SVM_voice_data.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/anirudh-bukka/DataAnalysis-SupervisedML/blob/main/SVM_voice_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "e53ae336",
      "metadata": {
        "id": "e53ae336"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ixELzK8NKAyz",
        "outputId": "64ec2e3c-326c-4668-9aa9-0ee64748ef83"
      },
      "id": "ixELzK8NKAyz",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "4fa7c42c",
      "metadata": {
        "id": "4fa7c42c"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Ojas ML/Material/SVM/voice.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f281224e",
      "metadata": {
        "id": "f281224e"
      },
      "source": [
        "# Attributes and correlation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "85b5a7d6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "85b5a7d6",
        "outputId": "c220002f-5a4f-4343-a2ca-9de27d90ce0f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          meanfreq        sd    median       Q25       Q75       IQR  \\\n",
              "meanfreq  1.000000 -0.739039  0.925445  0.911416  0.740997 -0.627605   \n",
              "sd       -0.739039  1.000000 -0.562603 -0.846931 -0.161076  0.874660   \n",
              "median    0.925445 -0.562603  1.000000  0.774922  0.731849 -0.477352   \n",
              "Q25       0.911416 -0.846931  0.774922  1.000000  0.477140 -0.874189   \n",
              "Q75       0.740997 -0.161076  0.731849  0.477140  1.000000  0.009636   \n",
              "IQR      -0.627605  0.874660 -0.477352 -0.874189  0.009636  1.000000   \n",
              "skew     -0.322327  0.314597 -0.257407 -0.319475 -0.206339  0.249497   \n",
              "kurt     -0.316036  0.346241 -0.243382 -0.350182 -0.148881  0.316185   \n",
              "sp.ent   -0.601203  0.716620 -0.502005 -0.648126 -0.174905  0.640813   \n",
              "sfm      -0.784332  0.838086 -0.661690 -0.766875 -0.378198  0.663601   \n",
              "mode      0.687715 -0.529150  0.677433  0.591277  0.486857 -0.403764   \n",
              "centroid  1.000000 -0.739039  0.925445  0.911416  0.740997 -0.627605   \n",
              "meanfun   0.460844 -0.466281  0.414909  0.545035  0.155091 -0.534462   \n",
              "minfun    0.383937 -0.345609  0.337602  0.320994  0.258002 -0.222680   \n",
              "maxfun    0.274004 -0.129662  0.251328  0.199841  0.285584 -0.069588   \n",
              "meandom   0.536666 -0.482726  0.455943  0.467403  0.359181 -0.333362   \n",
              "mindom    0.229261 -0.357667  0.191169  0.302255 -0.023750 -0.357037   \n",
              "maxdom    0.519528 -0.482278  0.438919  0.459683  0.335114 -0.337877   \n",
              "dfrange   0.515570 -0.475999  0.435621  0.454394  0.335648 -0.331563   \n",
              "modindx  -0.216979  0.122660 -0.213298 -0.141377 -0.216475  0.041252   \n",
              "\n",
              "              skew      kurt    sp.ent       sfm      mode  centroid  \\\n",
              "meanfreq -0.322327 -0.316036 -0.601203 -0.784332  0.687715  1.000000   \n",
              "sd        0.314597  0.346241  0.716620  0.838086 -0.529150 -0.739039   \n",
              "median   -0.257407 -0.243382 -0.502005 -0.661690  0.677433  0.925445   \n",
              "Q25      -0.319475 -0.350182 -0.648126 -0.766875  0.591277  0.911416   \n",
              "Q75      -0.206339 -0.148881 -0.174905 -0.378198  0.486857  0.740997   \n",
              "IQR       0.249497  0.316185  0.640813  0.663601 -0.403764 -0.627605   \n",
              "skew      1.000000  0.977020 -0.195459  0.079694 -0.434859 -0.322327   \n",
              "kurt      0.977020  1.000000 -0.127644  0.109884 -0.406722 -0.316036   \n",
              "sp.ent   -0.195459 -0.127644  1.000000  0.866411 -0.325298 -0.601203   \n",
              "sfm       0.079694  0.109884  0.866411  1.000000 -0.485913 -0.784332   \n",
              "mode     -0.434859 -0.406722 -0.325298 -0.485913  1.000000  0.687715   \n",
              "centroid -0.322327 -0.316036 -0.601203 -0.784332  0.687715  1.000000   \n",
              "meanfun  -0.167668 -0.194560 -0.513194 -0.421066  0.324771  0.460844   \n",
              "minfun   -0.216954 -0.203201 -0.305826 -0.362100  0.385467  0.383937   \n",
              "maxfun   -0.080861 -0.045667 -0.120738 -0.192369  0.172329  0.274004   \n",
              "meandom  -0.336848 -0.303234 -0.293562 -0.428442  0.491479  0.536666   \n",
              "mindom   -0.061608 -0.103313 -0.294869 -0.289593  0.198150  0.229261   \n",
              "maxdom   -0.305651 -0.274500 -0.324253 -0.436649  0.477187  0.519528   \n",
              "dfrange  -0.304640 -0.272729 -0.319054 -0.431580  0.473775  0.515570   \n",
              "modindx  -0.169325 -0.205539  0.198074  0.211477 -0.182344 -0.216979   \n",
              "\n",
              "           meanfun    minfun    maxfun   meandom    mindom    maxdom  \\\n",
              "meanfreq  0.460844  0.383937  0.274004  0.536666  0.229261  0.519528   \n",
              "sd       -0.466281 -0.345609 -0.129662 -0.482726 -0.357667 -0.482278   \n",
              "median    0.414909  0.337602  0.251328  0.455943  0.191169  0.438919   \n",
              "Q25       0.545035  0.320994  0.199841  0.467403  0.302255  0.459683   \n",
              "Q75       0.155091  0.258002  0.285584  0.359181 -0.023750  0.335114   \n",
              "IQR      -0.534462 -0.222680 -0.069588 -0.333362 -0.357037 -0.337877   \n",
              "skew     -0.167668 -0.216954 -0.080861 -0.336848 -0.061608 -0.305651   \n",
              "kurt     -0.194560 -0.203201 -0.045667 -0.303234 -0.103313 -0.274500   \n",
              "sp.ent   -0.513194 -0.305826 -0.120738 -0.293562 -0.294869 -0.324253   \n",
              "sfm      -0.421066 -0.362100 -0.192369 -0.428442 -0.289593 -0.436649   \n",
              "mode      0.324771  0.385467  0.172329  0.491479  0.198150  0.477187   \n",
              "centroid  0.460844  0.383937  0.274004  0.536666  0.229261  0.519528   \n",
              "meanfun   1.000000  0.339387  0.311950  0.270840  0.162163  0.277982   \n",
              "minfun    0.339387  1.000000  0.213987  0.375979  0.082015  0.317860   \n",
              "maxfun    0.311950  0.213987  1.000000  0.337553 -0.243426  0.355390   \n",
              "meandom   0.270840  0.375979  0.337553  1.000000  0.099656  0.812838   \n",
              "mindom    0.162163  0.082015 -0.243426  0.099656  1.000000  0.026640   \n",
              "maxdom    0.277982  0.317860  0.355390  0.812838  0.026640  1.000000   \n",
              "dfrange   0.275154  0.316486  0.359880  0.811304  0.008666  0.999838   \n",
              "modindx  -0.054858  0.002042 -0.363029 -0.180954  0.200212 -0.425531   \n",
              "\n",
              "           dfrange   modindx  \n",
              "meanfreq  0.515570 -0.216979  \n",
              "sd       -0.475999  0.122660  \n",
              "median    0.435621 -0.213298  \n",
              "Q25       0.454394 -0.141377  \n",
              "Q75       0.335648 -0.216475  \n",
              "IQR      -0.331563  0.041252  \n",
              "skew     -0.304640 -0.169325  \n",
              "kurt     -0.272729 -0.205539  \n",
              "sp.ent   -0.319054  0.198074  \n",
              "sfm      -0.431580  0.211477  \n",
              "mode      0.473775 -0.182344  \n",
              "centroid  0.515570 -0.216979  \n",
              "meanfun   0.275154 -0.054858  \n",
              "minfun    0.316486  0.002042  \n",
              "maxfun    0.359880 -0.363029  \n",
              "meandom   0.811304 -0.180954  \n",
              "mindom    0.008666  0.200212  \n",
              "maxdom    0.999838 -0.425531  \n",
              "dfrange   1.000000 -0.429266  \n",
              "modindx  -0.429266  1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d44e6394-aab4-4990-915b-0cc9e9794308\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>meanfreq</th>\n",
              "      <th>sd</th>\n",
              "      <th>median</th>\n",
              "      <th>Q25</th>\n",
              "      <th>Q75</th>\n",
              "      <th>IQR</th>\n",
              "      <th>skew</th>\n",
              "      <th>kurt</th>\n",
              "      <th>sp.ent</th>\n",
              "      <th>sfm</th>\n",
              "      <th>mode</th>\n",
              "      <th>centroid</th>\n",
              "      <th>meanfun</th>\n",
              "      <th>minfun</th>\n",
              "      <th>maxfun</th>\n",
              "      <th>meandom</th>\n",
              "      <th>mindom</th>\n",
              "      <th>maxdom</th>\n",
              "      <th>dfrange</th>\n",
              "      <th>modindx</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>meanfreq</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.739039</td>\n",
              "      <td>0.925445</td>\n",
              "      <td>0.911416</td>\n",
              "      <td>0.740997</td>\n",
              "      <td>-0.627605</td>\n",
              "      <td>-0.322327</td>\n",
              "      <td>-0.316036</td>\n",
              "      <td>-0.601203</td>\n",
              "      <td>-0.784332</td>\n",
              "      <td>0.687715</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.460844</td>\n",
              "      <td>0.383937</td>\n",
              "      <td>0.274004</td>\n",
              "      <td>0.536666</td>\n",
              "      <td>0.229261</td>\n",
              "      <td>0.519528</td>\n",
              "      <td>0.515570</td>\n",
              "      <td>-0.216979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sd</th>\n",
              "      <td>-0.739039</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.562603</td>\n",
              "      <td>-0.846931</td>\n",
              "      <td>-0.161076</td>\n",
              "      <td>0.874660</td>\n",
              "      <td>0.314597</td>\n",
              "      <td>0.346241</td>\n",
              "      <td>0.716620</td>\n",
              "      <td>0.838086</td>\n",
              "      <td>-0.529150</td>\n",
              "      <td>-0.739039</td>\n",
              "      <td>-0.466281</td>\n",
              "      <td>-0.345609</td>\n",
              "      <td>-0.129662</td>\n",
              "      <td>-0.482726</td>\n",
              "      <td>-0.357667</td>\n",
              "      <td>-0.482278</td>\n",
              "      <td>-0.475999</td>\n",
              "      <td>0.122660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>median</th>\n",
              "      <td>0.925445</td>\n",
              "      <td>-0.562603</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.774922</td>\n",
              "      <td>0.731849</td>\n",
              "      <td>-0.477352</td>\n",
              "      <td>-0.257407</td>\n",
              "      <td>-0.243382</td>\n",
              "      <td>-0.502005</td>\n",
              "      <td>-0.661690</td>\n",
              "      <td>0.677433</td>\n",
              "      <td>0.925445</td>\n",
              "      <td>0.414909</td>\n",
              "      <td>0.337602</td>\n",
              "      <td>0.251328</td>\n",
              "      <td>0.455943</td>\n",
              "      <td>0.191169</td>\n",
              "      <td>0.438919</td>\n",
              "      <td>0.435621</td>\n",
              "      <td>-0.213298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Q25</th>\n",
              "      <td>0.911416</td>\n",
              "      <td>-0.846931</td>\n",
              "      <td>0.774922</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.477140</td>\n",
              "      <td>-0.874189</td>\n",
              "      <td>-0.319475</td>\n",
              "      <td>-0.350182</td>\n",
              "      <td>-0.648126</td>\n",
              "      <td>-0.766875</td>\n",
              "      <td>0.591277</td>\n",
              "      <td>0.911416</td>\n",
              "      <td>0.545035</td>\n",
              "      <td>0.320994</td>\n",
              "      <td>0.199841</td>\n",
              "      <td>0.467403</td>\n",
              "      <td>0.302255</td>\n",
              "      <td>0.459683</td>\n",
              "      <td>0.454394</td>\n",
              "      <td>-0.141377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Q75</th>\n",
              "      <td>0.740997</td>\n",
              "      <td>-0.161076</td>\n",
              "      <td>0.731849</td>\n",
              "      <td>0.477140</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.009636</td>\n",
              "      <td>-0.206339</td>\n",
              "      <td>-0.148881</td>\n",
              "      <td>-0.174905</td>\n",
              "      <td>-0.378198</td>\n",
              "      <td>0.486857</td>\n",
              "      <td>0.740997</td>\n",
              "      <td>0.155091</td>\n",
              "      <td>0.258002</td>\n",
              "      <td>0.285584</td>\n",
              "      <td>0.359181</td>\n",
              "      <td>-0.023750</td>\n",
              "      <td>0.335114</td>\n",
              "      <td>0.335648</td>\n",
              "      <td>-0.216475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IQR</th>\n",
              "      <td>-0.627605</td>\n",
              "      <td>0.874660</td>\n",
              "      <td>-0.477352</td>\n",
              "      <td>-0.874189</td>\n",
              "      <td>0.009636</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.249497</td>\n",
              "      <td>0.316185</td>\n",
              "      <td>0.640813</td>\n",
              "      <td>0.663601</td>\n",
              "      <td>-0.403764</td>\n",
              "      <td>-0.627605</td>\n",
              "      <td>-0.534462</td>\n",
              "      <td>-0.222680</td>\n",
              "      <td>-0.069588</td>\n",
              "      <td>-0.333362</td>\n",
              "      <td>-0.357037</td>\n",
              "      <td>-0.337877</td>\n",
              "      <td>-0.331563</td>\n",
              "      <td>0.041252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>skew</th>\n",
              "      <td>-0.322327</td>\n",
              "      <td>0.314597</td>\n",
              "      <td>-0.257407</td>\n",
              "      <td>-0.319475</td>\n",
              "      <td>-0.206339</td>\n",
              "      <td>0.249497</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.977020</td>\n",
              "      <td>-0.195459</td>\n",
              "      <td>0.079694</td>\n",
              "      <td>-0.434859</td>\n",
              "      <td>-0.322327</td>\n",
              "      <td>-0.167668</td>\n",
              "      <td>-0.216954</td>\n",
              "      <td>-0.080861</td>\n",
              "      <td>-0.336848</td>\n",
              "      <td>-0.061608</td>\n",
              "      <td>-0.305651</td>\n",
              "      <td>-0.304640</td>\n",
              "      <td>-0.169325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>kurt</th>\n",
              "      <td>-0.316036</td>\n",
              "      <td>0.346241</td>\n",
              "      <td>-0.243382</td>\n",
              "      <td>-0.350182</td>\n",
              "      <td>-0.148881</td>\n",
              "      <td>0.316185</td>\n",
              "      <td>0.977020</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.127644</td>\n",
              "      <td>0.109884</td>\n",
              "      <td>-0.406722</td>\n",
              "      <td>-0.316036</td>\n",
              "      <td>-0.194560</td>\n",
              "      <td>-0.203201</td>\n",
              "      <td>-0.045667</td>\n",
              "      <td>-0.303234</td>\n",
              "      <td>-0.103313</td>\n",
              "      <td>-0.274500</td>\n",
              "      <td>-0.272729</td>\n",
              "      <td>-0.205539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sp.ent</th>\n",
              "      <td>-0.601203</td>\n",
              "      <td>0.716620</td>\n",
              "      <td>-0.502005</td>\n",
              "      <td>-0.648126</td>\n",
              "      <td>-0.174905</td>\n",
              "      <td>0.640813</td>\n",
              "      <td>-0.195459</td>\n",
              "      <td>-0.127644</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.866411</td>\n",
              "      <td>-0.325298</td>\n",
              "      <td>-0.601203</td>\n",
              "      <td>-0.513194</td>\n",
              "      <td>-0.305826</td>\n",
              "      <td>-0.120738</td>\n",
              "      <td>-0.293562</td>\n",
              "      <td>-0.294869</td>\n",
              "      <td>-0.324253</td>\n",
              "      <td>-0.319054</td>\n",
              "      <td>0.198074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sfm</th>\n",
              "      <td>-0.784332</td>\n",
              "      <td>0.838086</td>\n",
              "      <td>-0.661690</td>\n",
              "      <td>-0.766875</td>\n",
              "      <td>-0.378198</td>\n",
              "      <td>0.663601</td>\n",
              "      <td>0.079694</td>\n",
              "      <td>0.109884</td>\n",
              "      <td>0.866411</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.485913</td>\n",
              "      <td>-0.784332</td>\n",
              "      <td>-0.421066</td>\n",
              "      <td>-0.362100</td>\n",
              "      <td>-0.192369</td>\n",
              "      <td>-0.428442</td>\n",
              "      <td>-0.289593</td>\n",
              "      <td>-0.436649</td>\n",
              "      <td>-0.431580</td>\n",
              "      <td>0.211477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mode</th>\n",
              "      <td>0.687715</td>\n",
              "      <td>-0.529150</td>\n",
              "      <td>0.677433</td>\n",
              "      <td>0.591277</td>\n",
              "      <td>0.486857</td>\n",
              "      <td>-0.403764</td>\n",
              "      <td>-0.434859</td>\n",
              "      <td>-0.406722</td>\n",
              "      <td>-0.325298</td>\n",
              "      <td>-0.485913</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.687715</td>\n",
              "      <td>0.324771</td>\n",
              "      <td>0.385467</td>\n",
              "      <td>0.172329</td>\n",
              "      <td>0.491479</td>\n",
              "      <td>0.198150</td>\n",
              "      <td>0.477187</td>\n",
              "      <td>0.473775</td>\n",
              "      <td>-0.182344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>centroid</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.739039</td>\n",
              "      <td>0.925445</td>\n",
              "      <td>0.911416</td>\n",
              "      <td>0.740997</td>\n",
              "      <td>-0.627605</td>\n",
              "      <td>-0.322327</td>\n",
              "      <td>-0.316036</td>\n",
              "      <td>-0.601203</td>\n",
              "      <td>-0.784332</td>\n",
              "      <td>0.687715</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.460844</td>\n",
              "      <td>0.383937</td>\n",
              "      <td>0.274004</td>\n",
              "      <td>0.536666</td>\n",
              "      <td>0.229261</td>\n",
              "      <td>0.519528</td>\n",
              "      <td>0.515570</td>\n",
              "      <td>-0.216979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>meanfun</th>\n",
              "      <td>0.460844</td>\n",
              "      <td>-0.466281</td>\n",
              "      <td>0.414909</td>\n",
              "      <td>0.545035</td>\n",
              "      <td>0.155091</td>\n",
              "      <td>-0.534462</td>\n",
              "      <td>-0.167668</td>\n",
              "      <td>-0.194560</td>\n",
              "      <td>-0.513194</td>\n",
              "      <td>-0.421066</td>\n",
              "      <td>0.324771</td>\n",
              "      <td>0.460844</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.339387</td>\n",
              "      <td>0.311950</td>\n",
              "      <td>0.270840</td>\n",
              "      <td>0.162163</td>\n",
              "      <td>0.277982</td>\n",
              "      <td>0.275154</td>\n",
              "      <td>-0.054858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>minfun</th>\n",
              "      <td>0.383937</td>\n",
              "      <td>-0.345609</td>\n",
              "      <td>0.337602</td>\n",
              "      <td>0.320994</td>\n",
              "      <td>0.258002</td>\n",
              "      <td>-0.222680</td>\n",
              "      <td>-0.216954</td>\n",
              "      <td>-0.203201</td>\n",
              "      <td>-0.305826</td>\n",
              "      <td>-0.362100</td>\n",
              "      <td>0.385467</td>\n",
              "      <td>0.383937</td>\n",
              "      <td>0.339387</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.213987</td>\n",
              "      <td>0.375979</td>\n",
              "      <td>0.082015</td>\n",
              "      <td>0.317860</td>\n",
              "      <td>0.316486</td>\n",
              "      <td>0.002042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>maxfun</th>\n",
              "      <td>0.274004</td>\n",
              "      <td>-0.129662</td>\n",
              "      <td>0.251328</td>\n",
              "      <td>0.199841</td>\n",
              "      <td>0.285584</td>\n",
              "      <td>-0.069588</td>\n",
              "      <td>-0.080861</td>\n",
              "      <td>-0.045667</td>\n",
              "      <td>-0.120738</td>\n",
              "      <td>-0.192369</td>\n",
              "      <td>0.172329</td>\n",
              "      <td>0.274004</td>\n",
              "      <td>0.311950</td>\n",
              "      <td>0.213987</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.337553</td>\n",
              "      <td>-0.243426</td>\n",
              "      <td>0.355390</td>\n",
              "      <td>0.359880</td>\n",
              "      <td>-0.363029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>meandom</th>\n",
              "      <td>0.536666</td>\n",
              "      <td>-0.482726</td>\n",
              "      <td>0.455943</td>\n",
              "      <td>0.467403</td>\n",
              "      <td>0.359181</td>\n",
              "      <td>-0.333362</td>\n",
              "      <td>-0.336848</td>\n",
              "      <td>-0.303234</td>\n",
              "      <td>-0.293562</td>\n",
              "      <td>-0.428442</td>\n",
              "      <td>0.491479</td>\n",
              "      <td>0.536666</td>\n",
              "      <td>0.270840</td>\n",
              "      <td>0.375979</td>\n",
              "      <td>0.337553</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.099656</td>\n",
              "      <td>0.812838</td>\n",
              "      <td>0.811304</td>\n",
              "      <td>-0.180954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mindom</th>\n",
              "      <td>0.229261</td>\n",
              "      <td>-0.357667</td>\n",
              "      <td>0.191169</td>\n",
              "      <td>0.302255</td>\n",
              "      <td>-0.023750</td>\n",
              "      <td>-0.357037</td>\n",
              "      <td>-0.061608</td>\n",
              "      <td>-0.103313</td>\n",
              "      <td>-0.294869</td>\n",
              "      <td>-0.289593</td>\n",
              "      <td>0.198150</td>\n",
              "      <td>0.229261</td>\n",
              "      <td>0.162163</td>\n",
              "      <td>0.082015</td>\n",
              "      <td>-0.243426</td>\n",
              "      <td>0.099656</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.026640</td>\n",
              "      <td>0.008666</td>\n",
              "      <td>0.200212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>maxdom</th>\n",
              "      <td>0.519528</td>\n",
              "      <td>-0.482278</td>\n",
              "      <td>0.438919</td>\n",
              "      <td>0.459683</td>\n",
              "      <td>0.335114</td>\n",
              "      <td>-0.337877</td>\n",
              "      <td>-0.305651</td>\n",
              "      <td>-0.274500</td>\n",
              "      <td>-0.324253</td>\n",
              "      <td>-0.436649</td>\n",
              "      <td>0.477187</td>\n",
              "      <td>0.519528</td>\n",
              "      <td>0.277982</td>\n",
              "      <td>0.317860</td>\n",
              "      <td>0.355390</td>\n",
              "      <td>0.812838</td>\n",
              "      <td>0.026640</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999838</td>\n",
              "      <td>-0.425531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dfrange</th>\n",
              "      <td>0.515570</td>\n",
              "      <td>-0.475999</td>\n",
              "      <td>0.435621</td>\n",
              "      <td>0.454394</td>\n",
              "      <td>0.335648</td>\n",
              "      <td>-0.331563</td>\n",
              "      <td>-0.304640</td>\n",
              "      <td>-0.272729</td>\n",
              "      <td>-0.319054</td>\n",
              "      <td>-0.431580</td>\n",
              "      <td>0.473775</td>\n",
              "      <td>0.515570</td>\n",
              "      <td>0.275154</td>\n",
              "      <td>0.316486</td>\n",
              "      <td>0.359880</td>\n",
              "      <td>0.811304</td>\n",
              "      <td>0.008666</td>\n",
              "      <td>0.999838</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.429266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>modindx</th>\n",
              "      <td>-0.216979</td>\n",
              "      <td>0.122660</td>\n",
              "      <td>-0.213298</td>\n",
              "      <td>-0.141377</td>\n",
              "      <td>-0.216475</td>\n",
              "      <td>0.041252</td>\n",
              "      <td>-0.169325</td>\n",
              "      <td>-0.205539</td>\n",
              "      <td>0.198074</td>\n",
              "      <td>0.211477</td>\n",
              "      <td>-0.182344</td>\n",
              "      <td>-0.216979</td>\n",
              "      <td>-0.054858</td>\n",
              "      <td>0.002042</td>\n",
              "      <td>-0.363029</td>\n",
              "      <td>-0.180954</td>\n",
              "      <td>0.200212</td>\n",
              "      <td>-0.425531</td>\n",
              "      <td>-0.429266</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d44e6394-aab4-4990-915b-0cc9e9794308')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d44e6394-aab4-4990-915b-0cc9e9794308 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d44e6394-aab4-4990-915b-0cc9e9794308');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "df.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "5115f781",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5115f781",
        "outputId": "e7ffb879-f60f-40fc-896e-aac69714e0db"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "meanfreq    0\n",
              "sd          0\n",
              "median      0\n",
              "Q25         0\n",
              "Q75         0\n",
              "IQR         0\n",
              "skew        0\n",
              "kurt        0\n",
              "sp.ent      0\n",
              "sfm         0\n",
              "mode        0\n",
              "centroid    0\n",
              "meanfun     0\n",
              "minfun      0\n",
              "maxfun      0\n",
              "meandom     0\n",
              "mindom      0\n",
              "maxdom      0\n",
              "dfrange     0\n",
              "modindx     0\n",
              "label       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Checking for null values\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "73f7bf90",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "73f7bf90",
        "outputId": "67f92bf1-e02e-4977-cfe0-878ff58ec835"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3168, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "b30a6da2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b30a6da2",
        "outputId": "8c6be00f-bb53-4e30-b9be-956f77dbd2b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total no. of people:  3168 \n",
            " male      1584\n",
            "female    1584\n",
            "Name: label, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(\"Total no. of people: \", df['label'].count(), '\\n', df['label'].value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "86294e3b",
      "metadata": {
        "id": "86294e3b"
      },
      "source": [
        "# Splitting independent variables (features) and label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "c92408db",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "c92408db",
        "outputId": "38098a35-726a-45c0-caad-d98a0e304138"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      meanfreq        sd    median       Q25       Q75       IQR       skew  \\\n",
              "0     0.059781  0.064241  0.032027  0.015071  0.090193  0.075122  12.863462   \n",
              "1     0.066009  0.067310  0.040229  0.019414  0.092666  0.073252  22.423285   \n",
              "2     0.077316  0.083829  0.036718  0.008701  0.131908  0.123207  30.757155   \n",
              "3     0.151228  0.072111  0.158011  0.096582  0.207955  0.111374   1.232831   \n",
              "4     0.135120  0.079146  0.124656  0.078720  0.206045  0.127325   1.101174   \n",
              "...        ...       ...       ...       ...       ...       ...        ...   \n",
              "3163  0.131884  0.084734  0.153707  0.049285  0.201144  0.151859   1.762129   \n",
              "3164  0.116221  0.089221  0.076758  0.042718  0.204911  0.162193   0.693730   \n",
              "3165  0.142056  0.095798  0.183731  0.033424  0.224360  0.190936   1.876502   \n",
              "3166  0.143659  0.090628  0.184976  0.043508  0.219943  0.176435   1.591065   \n",
              "3167  0.165509  0.092884  0.183044  0.070072  0.250827  0.180756   1.705029   \n",
              "\n",
              "             kurt    sp.ent       sfm      mode  centroid   meanfun    minfun  \\\n",
              "0      274.402906  0.893369  0.491918  0.000000  0.059781  0.084279  0.015702   \n",
              "1      634.613855  0.892193  0.513724  0.000000  0.066009  0.107937  0.015826   \n",
              "2     1024.927705  0.846389  0.478905  0.000000  0.077316  0.098706  0.015656   \n",
              "3        4.177296  0.963322  0.727232  0.083878  0.151228  0.088965  0.017798   \n",
              "4        4.333713  0.971955  0.783568  0.104261  0.135120  0.106398  0.016931   \n",
              "...           ...       ...       ...       ...       ...       ...       ...   \n",
              "3163     6.630383  0.962934  0.763182  0.200836  0.131884  0.182790  0.083770   \n",
              "3164     2.503954  0.960716  0.709570  0.013683  0.116221  0.188980  0.034409   \n",
              "3165     6.604509  0.946854  0.654196  0.008006  0.142056  0.209918  0.039506   \n",
              "3166     5.388298  0.950436  0.675470  0.212202  0.143659  0.172375  0.034483   \n",
              "3167     5.769115  0.938829  0.601529  0.267702  0.165509  0.185607  0.062257   \n",
              "\n",
              "        maxfun   meandom    mindom    maxdom   dfrange   modindx  \n",
              "0     0.275862  0.007812  0.007812  0.007812  0.000000  0.000000  \n",
              "1     0.250000  0.009014  0.007812  0.054688  0.046875  0.052632  \n",
              "2     0.271186  0.007990  0.007812  0.015625  0.007812  0.046512  \n",
              "3     0.250000  0.201497  0.007812  0.562500  0.554688  0.247119  \n",
              "4     0.266667  0.712812  0.007812  5.484375  5.476562  0.208274  \n",
              "...        ...       ...       ...       ...       ...       ...  \n",
              "3163  0.262295  0.832899  0.007812  4.210938  4.203125  0.161929  \n",
              "3164  0.275862  0.909856  0.039062  3.679688  3.640625  0.277897  \n",
              "3165  0.275862  0.494271  0.007812  2.937500  2.929688  0.194759  \n",
              "3166  0.250000  0.791360  0.007812  3.593750  3.585938  0.311002  \n",
              "3167  0.271186  0.227022  0.007812  0.554688  0.546875  0.350000  \n",
              "\n",
              "[3168 rows x 20 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>meanfreq</th>\n",
              "      <th>sd</th>\n",
              "      <th>median</th>\n",
              "      <th>Q25</th>\n",
              "      <th>Q75</th>\n",
              "      <th>IQR</th>\n",
              "      <th>skew</th>\n",
              "      <th>kurt</th>\n",
              "      <th>sp.ent</th>\n",
              "      <th>sfm</th>\n",
              "      <th>mode</th>\n",
              "      <th>centroid</th>\n",
              "      <th>meanfun</th>\n",
              "      <th>minfun</th>\n",
              "      <th>maxfun</th>\n",
              "      <th>meandom</th>\n",
              "      <th>mindom</th>\n",
              "      <th>maxdom</th>\n",
              "      <th>dfrange</th>\n",
              "      <th>modindx</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.059781</td>\n",
              "      <td>0.064241</td>\n",
              "      <td>0.032027</td>\n",
              "      <td>0.015071</td>\n",
              "      <td>0.090193</td>\n",
              "      <td>0.075122</td>\n",
              "      <td>12.863462</td>\n",
              "      <td>274.402906</td>\n",
              "      <td>0.893369</td>\n",
              "      <td>0.491918</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.059781</td>\n",
              "      <td>0.084279</td>\n",
              "      <td>0.015702</td>\n",
              "      <td>0.275862</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.066009</td>\n",
              "      <td>0.067310</td>\n",
              "      <td>0.040229</td>\n",
              "      <td>0.019414</td>\n",
              "      <td>0.092666</td>\n",
              "      <td>0.073252</td>\n",
              "      <td>22.423285</td>\n",
              "      <td>634.613855</td>\n",
              "      <td>0.892193</td>\n",
              "      <td>0.513724</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.066009</td>\n",
              "      <td>0.107937</td>\n",
              "      <td>0.015826</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.009014</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>0.054688</td>\n",
              "      <td>0.046875</td>\n",
              "      <td>0.052632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.077316</td>\n",
              "      <td>0.083829</td>\n",
              "      <td>0.036718</td>\n",
              "      <td>0.008701</td>\n",
              "      <td>0.131908</td>\n",
              "      <td>0.123207</td>\n",
              "      <td>30.757155</td>\n",
              "      <td>1024.927705</td>\n",
              "      <td>0.846389</td>\n",
              "      <td>0.478905</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.077316</td>\n",
              "      <td>0.098706</td>\n",
              "      <td>0.015656</td>\n",
              "      <td>0.271186</td>\n",
              "      <td>0.007990</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>0.015625</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>0.046512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.151228</td>\n",
              "      <td>0.072111</td>\n",
              "      <td>0.158011</td>\n",
              "      <td>0.096582</td>\n",
              "      <td>0.207955</td>\n",
              "      <td>0.111374</td>\n",
              "      <td>1.232831</td>\n",
              "      <td>4.177296</td>\n",
              "      <td>0.963322</td>\n",
              "      <td>0.727232</td>\n",
              "      <td>0.083878</td>\n",
              "      <td>0.151228</td>\n",
              "      <td>0.088965</td>\n",
              "      <td>0.017798</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.201497</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>0.562500</td>\n",
              "      <td>0.554688</td>\n",
              "      <td>0.247119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.135120</td>\n",
              "      <td>0.079146</td>\n",
              "      <td>0.124656</td>\n",
              "      <td>0.078720</td>\n",
              "      <td>0.206045</td>\n",
              "      <td>0.127325</td>\n",
              "      <td>1.101174</td>\n",
              "      <td>4.333713</td>\n",
              "      <td>0.971955</td>\n",
              "      <td>0.783568</td>\n",
              "      <td>0.104261</td>\n",
              "      <td>0.135120</td>\n",
              "      <td>0.106398</td>\n",
              "      <td>0.016931</td>\n",
              "      <td>0.266667</td>\n",
              "      <td>0.712812</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>5.484375</td>\n",
              "      <td>5.476562</td>\n",
              "      <td>0.208274</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3163</th>\n",
              "      <td>0.131884</td>\n",
              "      <td>0.084734</td>\n",
              "      <td>0.153707</td>\n",
              "      <td>0.049285</td>\n",
              "      <td>0.201144</td>\n",
              "      <td>0.151859</td>\n",
              "      <td>1.762129</td>\n",
              "      <td>6.630383</td>\n",
              "      <td>0.962934</td>\n",
              "      <td>0.763182</td>\n",
              "      <td>0.200836</td>\n",
              "      <td>0.131884</td>\n",
              "      <td>0.182790</td>\n",
              "      <td>0.083770</td>\n",
              "      <td>0.262295</td>\n",
              "      <td>0.832899</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>4.210938</td>\n",
              "      <td>4.203125</td>\n",
              "      <td>0.161929</td>\n",
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              "    <tr>\n",
              "      <th>3164</th>\n",
              "      <td>0.116221</td>\n",
              "      <td>0.089221</td>\n",
              "      <td>0.076758</td>\n",
              "      <td>0.042718</td>\n",
              "      <td>0.204911</td>\n",
              "      <td>0.162193</td>\n",
              "      <td>0.693730</td>\n",
              "      <td>2.503954</td>\n",
              "      <td>0.960716</td>\n",
              "      <td>0.709570</td>\n",
              "      <td>0.013683</td>\n",
              "      <td>0.116221</td>\n",
              "      <td>0.188980</td>\n",
              "      <td>0.034409</td>\n",
              "      <td>0.275862</td>\n",
              "      <td>0.909856</td>\n",
              "      <td>0.039062</td>\n",
              "      <td>3.679688</td>\n",
              "      <td>3.640625</td>\n",
              "      <td>0.277897</td>\n",
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              "    <tr>\n",
              "      <th>3165</th>\n",
              "      <td>0.142056</td>\n",
              "      <td>0.095798</td>\n",
              "      <td>0.183731</td>\n",
              "      <td>0.033424</td>\n",
              "      <td>0.224360</td>\n",
              "      <td>0.190936</td>\n",
              "      <td>1.876502</td>\n",
              "      <td>6.604509</td>\n",
              "      <td>0.946854</td>\n",
              "      <td>0.654196</td>\n",
              "      <td>0.008006</td>\n",
              "      <td>0.142056</td>\n",
              "      <td>0.209918</td>\n",
              "      <td>0.039506</td>\n",
              "      <td>0.275862</td>\n",
              "      <td>0.494271</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>2.937500</td>\n",
              "      <td>2.929688</td>\n",
              "      <td>0.194759</td>\n",
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              "    <tr>\n",
              "      <th>3166</th>\n",
              "      <td>0.143659</td>\n",
              "      <td>0.090628</td>\n",
              "      <td>0.184976</td>\n",
              "      <td>0.043508</td>\n",
              "      <td>0.219943</td>\n",
              "      <td>0.176435</td>\n",
              "      <td>1.591065</td>\n",
              "      <td>5.388298</td>\n",
              "      <td>0.950436</td>\n",
              "      <td>0.675470</td>\n",
              "      <td>0.212202</td>\n",
              "      <td>0.143659</td>\n",
              "      <td>0.172375</td>\n",
              "      <td>0.034483</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.791360</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>3.593750</td>\n",
              "      <td>3.585938</td>\n",
              "      <td>0.311002</td>\n",
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              "    <tr>\n",
              "      <th>3167</th>\n",
              "      <td>0.165509</td>\n",
              "      <td>0.092884</td>\n",
              "      <td>0.183044</td>\n",
              "      <td>0.070072</td>\n",
              "      <td>0.250827</td>\n",
              "      <td>0.180756</td>\n",
              "      <td>1.705029</td>\n",
              "      <td>5.769115</td>\n",
              "      <td>0.938829</td>\n",
              "      <td>0.601529</td>\n",
              "      <td>0.267702</td>\n",
              "      <td>0.165509</td>\n",
              "      <td>0.185607</td>\n",
              "      <td>0.062257</td>\n",
              "      <td>0.271186</td>\n",
              "      <td>0.227022</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>0.554688</td>\n",
              "      <td>0.546875</td>\n",
              "      <td>0.350000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3168 rows × 20 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "X = df.iloc[:, :-1]\n",
        "y = df.iloc[:, -1]\n",
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "a9301fde",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a9301fde",
        "outputId": "f7898181-475c-4e2c-da8f-48278abaadd3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         male\n",
              "1         male\n",
              "2         male\n",
              "3         male\n",
              "4         male\n",
              "         ...  \n",
              "3163    female\n",
              "3164    female\n",
              "3165    female\n",
              "3166    female\n",
              "3167    female\n",
              "Name: label, Length: 3168, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "y"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6693621e",
      "metadata": {
        "id": "6693621e"
      },
      "source": [
        "## Converting label into category or integers\n",
        "Can be done using LabelEncoder or One-hot Encoding."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "bf7799f7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bf7799f7",
        "outputId": "669949bd-2c9a-4634-b062-406650e6f704"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# Using LabelEncoder\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "gender = LabelEncoder()\n",
        "y = gender.fit_transform(y)\n",
        "y"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "07abe74c",
      "metadata": {
        "id": "07abe74c"
      },
      "source": [
        "## Data Standardization\n",
        "\n",
        "Standardization refers to shifting the distribution of each attribute to have a mean of zero and a standard deviation of one (unit variance). It is useful to standardize attributes for a model. Standardization of datasets is a common requirement for many machine learning estimators implemented in scikit-learn; they might behave badly if the individual features do not more or less look like standard normally distributed data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "9d18d2de",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9d18d2de",
        "outputId": "6d13b8a7-dcbd-4298-80de-bbab8741c983"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-4.04924806,  0.4273553 , -4.22490077, ..., -1.43142165,\n",
              "        -1.41913712, -1.45477229],\n",
              "       [-3.84105325,  0.6116695 , -3.99929342, ..., -1.41810716,\n",
              "        -1.4058184 , -1.01410294],\n",
              "       [-3.46306647,  1.60384791, -4.09585052, ..., -1.42920257,\n",
              "        -1.41691733, -1.06534356],\n",
              "       ...,\n",
              "       [-1.29877326,  2.32272355, -0.05197279, ..., -0.5992661 ,\n",
              "        -0.58671739,  0.17588664],\n",
              "       [-1.2452018 ,  2.012196  , -0.01772849, ..., -0.41286326,\n",
              "        -0.40025537,  1.14916112],\n",
              "       [-0.51474626,  2.14765111, -0.07087873, ..., -1.27608595,\n",
              "        -1.2637521 ,  1.47567886]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "\n",
        "X = sc.fit_transform(X)\n",
        "X"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4780ae45",
      "metadata": {
        "id": "4780ae45"
      },
      "source": [
        "## Split into train and test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "39c40db6",
      "metadata": {
        "id": "39c40db6"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aa5da961",
      "metadata": {
        "id": "aa5da961"
      },
      "source": [
        "# SVM"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "326e0e45",
      "metadata": {
        "id": "326e0e45"
      },
      "source": [
        "## Running with default parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "5b7f8e32",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5b7f8e32",
        "outputId": "35b6ad9c-d1a1-4088-a5dc-3d24e8d9e0b5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score with default parameters:  0.9763406940063092\n"
          ]
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn import metrics\n",
        "\n",
        "# instantiate model\n",
        "svc = SVC()\n",
        "svc.fit(X_train, y_train)\n",
        "y_svc_pred = svc.predict(X_test)\n",
        "print(\"Accuracy score with default parameters: \", metrics.accuracy_score(y_test, y_svc_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8c25bbeb",
      "metadata": {
        "id": "8c25bbeb"
      },
      "source": [
        "## Running with default linear kernel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "a886f945",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a886f945",
        "outputId": "05f5ae40-1ac7-42e4-e527-a70f657618e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score with linear kernel: 0.9779179810725552\n"
          ]
        }
      ],
      "source": [
        "lin_svc = SVC(kernel = 'linear')\n",
        "lin_svc.fit(X_train, y_train)\n",
        "\n",
        "y_linsvc_pred = lin_svc.predict(X_test)\n",
        "print(\"Accuracy score with linear kernel:\", metrics.accuracy_score(y_test, y_linsvc_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3e23926f",
      "metadata": {
        "id": "3e23926f"
      },
      "source": [
        "## Default polynomial \n",
        "Computes relationship between pairs of observations (from one group to another) WITHOUT actually changing the dimension. <br>\n",
        "<br>\n",
        "Formula: (a * b + r)^d <br> <br>\n",
        "a: value of one category <br>\n",
        "b: value of the other category <br>\n",
        "r: polynomial coefficient (Determined using Cross-validation) <br>\n",
        "d: degree of polynomial (Determined using Cross-validation)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "41910cd9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "41910cd9",
        "outputId": "f07c5afc-9856-4bfb-ce7b-7614c9fe9364"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score with polynomial kernel: 0.9589905362776026\n"
          ]
        }
      ],
      "source": [
        "svc_poly = SVC(kernel = 'poly')\n",
        "svc_poly.fit(X_train, y_train)\n",
        "y_polysvc_pred = svc_poly.predict(X_test)\n",
        "print(\"Accuracy score with polynomial kernel:\", metrics.accuracy_score(y_test, y_polysvc_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4ebee95e",
      "metadata": {
        "id": "4ebee95e"
      },
      "source": [
        "Accuracy using Polynomial kernel is LOW maybe because of over-fitting."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cbdfaad1",
      "metadata": {
        "id": "cbdfaad1"
      },
      "source": [
        "# Performing n-fold Cross Validation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "21da02bd",
      "metadata": {
        "id": "21da02bd"
      },
      "outputs": [],
      "source": [
        "# from sklearn.cross_validation import cross_val_score\n",
        "from sklearn.model_selection import cross_validate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "72d9b9f0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "72d9b9f0",
        "outputId": "092574b0-b830-456e-90c5-11f5c7bd52d9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'fit_time': array([0.06306529, 0.05229354, 0.05059481, 0.0525775 , 0.04418755,\n",
            "       0.04937482, 0.04814386, 0.04783463, 0.04270744, 0.04758072]), 'score_time': array([0.00301528, 0.00198936, 0.00209427, 0.00199151, 0.00187922,\n",
            "       0.00218725, 0.00210071, 0.0019908 , 0.00197911, 0.00207186]), 'test_score': array([0.91167192, 0.97160883, 0.97160883, 0.97791798, 0.95899054,\n",
            "       0.9873817 , 0.99369085, 0.97791798, 0.95253165, 0.99367089])}\n"
          ]
        }
      ],
      "source": [
        "scores = cross_validate(lin_svc, X, y, cv=10, scoring='accuracy') #cv is cross validation\n",
        "print(scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "ac8e70a5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ac8e70a5",
        "outputId": "e7b0f250-f895-4322-fc6f-28ffbef8c621"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.06572175 0.07413459 0.06553531 0.0721972  0.0732305  0.07426357\n",
            " 0.07338333 0.0734272  0.06441927 0.07331681]\n",
            "[0.00553179 0.00589705 0.00553727 0.00571346 0.00574207 0.00652504\n",
            " 0.00604272 0.00610423 0.00562477 0.00605083]\n",
            "[0.89274448 0.94952681 0.93059937 0.92744479 0.94952681 0.99369085\n",
            " 0.98422713 0.96529968 0.87974684 0.9778481 ]\n",
            "[0.32133267 0.34318615 0.33389065 0.33511849 0.34283313 0.35815982\n",
            " 0.35455106 0.34827704 0.31659696 0.35240525]\n"
          ]
        }
      ],
      "source": [
        "# print(scores.mean())\n",
        "sum = 0\n",
        "for i in scores.values():\n",
        "  sum = sum + i\n",
        "  print(i)\n",
        "\n",
        "l = len(scores)\n",
        "\n",
        "print(sum/l)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "e99a0f62",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e99a0f62",
        "outputId": "d9824924-880c-480c-fedf-90adaed46190"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'fit_time': array([0.06734228, 0.07079291, 0.06559277, 0.07049632, 0.06545472,\n",
            "       0.08175945, 0.07139754, 0.07350302, 0.06541443, 0.07079887]), 'score_time': array([0.00537825, 0.00597692, 0.00559926, 0.00748301, 0.00578785,\n",
            "       0.00620699, 0.00646424, 0.00608253, 0.00562453, 0.0061872 ]), 'test_score': array([0.89274448, 0.94952681, 0.93059937, 0.92744479, 0.94952681,\n",
            "       0.99369085, 0.98422713, 0.96529968, 0.87974684, 0.9778481 ])}\n"
          ]
        }
      ],
      "source": [
        "scores = cross_validate(svc_poly, X, y, cv=10, scoring='accuracy') #cv is cross validation\n",
        "print(scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "2006cc90",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2006cc90",
        "outputId": "1e9b8878-1bbd-48c1-88a9-974e6777446d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.06734228 0.07079291 0.06559277 0.07049632 0.06545472 0.08175945\n",
            " 0.07139754 0.07350302 0.06541443 0.07079887]\n",
            "[0.00537825 0.00597692 0.00559926 0.00748301 0.00578785 0.00620699\n",
            " 0.00646424 0.00608253 0.00562453 0.0061872 ]\n",
            "[0.89274448 0.94952681 0.93059937 0.92744479 0.94952681 0.99369085\n",
            " 0.98422713 0.96529968 0.87974684 0.9778481 ]\n",
            "[0.32182167 0.34209888 0.33393047 0.33514137 0.34025646 0.36055243\n",
            " 0.35402964 0.34829508 0.3169286  0.35161139]\n"
          ]
        }
      ],
      "source": [
        "sum = 0\n",
        "for i in scores.values():\n",
        "  sum = sum + i\n",
        "  print(i)\n",
        "\n",
        "l = len(scores)\n",
        "\n",
        "print(sum/l)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
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      "name": "python3"
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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