\n",
              "      <td>2.182418e-01</td>\n",
              "      <td>2.352199e-01</td>\n",
              "      <td>2.535190e-01</td>\n",
              "      <td>0.059392</td>\n",
              "      <td>0.066003</td>\n",
              "      <td>0.073350</td>\n",
              "      <td>0.081514</td>\n",
              "      <td>0.090587</td>\n",
              "      <td>0.100671</td>\n",
              "      <td>0.111876</td>\n",
              "      <td>0.124329</td>\n",
              "      <td>1.381685e-01</td>\n",
              "      <td>1.535481e-01</td>\n",
              "      <td>1.706395e-01</td>\n",
              "      <td>1.896334e-01</td>\n",
              "      <td>2.107415e-01</td>\n",
              "      <td>2.341991e-01</td>\n",
              "      <td>2.602678e-01</td>\n",
              "      <td>2.892382e-01</td>\n",
              "      <td>3.214333e-01</td>\n",
              "      <td>3.572121e-01</td>\n",
              "      <td>3.969734e-01</td>\n",
              "      <td>4.411454e-01</td>\n",
              "      <td>4.901927e-01</td>\n",
              "      <td>-11.653340</td>\n",
              "      <td>-809.601435</td>\n",
              "      <td>2.741600</td>\n",
              "      <td>7.880711e+07</td>\n",
              "      <td>-1.246254</td>\n",
              "      <td>-1.393847</td>\n",
              "      <td>9.688521</td>\n",
              "      <td>7.285820</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.292826e-10</td>\n",
              "      <td>6.558059e-10</td>\n",
              "      <td>2.840000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.987931</td>\n",
              "      <td>4.272786</td>\n",
              "      <td>0.844696</td>\n",
              "      <td>3.774387</td>\n",
              "      <td>0.135802</td>\n",
              "      <td>0.100006</td>\n",
              "      <td>4.924677</td>\n",
              "      <td>6.268605</td>\n",
              "      <td>7.949204</td>\n",
              "      <td>10.152453</td>\n",
              "      <td>12.988938</td>\n",
              "      <td>16.463984</td>\n",
              "      <td>20.872842</td>\n",
              "      <td>26.397640</td>\n",
              "      <td>33.684698</td>\n",
              "      <td>4.292813e+01</td>\n",
              "      <td>5.443239e+01</td>\n",
              "      <td>6.918518e+01</td>\n",
              "      <td>8.767991e+01</td>\n",
              "      <td>1.111780e+02</td>\n",
              "      <td>1.404902e+02</td>\n",
              "      <td>1.776896e+02</td>\n",
              "      <td>2.260335e+02</td>\n",
              "      <td>2.856635e+02</td>\n",
              "      <td>3.590979e+02</td>\n",
              "      <td>4.551273e+02</td>\n",
              "      <td>5.779240e+02</td>\n",
              "      <td>3.872879</td>\n",
              "      <td>4.775011</td>\n",
              "      <td>5.870157</td>\n",
              "      <td>7.190029</td>\n",
              "      <td>8.735019</td>\n",
              "      <td>10.693295</td>\n",
              "      <td>13.188553</td>\n",
              "      <td>15.970119</td>\n",
              "      <td>1.952679e+01</td>\n",
              "      <td>2.372699e+01</td>\n",
              "      <td>2.883844e+01</td>\n",
              "      <td>3.509318e+01</td>\n",
              "      <td>4.262363e+01</td>\n",
              "      <td>5.180223e+01</td>\n",
              "      <td>6.316378e+01</td>\n",
              "      <td>7.697989e+01</td>\n",
              "      <td>9.446652e+01</td>\n",
              "      <td>1.162215e+02</td>\n",
              "      <td>1.422136e+02</td>\n",
              "      <td>1.749725e+02</td>\n",
              "      <td>2.151337e+02</td>\n",
              "      <td>-3.759304</td>\n",
              "      <td>-7.190467</td>\n",
              "      <td>2.927200</td>\n",
              "      <td>2.769785e+08</td>\n",
              "      <td>-0.053477</td>\n",
              "      <td>-0.061220</td>\n",
              "      <td>20.632579</td>\n",
              "      <td>18.100290</td>\n",
              "      <td>3.550000</td>\n",
              "      <td>9.577133e-05</td>\n",
              "      <td>1.804959e-04</td>\n",
              "      <td>3.790000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>20.000000</td>\n",
              "      <td>1.148320</td>\n",
              "      <td>4.687762</td>\n",
              "      <td>1.034377</td>\n",
              "      <td>4.346497</td>\n",
              "      <td>0.161048</td>\n",
              "      <td>0.132389</td>\n",
              "      <td>16.557785</td>\n",
              "      <td>21.229288</td>\n",
              "      <td>27.294964</td>\n",
              "      <td>35.028430</td>\n",
              "      <td>44.991217</td>\n",
              "      <td>57.916702</td>\n",
              "      <td>74.733448</td>\n",
              "      <td>96.229695</td>\n",
              "      <td>123.780554</td>\n",
              "      <td>1.592108e+02</td>\n",
              "      <td>2.050729e+02</td>\n",
              "      <td>2.640895e+02</td>\n",
              "      <td>3.418746e+02</td>\n",
              "      <td>4.410562e+02</td>\n",
              "      <td>5.684953e+02</td>\n",
              "      <td>7.373154e+02</td>\n",
              "      <td>9.548874e+02</td>\n",
              "      <td>1.236293e+03</td>\n",
              "      <td>1.595909e+03</td>\n",
              "      <td>2.067600e+03</td>\n",
              "      <td>2.678976e+03</td>\n",
              "      <td>13.143156</td>\n",
              "      <td>16.433042</td>\n",
              "      <td>20.551837</td>\n",
              "      <td>25.507639</td>\n",
              "      <td>31.757069</td>\n",
              "      <td>39.511983</td>\n",
              "      <td>49.267516</td>\n",
              "      <td>61.585124</td>\n",
              "      <td>7.711127e+01</td>\n",
              "      <td>9.649045e+01</td>\n",
              "      <td>1.205673e+02</td>\n",
              "      <td>1.516058e+02</td>\n",
              "      <td>1.891806e+02</td>\n",
              "      <td>2.353609e+02</td>\n",
              "      <td>2.932965e+02</td>\n",
              "      <td>3.679066e+02</td>\n",
              "      <td>4.615190e+02</td>\n",
              "      <td>5.822399e+02</td>\n",
              "      <td>7.369457e+02</td>\n",
              "      <td>9.312490e+02</td>\n",
              "      <td>1.174270e+03</td>\n",
              "      <td>0.040329</td>\n",
              "      <td>-0.039973</td>\n",
              "      <td>2.982400</td>\n",
              "      <td>4.729916e+08</td>\n",
              "      <td>0.171413</td>\n",
              "      <td>0.088183</td>\n",
              "      <td>25.484932</td>\n",
              "      <td>21.188630</td>\n",
              "      <td>4.010000</td>\n",
              "      <td>3.589559e-04</td>\n",
              "      <td>7.886241e-04</td>\n",
              "      <td>4.180000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>34.000000</td>\n",
              "      <td>1.339588</td>\n",
              "      <td>5.186202</td>\n",
              "      <td>1.247844</td>\n",
              "      <td>4.941300</td>\n",
              "      <td>0.194605</td>\n",
              "      <td>0.166689</td>\n",
              "      <td>34.349651</td>\n",
              "      <td>45.819543</td>\n",
              "      <td>61.175511</td>\n",
              "      <td>82.145865</td>\n",
              "      <td>110.287019</td>\n",
              "      <td>148.034194</td>\n",
              "      <td>198.477879</td>\n",
              "      <td>266.930651</td>\n",
              "      <td>357.414841</td>\n",
              "      <td>4.800007e+02</td>\n",
              "      <td>6.439225e+02</td>\n",
              "      <td>8.613474e+02</td>\n",
              "      <td>1.156820e+03</td>\n",
              "      <td>1.558555e+03</td>\n",
              "      <td>2.103372e+03</td>\n",
              "      <td>2.824539e+03</td>\n",
              "      <td>3.797146e+03</td>\n",
              "      <td>5.115072e+03</td>\n",
              "      <td>6.904989e+03</td>\n",
              "      <td>9.306304e+03</td>\n",
              "      <td>1.257624e+04</td>\n",
              "      <td>28.435031</td>\n",
              "      <td>37.038252</td>\n",
              "      <td>48.496104</td>\n",
              "      <td>63.573821</td>\n",
              "      <td>83.156141</td>\n",
              "      <td>109.502115</td>\n",
              "      <td>144.051631</td>\n",
              "      <td>189.297417</td>\n",
              "      <td>2.492680e+02</td>\n",
              "      <td>3.288611e+02</td>\n",
              "      <td>4.320451e+02</td>\n",
              "      <td>5.678277e+02</td>\n",
              "      <td>7.488432e+02</td>\n",
              "      <td>9.869320e+02</td>\n",
              "      <td>1.303583e+03</td>\n",
              "      <td>1.719964e+03</td>\n",
              "      <td>2.267588e+03</td>\n",
              "      <td>2.997152e+03</td>\n",
              "      <td>3.966817e+03</td>\n",
              "      <td>5.249021e+03</td>\n",
              "      <td>6.957452e+03</td>\n",
              "      <td>7.817022</td>\n",
              "      <td>4.759981</td>\n",
              "      <td>3.044400</td>\n",
              "      <td>1.636415e+09</td>\n",
              "      <td>0.477217</td>\n",
              "      <td>0.260262</td>\n",
              "      <td>32.109057</td>\n",
              "      <td>24.562371</td>\n",
              "      <td>4.800000</td>\n",
              "      <td>1.086943e-03</td>\n",
              "      <td>2.923550e-03</td>\n",
              "      <td>4.790000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>90.000000</td>\n",
              "      <td>3.067051</td>\n",
              "      <td>9.673304</td>\n",
              "      <td>3.061075</td>\n",
              "      <td>9.793875</td>\n",
              "      <td>0.555498</td>\n",
              "      <td>0.471748</td>\n",
              "      <td>1967.306361</td>\n",
              "      <td>3986.365713</td>\n",
              "      <td>8077.598849</td>\n",
              "      <td>16367.691242</td>\n",
              "      <td>33165.959538</td>\n",
              "      <td>67204.400168</td>\n",
              "      <td>136176.714463</td>\n",
              "      <td>275935.764856</td>\n",
              "      <td>559130.440376</td>\n",
              "      <td>1.132970e+06</td>\n",
              "      <td>2.295744e+06</td>\n",
              "      <td>4.651881e+06</td>\n",
              "      <td>9.426136e+06</td>\n",
              "      <td>1.910024e+07</td>\n",
              "      <td>3.870294e+07</td>\n",
              "      <td>7.842403e+07</td>\n",
              "      <td>1.589111e+08</td>\n",
              "      <td>3.220026e+08</td>\n",
              "      <td>6.524760e+08</td>\n",
              "      <td>1.322116e+09</td>\n",
              "      <td>2.679013e+09</td>\n",
              "      <td>6488.613461</td>\n",
              "      <td>13129.838719</td>\n",
              "      <td>26568.490455</td>\n",
              "      <td>53761.870207</td>\n",
              "      <td>108788.216366</td>\n",
              "      <td>220135.125033</td>\n",
              "      <td>445447.814958</td>\n",
              "      <td>901372.535715</td>\n",
              "      <td>1.823945e+06</td>\n",
              "      <td>3.690790e+06</td>\n",
              "      <td>7.468387e+06</td>\n",
              "      <td>1.511243e+07</td>\n",
              "      <td>3.058030e+07</td>\n",
              "      <td>6.187985e+07</td>\n",
              "      <td>1.252151e+08</td>\n",
              "      <td>2.533753e+08</td>\n",
              "      <td>5.127099e+08</td>\n",
              "      <td>1.037479e+09</td>\n",
              "      <td>2.099359e+09</td>\n",
              "      <td>4.248095e+09</td>\n",
              "      <td>8.596104e+09</td>\n",
              "      <td>30.137493</td>\n",
              "      <td>19.028741</td>\n",
              "      <td>3.944800</td>\n",
              "      <td>3.313170e+11</td>\n",
              "      <td>2.960886</td>\n",
              "      <td>1.347404</td>\n",
              "      <td>100.110051</td>\n",
              "      <td>49.706596</td>\n",
              "      <td>7.300000</td>\n",
              "      <td>1.056601e-01</td>\n",
              "      <td>1.328281e-01</td>\n",
              "      <td>7.300000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             T_Days         b_lik  ...    Actual Mag           mag\n",
              "count  29124.000000  29124.000000  ...  29124.000000  29124.000000\n",
              "mean      22.685586      1.182601  ...      4.291723      0.151490\n",
              "std       17.180184      0.284475  ...      0.683513      0.358532\n",
              "min        1.000000      0.325363  ...      2.840000      0.000000\n",
              "25%        7.000000      0.987931  ...      3.790000      0.000000\n",
              "50%       20.000000      1.148320  ...      4.180000      0.000000\n",
              "75%       34.000000      1.339588  ...      4.790000      0.000000\n",
              "max       90.000000      3.067051  ...      7.300000      1.000000\n",
              "\n",
              "[8 rows x 62 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mpnw_t2uREAU"
      },
      "source": [
        "### Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y0jwHb9mQ9M5"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, y_train, X_test, y_test = train_test_split(features,target, test_size = 0.3,random_state =1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hwclGXC1RBOI",
        "outputId": "c8ea96b4-effc-4bfb-8c65-e301f08e5331"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "lr = LogisticRegression()\n",
        "lr.fit(X_train,X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CaT_aDUVRLow",
        "outputId": "6a677018-86f1-4050-c9ae-1143f39d37a3"
      },
      "source": [
        "pred = lr.predict(y_train)\n",
        "from sklearn.metrics import matthews_corrcoef,accuracy_score\n",
        "print('Accuracy: ',accuracy_score(y_test, pred)*100,'%')\n",
        "print('MCC score: ',matthews_corrcoef(y_test, pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  84.65323872739758 %\n",
            "MCC score:  0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 889
        },
        "id": "y8RsYbOgRN-B",
        "outputId": "b369535b-d0d0-48ef-a0cf-714e8a046711"
      },
      "source": [
        "plt.figure(figsize = (14,14))\n",
        "plt.title('correlation')\n",
        "corr = sc_df.corr()\n",
        "sns.heatmap(corr,xticklabels=corr.columns,yticklabels=corr.columns,linewidths=.1,cmap=\"Reds\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x1008 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-_F6U20ORdHC"
      },
      "source": [
        "### Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KDLOSDrzRU90",
        "outputId": "c9c498b0-187c-437e-9b5e-5bab0d9e2041"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf =  RandomForestClassifier(n_estimators=100, n_jobs=-1)\n",
        "rf.fit(X_train,X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                       n_jobs=-1, oob_score=False, random_state=None, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JfGOy-KURlK7",
        "outputId": "496d250e-c941-4bb4-c17c-c96737ba613f"
      },
      "source": [
        "pred = rf.predict(y_train)\n",
        "from sklearn.metrics import matthews_corrcoef,accuracy_score\n",
        "print('Accuracy: ',accuracy_score(y_test, pred)*100,'%')\n",
        "print('MCC score: ',matthews_corrcoef(y_test, pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  96.01739528496223 %\n",
            "MCC score:  0.8402069520937141\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jx2oTIiTRvuR"
      },
      "source": [
        "### XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WqYVIu9eRpsZ",
        "outputId": "be6b15f2-f799-4400-88d7-2b4230b7d6e2"
      },
      "source": [
        "MAX_ROUNDS = 1000\n",
        "EARLY_STOP = 50\n",
        "from xgboost import XGBClassifier\n",
        "xg = XGBClassifier(MAX_ROUNDS,early_stopping_rounds=EARLY_STOP,maximize=True)\n",
        "xg.fit(X_train,X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=1, early_stopping_rounds=50,\n",
              "              gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=1000,\n",
              "              maximize=True, min_child_weight=1, missing=None, n_estimators=100,\n",
              "              n_jobs=1, nthread=None, objective='binary:logistic',\n",
              "              random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
              "              seed=None, silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I6JqbJSJR1eo",
        "outputId": "f7f13a67-14ea-4cb8-f64e-8667bd40848d"
      },
      "source": [
        "pred = xg.predict(y_train)\n",
        "from sklearn.metrics import matthews_corrcoef,accuracy_score\n",
        "print('Accuracy: ',accuracy_score(y_test, pred)*100,'%')\n",
        "print('MCC score: ',matthews_corrcoef(y_test, pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  97.72259098191806 %\n",
            "MCC score:  0.9104558000383849\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jK9R5CFISIha"
      },
      "source": [
        "### PCA with Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wiroIpaVSD9i",
        "outputId": "6b8a4718-ba40-4b81-c836-94b0c0d46abd"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "from statistics import mean\n",
        "avgaccs=[]\n",
        "avgmccs=[]\n",
        "for i in range(1,29):\n",
        "  pca=PCA(n_components=i)\n",
        "  pca.fit(features)\n",
        "  x_pca=pca.transform(features)\n",
        "  acc,mcc=[],[]\n",
        "  for j in range(0,10):\n",
        "    X_train, y_train, X_test, y_test = train_test_split(x_pca,target, test_size = 0.3,stratify=target)\n",
        "    lr = LogisticRegression()\n",
        "    lr.fit(X_train,X_test)\n",
        "    pred = lr.predict(y_train)\n",
        "    acc.append(accuracy_score(y_test, pred))\n",
        "    mcc.append(matthews_corrcoef(y_test, pred))\n",
        "  print('For ',i,'number of features: ',round(mean(acc),4)*100,round(mean(mcc),4))\n",
        "  avgaccs.append(round(mean(acc),4)*100)\n",
        "  avgmccs.append(round(mean(mcc),4))\n",
        "print(max(avgaccs))\n",
        "print(avgaccs.index(max(avgaccs)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For  1 number of features:  77.37 0.2491\n",
            "For  2 number of features:  77.59 0.2519\n",
            "For  3 number of features:  77.42999999999999 0.2482\n",
            "For  4 number of features:  78.08 0.2596\n",
            "For  5 number of features:  77.68 0.2564\n",
            "For  6 number of features:  78.69 0.2552\n",
            "For  7 number of features:  78.08 0.2542\n",
            "For  8 number of features:  77.8 0.2549\n",
            "For  9 number of features:  77.91 0.2541\n",
            "For  10 number of features:  77.42 0.2507\n",
            "For  11 number of features:  77.51 0.2461\n",
            "For  12 number of features:  78.62 0.2574\n",
            "For  13 number of features:  77.97 0.2547\n",
            "For  14 number of features:  77.84 0.2491\n",
            "For  15 number of features:  77.53 0.2489\n",
            "For  16 number of features:  78.01 0.2533\n",
            "For  17 number of features:  78.4 0.2523\n",
            "For  18 number of features:  77.46 0.2485\n",
            "For  19 number of features:  77.75 0.2595\n",
            "For  20 number of features:  77.45 0.251\n",
            "For  21 number of features:  78.02 0.2548\n",
            "For  22 number of features:  77.45 0.2555\n",
            "For  23 number of features:  77.34 0.2479\n",
            "For  24 number of features:  78.94 0.2561\n",
            "For  25 number of features:  77.97 0.2566\n",
            "For  26 number of features:  77.49000000000001 0.2515\n",
            "For  27 number of features:  77.7 0.2522\n",
            "For  28 number of features:  78.11 0.2491\n",
            "78.94\n",
            "23\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hB-6vuAESPKz"
      },
      "source": [
        "pca=PCA(n_components=23) #Using 23 features for PCA\n",
        "pca.fit(features)\n",
        "x_pca=pca.transform(features)\n",
        "X_train, y_train, X_test, y_test = train_test_split(x_pca,target, test_size = 0.3,random_state=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-F3p3BEGTFh0",
        "outputId": "fd68deaa-8058-40a8-d1b0-189e7e75eb49"
      },
      "source": [
        "  lr = LogisticRegression()\n",
        "  lr.fit(X_train,X_test)\n",
        "  pred = lr.predict(y_train)\n",
        "  from sklearn.metrics import matthews_corrcoef,accuracy_score\n",
        "print('Accuracy: ',accuracy_score(y_test, pred)*100,'%')\n",
        "print('MCC score: ',matthews_corrcoef(y_test, pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  78.1528953994049 %\n",
            "MCC score:  0.26499968487945874\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3u0Ut52KTME9"
      },
      "source": [
        "### PCA with Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vwf9vdixTJPv",
        "outputId": "4ebcc468-d145-4e60-ee32-e3a76a2687d2"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "from statistics import mean\n",
        "avgaccs=[]\n",
        "avgmccs=[]\n",
        "for i in range(1,30):\n",
        "  pca=PCA(n_components=i)\n",
        "  pca.fit(features)\n",
        "  x_pca=pca.transform(features)\n",
        "  acc,mcc=[],[]\n",
        "  for j in range(0,10):\n",
        "    X_train, y_train, X_test, y_test = train_test_split(x_pca,target, test_size = 0.3,stratify=target)\n",
        "    rf =  RandomForestClassifier(n_estimators=100, n_jobs=-1)\n",
        "    rf.fit(X_train,X_test)\n",
        "    pred = rf.predict(y_train)\n",
        "    acc.append(accuracy_score(y_test, pred))\n",
        "    mcc.append(matthews_corrcoef(y_test, pred))\n",
        "  print('For ',i,'number of features: ',round(mean(acc),4)*100,round(mean(mcc),4))\n",
        "  avgaccs.append(round(mean(acc),4)*100)\n",
        "  avgmccs.append(round(mean(mcc),4))\n",
        "print(max(avgaccs))\n",
        "print(avgaccs.index(max(avgaccs)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For  1 number of features:  78.62 0.17\n",
            "For  2 number of features:  85.94000000000001 0.3681\n",
            "For  3 number of features:  88.21 0.4729\n",
            "For  4 number of features:  89.05999999999999 0.5222\n",
            "For  5 number of features:  89.86 0.557\n",
            "For  6 number of features:  90.23 0.5744\n",
            "For  7 number of features:  90.53999999999999 0.5889\n",
            "For  8 number of features:  91.18 0.6183\n",
            "For  9 number of features:  91.47 0.6333\n",
            "For  10 number of features:  92.05 0.6593\n",
            "For  11 number of features:  92.15 0.6637\n",
            "For  12 number of features:  92.89 0.6982\n",
            "For  13 number of features:  93.08999999999999 0.7075\n",
            "For  14 number of features:  93.63 0.7323\n",
            "For  15 number of features:  93.82000000000001 0.7408\n",
            "For  16 number of features:  94.55 0.7739\n",
            "For  17 number of features:  94.56 0.7747\n",
            "For  18 number of features:  94.88 0.7886\n",
            "For  19 number of features:  95.41 0.8121\n",
            "For  20 number of features:  95.54 0.8175\n",
            "For  21 number of features:  95.72 0.8256\n",
            "For  22 number of features:  95.85000000000001 0.8312\n",
            "For  23 number of features:  96.04 0.8394\n",
            "For  24 number of features:  96.1 0.842\n",
            "For  25 number of features:  96.2 0.8461\n",
            "For  26 number of features:  96.17999999999999 0.8455\n",
            "For  27 number of features:  96.39999999999999 0.8548\n",
            "For  28 number of features:  96.45 0.8569\n",
            "For  29 number of features:  96.64 0.865\n",
            "96.64\n",
            "28\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oDzTV7xfTWmX"
      },
      "source": [
        "pca=PCA(n_components=28) #Using 28 features for PCA\n",
        "pca.fit(features)\n",
        "x_pca=pca.transform(features)\n",
        "X_train, y_train, X_test, y_test = train_test_split(x_pca,target, test_size = 0.3,random_state=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QBykUQN8ZL4L",
        "outputId": "01ab4f3b-a1db-46fa-ed61-b40dc36a2c7f"
      },
      "source": [
        "rf =  RandomForestClassifier(n_estimators=100, n_jobs=-1)\n",
        "rf.fit(X_train,X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                       n_jobs=-1, oob_score=False, random_state=None, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lIsAX7mLZMrw",
        "outputId": "1e4fc1e1-3af2-4c91-a1c5-a25eef70130f"
      },
      "source": [
        "pred = rf.predict(y_train)\n",
        "from sklearn.metrics import matthews_corrcoef,accuracy_score\n",
        "print('Accuracy: ',accuracy_score(y_test, pred)*100,'%')\n",
        "print('MCC score: ',matthews_corrcoef(y_test, pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  96.1547264820325 %\n",
            "MCC score:  0.8461630995034547\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZaX1MWb-bJkZ"
      },
      "source": [
        "### PCA WITH XGBOOST"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hiRA9bN3bHMx",
        "outputId": "3b8ceea8-98b2-4c1c-c8be-2d900fbb9ff5"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "from statistics import mean\n",
        "avgaccs=[]\n",
        "avgmccs=[]\n",
        "for i in range(1,30):\n",
        "  pca=PCA(n_components=i)\n",
        "  pca.fit(features)\n",
        "  x_pca=pca.transform(features)\n",
        "  acc,mcc=[],[]\n",
        "  for j in range(0,1):\n",
        "    X_train, y_train, X_test, y_test = train_test_split(x_pca,target, test_size = 0.3,stratify=target)\n",
        "    xg = XGBClassifier(MAX_ROUNDS,early_stopping_rounds=EARLY_STOP,maximize=True)\n",
        "    xg.fit(X_train,X_test)\n",
        "    pred = xg.predict(y_train)\n",
        "    acc.append(accuracy_score(y_test, pred))\n",
        "    mcc.append(matthews_corrcoef(y_test, pred))\n",
        "  print('For ',i,'number of features: ',round(mean(acc),4)*100,round(mean(mcc),4))\n",
        "  avgaccs.append(round(mean(acc),4)*100)\n",
        "  avgmccs.append(round(mean(mcc),4))\n",
        "print(max(avgaccs))\n",
        "print(avgaccs.index(max(avgaccs)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For  1 number of features:  83.67 0.2518\n",
            "For  2 number of features:  85.28999999999999 0.343\n",
            "For  3 number of features:  88.24 0.4902\n",
            "For  4 number of features:  89.3 0.5377\n",
            "For  5 number of features:  89.73 0.5642\n",
            "For  6 number of features:  89.89 0.5656\n",
            "For  7 number of features:  90.5 0.5934\n",
            "For  8 number of features:  91.43 0.6334\n",
            "For  9 number of features:  91.7 0.6477\n",
            "For  10 number of features:  92.30000000000001 0.6749\n",
            "For  11 number of features:  92.65 0.6911\n",
            "For  12 number of features:  93.05 0.7072\n",
            "For  13 number of features:  93.43 0.7247\n",
            "For  14 number of features:  94.31 0.764\n",
            "For  15 number of features:  94.51 0.7728\n",
            "For  16 number of features:  94.87 0.7888\n",
            "For  17 number of features:  95.25 0.8054\n",
            "For  18 number of features:  95.65 0.8225\n",
            "For  19 number of features:  96.11 0.8421\n",
            "For  20 number of features:  96.34 0.852\n",
            "For  21 number of features:  96.66 0.8654\n",
            "For  22 number of features:  96.8 0.8711\n",
            "For  23 number of features:  96.74000000000001 0.8687\n",
            "For  24 number of features:  96.94 0.8775\n",
            "For  25 number of features:  97.06 0.882\n",
            "For  26 number of features:  96.99 0.8792\n",
            "For  27 number of features:  97.55 0.9023\n",
            "For  28 number of features:  97.21 0.8882\n",
            "For  29 number of features:  97.48 0.8995\n",
            "97.55\n",
            "26\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21hix1z5bIon"
      },
      "source": [
        "pca=PCA(n_components=26) #Using 28 features for PCA\n",
        "pca.fit(features)\n",
        "x_pca=pca.transform(features)\n",
        "X_train, y_train, X_test, y_test = train_test_split(x_pca,target, test_size = 0.3,random_state=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CiR12wrrbgBr",
        "outputId": "b22f6d07-9a09-45c4-a304-1571f4f32ada"
      },
      "source": [
        "xg = XGBClassifier(MAX_ROUNDS,early_stopping_rounds=EARLY_STOP,maximize=True)\n",
        "xg.fit(X_train,X_test)\n",
        "pred = xg.predict(y_train)\n",
        "from sklearn.metrics import matthews_corrcoef,accuracy_score\n",
        "print('Accuracy: ',accuracy_score(y_test, pred)*100,'%')\n",
        "print('MCC score: ',matthews_corrcoef(y_test, pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  96.76127260242619 %\n",
            "MCC score:  0.8711130019069712\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MxQ48gBRcLy5"
      },
      "source": [
        "### MRMR with XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zKq-fF_CcIyc"
      },
      "source": [
        "from skfeature.function.information_theoretical_based import LCSI\n",
        "def mrmr(X, y,i,**kwargs):\n",
        "    \"\"\"\n",
        "    This function implements the MRMR feature selection\n",
        "    Input\n",
        "    -----\n",
        "    X: {numpy array}, shape (n_samples, n_features)\n",
        "        input data, guaranteed to be discrete\n",
        "    y: {numpy array}, shape (n_samples,)\n",
        "        input class labels\n",
        "    kwargs: {dictionary}\n",
        "        n_selected_features: {int}\n",
        "            number of features to select\n",
        "    Output\n",
        "    ------\n",
        "    F: {numpy array}, shape (n_features,)\n",
        "        index of selected features, F[0] is the most important feature\n",
        "    J_CMI: {numpy array}, shape: (n_features,)\n",
        "        corresponding objective function value of selected features\n",
        "    MIfy: {numpy array}, shape: (n_features,)\n",
        "        corresponding mutual information between selected features and response\n",
        "    Reference\n",
        "    ---------\n",
        "    Brown, Gavin et al. \"Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection.\" JMLR 2012.\n",
        "    \"\"\"\n",
        "    if 'n_selected_features' in kwargs.keys():\n",
        "        n_selected_features = kwargs['n_selected_features']\n",
        "        F, J_CMI, MIfy = LCSI.lcsi(X, y, gamma=0, function_name='MRMR', n_selected_features=n_selected_features)\n",
        "    else:\n",
        "        F, J_CMI, MIfy = LCSI.lcsi(X, y, gamma=0, function_name='MRMR', n_selected_features=i)\n",
        "    return F"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5dNNIs1odqAT"
      },
      "source": [
        "X_train, y_train, X_test, y_test = train_test_split(features,target, test_size = 0.3,random_state=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tihG1t83du2e",
        "outputId": "6f98c1c3-a572-416f-e292-7dc46014c844"
      },
      "source": [
        "F = mrmr(X_train,X_test,60)\n",
        "print(F)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 6  0 57 49 51  1  2 58 50  3 59  4 54 56 28 30 29 33 48 35 34 46 31 53\n",
            " 32 36  7 37 38 39  8 40 41 42 43  9 44 45 47 10 11 15 12 13 14 16 18 20\n",
            " 17 19 26 22 24 21 25 27 23 55  5 52]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EFDfOnQLdyd0",
        "outputId": "b861265e-f880-4729-f81b-f23d188e91fd"
      },
      "source": [
        "acc = []\n",
        "mcc=[]\n",
        "MAX_ROUNDS = 1000 #lgb iterations\n",
        "EARLY_STOP = 50\n",
        "for i in range(1,61):\n",
        "  features2 = sc_df.iloc[:,F[0:i]].to_numpy()\n",
        "  #print(features2.shape)\n",
        "  X_train, y_train, X_test, y_test = train_test_split(features2,target, test_size = 0.3)\n",
        "  print(X_train.shape,X_test.shape)\n",
        "  from xgboost import XGBClassifier\n",
        "  xg = XGBClassifier(MAX_ROUNDS,early_stopping_rounds=EARLY_STOP,maximize=True)\n",
        "  xg.fit(X_train,X_test)\n",
        "  pred = xg.predict(y_train)\n",
        "  from sklearn.metrics import accuracy_score,confusion_matrix,r2_score\n",
        "  from sklearn.metrics import matthews_corrcoef\n",
        "  print(accuracy_score(y_test, pred), matthews_corrcoef(y_test, pred))\n",
        "  acc.append(accuracy_score(y_test, pred))\n",
        "  mcc.append(matthews_corrcoef(y_test, pred))\n",
        "print(acc)\n",
        "print(max(acc))\n",
        "print(acc.index(max(acc)))\n",
        "print(mcc[acc.index(max(acc))])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(20386, 1) (20386,)\n",
            "0.8187228198672465 0.06976008503361282\n",
            "(20386, 2) (20386,)\n",
            "0.8529411764705882 0.3329572488016716\n",
            "(20386, 3) (20386,)\n",
            "0.9729915312428473 0.8919221357753078\n",
            "(20386, 4) (20386,)\n",
            "0.9684138246738384 0.8716077789646539\n",
            "(20386, 5) (20386,)\n",
            "0.9761959258411536 0.908620477113298\n",
            "(20386, 6) (20386,)\n",
            "0.977454795147631 0.910447520140796\n",
            "(20386, 7) (20386,)\n",
            "0.9735637445639734 0.8931295042557217\n",
            "(20386, 8) (20386,)\n",
            "0.9756237125200274 0.9047812885444709\n",
            "(20386, 9) (20386,)\n",
            "0.9735637445639734 0.8945468347832496\n",
            "(20386, 10) (20386,)\n",
            "0.9766536964980544 0.90965417914823\n",
            "(20386, 11) (20386,)\n",
            "0.9842069123369191 0.9362881759888892\n",
            "(20386, 12) (20386,)\n",
            "0.9790569924467841 0.9159774788852402\n",
            "(20386, 13) (20386,)\n",
            "0.9804303044174868 0.922995806219728\n",
            "(20386, 14) (20386,)\n",
            "0.9816891737239642 0.9291646345124777\n",
            "(20386, 15) (20386,)\n",
            "0.9829480430304417 0.9335176294523854\n",
            "(20386, 16) (20386,)\n",
            "0.9812314030670634 0.9260253785278505\n",
            "(20386, 17) (20386,)\n",
            "0.9823758297093156 0.9292293557673496\n",
            "(20386, 18) (20386,)\n",
            "0.9787136644541085 0.9183633399032737\n",
            "(20386, 19) (20386,)\n",
            "0.9787136644541085 0.9167578174076021\n",
            "(20386, 20) (20386,)\n",
            "0.9828336003662165 0.9291782045445572\n",
            "(20386, 21) (20386,)\n",
            "0.980544747081712 0.9209421206014825\n",
            "(20386, 22) (20386,)\n",
            "0.9827191577019913 0.9313124248093632\n",
            "(20386, 23) (20386,)\n",
            "0.9806591897459372 0.9219268218047457\n",
            "(20386, 24) (20386,)\n",
            "0.9832913710231174 0.934463542679758\n",
            "(20386, 25) (20386,)\n",
            "0.9829480430304417 0.9327799834271027\n",
            "(20386, 26) (20386,)\n",
            "0.9802014190890364 0.9224752930060368\n",
            "(20386, 27) (20386,)\n",
            "0.9821469443808651 0.9291569442541264\n",
            "(20386, 28) (20386,)\n",
            "0.9813458457312886 0.9261677350748626\n",
            "(20386, 29) (20386,)\n",
            "0.9823758297093156 0.9299902633118609\n",
            "(20386, 30) (20386,)\n",
            "0.9796292057679102 0.9205857977876853\n",
            "(20386, 31) (20386,)\n",
            "0.9813458457312886 0.9246415525733304\n",
            "(20386, 32) (20386,)\n",
            "0.9822613870450904 0.9293371935556005\n",
            "(20386, 33) (20386,)\n",
            "0.9813458457312886 0.9250692329793073\n",
            "(20386, 34) (20386,)\n",
            "0.9820325017166399 0.9294626922778743\n",
            "(20386, 35) (20386,)\n",
            "0.9811169604028381 0.9260469183907662\n",
            "(20386, 36) (20386,)\n",
            "0.9818036163881895 0.9294564081282038\n",
            "(20386, 37) (20386,)\n",
            "0.9810025177386129 0.926066197678744\n",
            "(20386, 38) (20386,)\n",
            "0.9821469443808651 0.9283119423141704\n",
            "(20386, 39) (20386,)\n",
            "0.981574731059739 0.9286040309316711\n",
            "(20386, 40) (20386,)\n",
            "0.9812314030670634 0.9235433205950316\n",
            "(20386, 41) (20386,)\n",
            "0.9811169604028381 0.9270870319421627\n",
            "(20386, 42) (20386,)\n",
            "0.9776836804760815 0.9127638534587136\n",
            "(20386, 43) (20386,)\n",
            "0.9800869764248111 0.9203965355973147\n",
            "(20386, 44) (20386,)\n",
            "0.9787136644541085 0.9156295375891433\n",
            "(20386, 45) (20386,)\n",
            "0.9812314030670634 0.9254260928536955\n",
            "(20386, 46) (20386,)\n",
            "0.9787136644541085 0.914853628301399\n",
            "(20386, 47) (20386,)\n",
            "0.9822613870450904 0.9291128837056073\n",
            "(20386, 48) (20386,)\n",
            "0.9820325017166399 0.9293164186230363\n",
            "(20386, 49) (20386,)\n",
            "0.9806591897459372 0.9243566865489921\n",
            "(20386, 50) (20386,)\n",
            "0.9818036163881895 0.925902803978272\n",
            "(20386, 51) (20386,)\n",
            "0.9816891737239642 0.9255086231255246\n",
            "(20386, 52) (20386,)\n",
            "0.9806591897459372 0.9220199951477502\n",
            "(20386, 53) (20386,)\n",
            "0.9822613870450904 0.9294524780114205\n",
            "(20386, 54) (20386,)\n",
            "0.9829480430304417 0.9325005851997831\n",
            "(20386, 55) (20386,)\n",
            "0.9812314030670634 0.9263245236458073\n",
            "(20386, 56) (20386,)\n",
            "0.9767681391622797 0.9078743034825941\n",
            "(20386, 57) (20386,)\n",
            "0.9794003204394598 0.9168430160303631\n",
            "(20386, 58) (20386,)\n",
            "0.9821469443808651 0.9272752565894715\n",
            "(20386, 59) (20386,)\n",
            "0.9813458457312886 0.9270546729766458\n",
            "(20386, 60) (20386,)\n",
            "0.9810025177386129 0.9210232150131024\n",
            "[0.8187228198672465, 0.8529411764705882, 0.9729915312428473, 0.9684138246738384, 0.9761959258411536, 0.977454795147631, 0.9735637445639734, 0.9756237125200274, 0.9735637445639734, 0.9766536964980544, 0.9842069123369191, 0.9790569924467841, 0.9804303044174868, 0.9816891737239642, 0.9829480430304417, 0.9812314030670634, 0.9823758297093156, 0.9787136644541085, 0.9787136644541085, 0.9828336003662165, 0.980544747081712, 0.9827191577019913, 0.9806591897459372, 0.9832913710231174, 0.9829480430304417, 0.9802014190890364, 0.9821469443808651, 0.9813458457312886, 0.9823758297093156, 0.9796292057679102, 0.9813458457312886, 0.9822613870450904, 0.9813458457312886, 0.9820325017166399, 0.9811169604028381, 0.9818036163881895, 0.9810025177386129, 0.9821469443808651, 0.981574731059739, 0.9812314030670634, 0.9811169604028381, 0.9776836804760815, 0.9800869764248111, 0.9787136644541085, 0.9812314030670634, 0.9787136644541085, 0.9822613870450904, 0.9820325017166399, 0.9806591897459372, 0.9818036163881895, 0.9816891737239642, 0.9806591897459372, 0.9822613870450904, 0.9829480430304417, 0.9812314030670634, 0.9767681391622797, 0.9794003204394598, 0.9821469443808651, 0.9813458457312886, 0.9810025177386129]\n",
            "0.9842069123369191\n",
            "10\n",
            "0.9362881759888892\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "vntRKzA0g6sU",
        "outputId": "8a1999ad-d9b0-49a0-bb16-aa1133973149"
      },
      "source": [
        "avgaccs=[]\n",
        "avgmccs=[]\n",
        "for i in range(1,61):\n",
        "  features2 = sc_df.iloc[:,F[0:i]].to_numpy()\n",
        "  acc,mcc=[],[]\n",
        "  for j in range(0,10):\n",
        "    X_train, y_train, X_test, y_test = train_test_split(features2,target, test_size = 0.3,stratify=target)\n",
        "    xg = XGBClassifier(MAX_ROUNDS,early_stopping_rounds=EARLY_STOP,maximize=True)\n",
        "    xg.fit(X_train,X_test)\n",
        "    pred = xg.predict(y_train)\n",
        "    acc.append(accuracy_score(y_test, pred))\n",
        "    mcc.append(matthews_corrcoef(y_test, pred))\n",
        "  print('For ',i,'number of features: ',round(mean(acc),4)*100,round(mean(mcc),4))\n",
        "  avgaccs.append(round(mean(acc),4)*100)\n",
        "  avgmccs.append(round(mean(mcc),4))\n",
        "print(max(avgaccs))\n",
        "print(avgaccs.index(max(avgaccs)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For  1 number of features:  81.08999999999999 0.0653\n",
            "For  2 number of features:  84.99 0.3383\n",
            "For  3 number of features:  96.97 0.8786\n",
            "For  4 number of features:  97.06 0.8823\n",
            "For  5 number of features:  97.71 0.9089\n",
            "For  6 number of features:  97.66 0.9066\n",
            "For  7 number of features:  97.67 0.9073\n",
            "For  8 number of features:  97.57000000000001 0.9029\n",
            "For  9 number of features:  97.55 0.9022\n",
            "For  10 number of features:  97.89999999999999 0.9165\n",
            "For  11 number of features:  97.88 0.9159\n",
            "For  12 number of features:  97.8 0.9126\n",
            "For  13 number of features:  98.17 0.9276\n",
            "For  14 number of features:  98.2 0.9287\n",
            "For  15 number of features:  98.29 0.9322\n",
            "For  16 number of features:  98.16 0.927\n",
            "For  17 number of features:  98.13 0.9258\n",
            "For  18 number of features:  98.21 0.9293\n",
            "For  19 number of features:  98.18 0.928\n",
            "For  20 number of features:  98.11999999999999 0.9255\n",
            "For  21 number of features:  98.18 0.9279\n",
            "For  22 number of features:  98.1 0.9247\n",
            "For  23 number of features:  98.13 0.9257\n",
            "For  24 number of features:  98.08 0.9239\n",
            "For  25 number of features:  98.14 0.9264\n",
            "For  26 number of features:  98.17 0.9276\n",
            "For  27 number of features:  98.11 0.9251\n",
            "For  28 number of features:  98.17 0.9274\n",
            "For  29 number of features:  98.04 0.9224\n",
            "For  30 number of features:  98.1 0.9249\n",
            "For  31 number of features:  98.09 0.9243\n",
            "For  32 number of features:  98.08 0.9238\n",
            "For  33 number of features:  98.13 0.9259\n",
            "For  34 number of features:  98.1 0.9248\n",
            "For  35 number of features:  98.13 0.9261\n",
            "For  36 number of features:  98.09 0.9243\n",
            "For  37 number of features:  98.07000000000001 0.9237\n",
            "For  38 number of features:  98.07000000000001 0.9236\n",
            "For  39 number of features:  98.07000000000001 0.9235\n",
            "For  40 number of features:  98.13 0.9258\n",
            "For  41 number of features:  98.15 0.9268\n",
            "For  42 number of features:  98.05 0.9226\n",
            "For  43 number of features:  98.16 0.9272\n",
            "For  44 number of features:  98.15 0.9266\n",
            "For  45 number of features:  98.11999999999999 0.9255\n",
            "For  46 number of features:  98.08 0.9239\n",
            "For  47 number of features:  98.1 0.9248\n",
            "For  48 number of features:  98.09 0.9245\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-59-991e84a4c8da>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstratify\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mxg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mXGBClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMAX_ROUNDS\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mearly_stopping_rounds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEARLY_STOP\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmaximize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mxg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX_test\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     10\u001b[0m     \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0macc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred\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/python3.6/dist-packages/xgboost/sklearn.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set, callbacks)\u001b[0m\n\u001b[1;32m    730\u001b[0m                               \u001b[0mevals_result\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mevals_result\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeval\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    731\u001b[0m                               \u001b[0mverbose_eval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxgb_model\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxgb_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 732\u001b[0;31m                               callbacks=callbacks)\n\u001b[0m\u001b[1;32m    733\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    734\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobjective\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxgb_options\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"objective\"\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/python3.6/dist-packages/xgboost/training.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks, learning_rates)\u001b[0m\n\u001b[1;32m    214\u001b[0m                            \u001b[0mevals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mevals\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m                            \u001b[0mobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeval\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m                            xgb_model=xgb_model, callbacks=callbacks)\n\u001b[0m\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/xgboost/training.py\u001b[0m in \u001b[0;36m_train_internal\u001b[0;34m(params, dtrain, num_boost_round, evals, obj, feval, xgb_model, callbacks)\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0;31m# Skip the first update if it is a recovery step.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mversion\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m             \u001b[0mbst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\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     75\u001b[0m             \u001b[0mbst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_rabit_checkpoint\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[1;32m     76\u001b[0m             \u001b[0mversion\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, dtrain, iteration, fobj)\u001b[0m\n\u001b[1;32m   1107\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfobj\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1108\u001b[0m             _check_call(_LIB.XGBoosterUpdateOneIter(self.handle, ctypes.c_int(iteration),\n\u001b[0;32m-> 1109\u001b[0;31m                                                     dtrain.handle))\n\u001b[0m\u001b[1;32m   1110\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   1111\u001b[0m             \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wc5sb0P6o_-6"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}