SVM-detail-analysis.ipynb

{
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.5.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0,
  "cells": [
    {
      "cell_type": "markdown",
      "source": "Here I m going to run **Support Vector machine** with different **kernels(linear,gaussian,polynomial)** and also tune the various parameters such as **C** ,**gamma** and **degree** to find out the best performing model .",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output.",
      "execution_count": 1,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "voice.csv\n\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Importing all the necessary libraries",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np\nimport seaborn as sns\n\nimport matplotlib.pyplot as plt\n\n%matplotlib inline",
      "execution_count": 2,
      "outputs": [],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Reading the comma separated values file into the dataframe",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df = pd.read_csv('../input/voice.csv')\ndf.head()",
      "execution_count": 3,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<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>...</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      <th>label</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>...</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      <td>male</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>...</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      <td>male</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>...</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      <td>male</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>...</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      <td>male</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>...</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      <td>male</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 21 columns</p>\n</div>",
            "text/plain": "   meanfreq        sd    median       Q25       Q75       IQR       skew  \\\n0  0.059781  0.064241  0.032027  0.015071  0.090193  0.075122  12.863462   \n1  0.066009  0.067310  0.040229  0.019414  0.092666  0.073252  22.423285   \n2  0.077316  0.083829  0.036718  0.008701  0.131908  0.123207  30.757155   \n3  0.151228  0.072111  0.158011  0.096582  0.207955  0.111374   1.232831   \n4  0.135120  0.079146  0.124656  0.078720  0.206045  0.127325   1.101174   \n\n          kurt    sp.ent       sfm  ...    centroid   meanfun    minfun  \\\n0   274.402906  0.893369  0.491918  ...    0.059781  0.084279  0.015702   \n1   634.613855  0.892193  0.513724  ...    0.066009  0.107937  0.015826   \n2  1024.927705  0.846389  0.478905  ...    0.077316  0.098706  0.015656   \n3     4.177296  0.963322  0.727232  ...    0.151228  0.088965  0.017798   \n4     4.333713  0.971955  0.783568  ...    0.135120  0.106398  0.016931   \n\n     maxfun   meandom    mindom    maxdom   dfrange   modindx  label  \n0  0.275862  0.007812  0.007812  0.007812  0.000000  0.000000   male  \n1  0.250000  0.009014  0.007812  0.054688  0.046875  0.052632   male  \n2  0.271186  0.007990  0.007812  0.015625  0.007812  0.046512   male  \n3  0.250000  0.201497  0.007812  0.562500  0.554688  0.247119   male  \n4  0.266667  0.712812  0.007812  5.484375  5.476562  0.208274   male  \n\n[5 rows x 21 columns]"
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Checking the correlation between each feature",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.corr()",
      "execution_count": 4,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<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>",
            "text/plain": "          meanfreq        sd    median       Q25       Q75       IQR  \\\nmeanfreq  1.000000 -0.739039  0.925445  0.911416  0.740997 -0.627605   \nsd       -0.739039  1.000000 -0.562603 -0.846931 -0.161076  0.874660   \nmedian    0.925445 -0.562603  1.000000  0.774922  0.731849 -0.477352   \nQ25       0.911416 -0.846931  0.774922  1.000000  0.477140 -0.874189   \nQ75       0.740997 -0.161076  0.731849  0.477140  1.000000  0.009636   \nIQR      -0.627605  0.874660 -0.477352 -0.874189  0.009636  1.000000   \nskew     -0.322327  0.314597 -0.257407 -0.319475 -0.206339  0.249497   \nkurt     -0.316036  0.346241 -0.243382 -0.350182 -0.148881  0.316185   \nsp.ent   -0.601203  0.716620 -0.502005 -0.648126 -0.174905  0.640813   \nsfm      -0.784332  0.838086 -0.661690 -0.766875 -0.378198  0.663601   \nmode      0.687715 -0.529150  0.677433  0.591277  0.486857 -0.403764   \ncentroid  1.000000 -0.739039  0.925445  0.911416  0.740997 -0.627605   \nmeanfun   0.460844 -0.466281  0.414909  0.545035  0.155091 -0.534462   \nminfun    0.383937 -0.345609  0.337602  0.320994  0.258002 -0.222680   \nmaxfun    0.274004 -0.129662  0.251328  0.199841  0.285584 -0.069588   \nmeandom   0.536666 -0.482726  0.455943  0.467403  0.359181 -0.333362   \nmindom    0.229261 -0.357667  0.191169  0.302255 -0.023750 -0.357037   \nmaxdom    0.519528 -0.482278  0.438919  0.459683  0.335114 -0.337877   \ndfrange   0.515570 -0.475999  0.435621  0.454394  0.335648 -0.331563   \nmodindx  -0.216979  0.122660 -0.213298 -0.141377 -0.216475  0.041252   \n\n              skew      kurt    sp.ent       sfm      mode  centroid  \\\nmeanfreq -0.322327 -0.316036 -0.601203 -0.784332  0.687715  1.000000   \nsd        0.314597  0.346241  0.716620  0.838086 -0.529150 -0.739039   \nmedian   -0.257407 -0.243382 -0.502005 -0.661690  0.677433  0.925445   \nQ25      -0.319475 -0.350182 -0.648126 -0.766875  0.591277  0.911416   \nQ75      -0.206339 -0.148881 -0.174905 -0.378198  0.486857  0.740997   \nIQR       0.249497  0.316185  0.640813  0.663601 -0.403764 -0.627605   \nskew      1.000000  0.977020 -0.195459  0.079694 -0.434859 -0.322327   \nkurt      0.977020  1.000000 -0.127644  0.109884 -0.406722 -0.316036   \nsp.ent   -0.195459 -0.127644  1.000000  0.866411 -0.325298 -0.601203   \nsfm       0.079694  0.109884  0.866411  1.000000 -0.485913 -0.784332   \nmode     -0.434859 -0.406722 -0.325298 -0.485913  1.000000  0.687715   \ncentroid -0.322327 -0.316036 -0.601203 -0.784332  0.687715  1.000000   \nmeanfun  -0.167668 -0.194560 -0.513194 -0.421066  0.324771  0.460844   \nminfun   -0.216954 -0.203201 -0.305826 -0.362100  0.385467  0.383937   \nmaxfun   -0.080861 -0.045667 -0.120738 -0.192369  0.172329  0.274004   \nmeandom  -0.336848 -0.303234 -0.293562 -0.428442  0.491479  0.536666   \nmindom   -0.061608 -0.103313 -0.294869 -0.289593  0.198150  0.229261   \nmaxdom   -0.305651 -0.274500 -0.324253 -0.436649  0.477187  0.519528   \ndfrange  -0.304640 -0.272729 -0.319054 -0.431580  0.473775  0.515570   \nmodindx  -0.169325 -0.205539  0.198074  0.211477 -0.182344 -0.216979   \n\n           meanfun    minfun    maxfun   meandom    mindom    maxdom  \\\nmeanfreq  0.460844  0.383937  0.274004  0.536666  0.229261  0.519528   \nsd       -0.466281 -0.345609 -0.129662 -0.482726 -0.357667 -0.482278   \nmedian    0.414909  0.337602  0.251328  0.455943  0.191169  0.438919   \nQ25       0.545035  0.320994  0.199841  0.467403  0.302255  0.459683   \nQ75       0.155091  0.258002  0.285584  0.359181 -0.023750  0.335114   \nIQR      -0.534462 -0.222680 -0.069588 -0.333362 -0.357037 -0.337877   \nskew     -0.167668 -0.216954 -0.080861 -0.336848 -0.061608 -0.305651   \nkurt     -0.194560 -0.203201 -0.045667 -0.303234 -0.103313 -0.274500   \nsp.ent   -0.513194 -0.305826 -0.120738 -0.293562 -0.294869 -0.324253   \nsfm      -0.421066 -0.362100 -0.192369 -0.428442 -0.289593 -0.436649   \nmode      0.324771  0.385467  0.172329  0.491479  0.198150  0.477187   \ncentroid  0.460844  0.383937  0.274004  0.536666  0.229261  0.519528   \nmeanfun   1.000000  0.339387  0.311950  0.270840  0.162163  0.277982   \nminfun    0.339387  1.000000  0.213987  0.375979  0.082015  0.317860   \nmaxfun    0.311950  0.213987  1.000000  0.337553 -0.243426  0.355390   \nmeandom   0.270840  0.375979  0.337553  1.000000  0.099656  0.812838   \nmindom    0.162163  0.082015 -0.243426  0.099656  1.000000  0.026640   \nmaxdom    0.277982  0.317860  0.355390  0.812838  0.026640  1.000000   \ndfrange   0.275154  0.316486  0.359880  0.811304  0.008666  0.999838   \nmodindx  -0.054858  0.002042 -0.363029 -0.180954  0.200212 -0.425531   \n\n           dfrange   modindx  \nmeanfreq  0.515570 -0.216979  \nsd       -0.475999  0.122660  \nmedian    0.435621 -0.213298  \nQ25       0.454394 -0.141377  \nQ75       0.335648 -0.216475  \nIQR      -0.331563  0.041252  \nskew     -0.304640 -0.169325  \nkurt     -0.272729 -0.205539  \nsp.ent   -0.319054  0.198074  \nsfm      -0.431580  0.211477  \nmode      0.473775 -0.182344  \ncentroid  0.515570 -0.216979  \nmeanfun   0.275154 -0.054858  \nminfun    0.316486  0.002042  \nmaxfun    0.359880 -0.363029  \nmeandom   0.811304 -0.180954  \nmindom    0.008666  0.200212  \nmaxdom    0.999838 -0.425531  \ndfrange   1.000000 -0.429266  \nmodindx  -0.429266  1.000000  "
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Checking whether there is any null values ",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.isnull().sum()",
      "execution_count": 5,
      "outputs": [
        {
          "data": {
            "text/plain": "meanfreq    0\nsd          0\nmedian      0\nQ25         0\nQ75         0\nIQR         0\nskew        0\nkurt        0\nsp.ent      0\nsfm         0\nmode        0\ncentroid    0\nmeanfun     0\nminfun      0\nmaxfun      0\nmeandom     0\nmindom      0\nmaxdom      0\ndfrange     0\nmodindx     0\nlabel       0\ndtype: int64"
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.shape",
      "execution_count": 6,
      "outputs": [
        {
          "data": {
            "text/plain": "(3168, 21)"
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print(\"Total number of labels: {}\".format(df.shape[0]))\nprint(\"Number of male: {}\".format(df[df.label == 'male'].shape[0]))\nprint(\"Number of female: {}\".format(df[df.label == 'female'].shape[0]))",
      "execution_count": 7,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Total number of labels: 3168\nNumber of male: 1584\nNumber of female: 1584\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Thus we can see there are equal number of male and female labels",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.shape",
      "execution_count": 8,
      "outputs": [
        {
          "data": {
            "text/plain": "(3168, 21)"
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "There are 21 features and 3168 instances.",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Separating features and labels",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "X=df.iloc[:, :-1]\nX.head()",
      "execution_count": 9,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<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>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    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   meanfreq        sd    median       Q25       Q75       IQR       skew  \\\n0  0.059781  0.064241  0.032027  0.015071  0.090193  0.075122  12.863462   \n1  0.066009  0.067310  0.040229  0.019414  0.092666  0.073252  22.423285   \n2  0.077316  0.083829  0.036718  0.008701  0.131908  0.123207  30.757155   \n3  0.151228  0.072111  0.158011  0.096582  0.207955  0.111374   1.232831   \n4  0.135120  0.079146  0.124656  0.078720  0.206045  0.127325   1.101174   \n\n          kurt    sp.ent       sfm      mode  centroid   meanfun    minfun  \\\n0   274.402906  0.893369  0.491918  0.000000  0.059781  0.084279  0.015702   \n1   634.613855  0.892193  0.513724  0.000000  0.066009  0.107937  0.015826   \n2  1024.927705  0.846389  0.478905  0.000000  0.077316  0.098706  0.015656   \n3     4.177296  0.963322  0.727232  0.083878  0.151228  0.088965  0.017798   \n4     4.333713  0.971955  0.783568  0.104261  0.135120  0.106398  0.016931   \n\n     maxfun   meandom    mindom    maxdom   dfrange   modindx  \n0  0.275862  0.007812  0.007812  0.007812  0.000000  0.000000  \n1  0.250000  0.009014  0.007812  0.054688  0.046875  0.052632  \n2  0.271186  0.007990  0.007812  0.015625  0.007812  0.046512  \n3  0.250000  0.201497  0.007812  0.562500  0.554688  0.247119  \n4  0.266667  0.712812  0.007812  5.484375  5.476562  0.208274  "
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Converting string value to int type for labels",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.preprocessing import LabelEncoder\ny=df.iloc[:,-1]\n\n# Encode label category\n# male -> 1\n# female -> 0\n\ngender_encoder = LabelEncoder()\ny = gender_encoder.fit_transform(y)\ny",
      "execution_count": 10,
      "outputs": [
        {
          "data": {
            "text/plain": "array([1, 1, 1, ..., 0, 0, 0])"
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Data Standardisation\nStandardization 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.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Scale the data to be between -1 and 1\nfrom sklearn.preprocessing import StandardScaler\nscaler = StandardScaler()\nscaler.fit(X)\nX = scaler.transform(X)",
      "execution_count": 11,
      "outputs": [],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Splitting dataset into training set and testing set for better generalisation",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)",
      "execution_count": 12,
      "outputs": [],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Running SVM with default hyperparameter.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.svm import SVC\nfrom sklearn import metrics\nsvc=SVC() #Default hyperparameters\nsvc.fit(X_train,y_train)\ny_pred=svc.predict(X_test)\nprint('Accuracy Score:')\nprint(metrics.accuracy_score(y_test,y_pred))",
      "execution_count": 13,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Accuracy Score:\n0.976340694006\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Default Linear kernel",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "svc=SVC(kernel='linear')\nsvc.fit(X_train,y_train)\ny_pred=svc.predict(X_test)\nprint('Accuracy Score:')\nprint(metrics.accuracy_score(y_test,y_pred))",
      "execution_count": 14,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Accuracy Score:\n0.977917981073\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Default RBF kernel",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "svc=SVC(kernel='rbf')\nsvc.fit(X_train,y_train)\ny_pred=svc.predict(X_test)\nprint('Accuracy Score:')\nprint(metrics.accuracy_score(y_test,y_pred))",
      "execution_count": 15,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Accuracy Score:\n0.976340694006\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can conclude from above that svm by default uses **rbf** kernel as a parameter for kernel",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Default Polynomial kernel",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "svc=SVC(kernel='poly')\nsvc.fit(X_train,y_train)\ny_pred=svc.predict(X_test)\nprint('Accuracy Score:')\nprint(metrics.accuracy_score(y_test,y_pred))",
      "execution_count": 16,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Accuracy Score:\n0.958990536278\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Polynomial kernel is performing poorly.The reason behind this maybe it is overfitting the training dataset",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Performing K-fold cross validation with different kernels",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# CV on Linear kernel",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.cross_validation import cross_val_score\nsvc=SVC(kernel='linear')\nscores = cross_val_score(svc, X, y, cv=10, scoring='accuracy') #cv is cross validation\nprint(scores)",
      "execution_count": 17,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "[ 0.91194969  0.97169811  0.97169811  0.97169811  0.96202532  0.98734177\n  0.99050633  0.98101266  0.95253165  0.99367089]\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can see above how the accuracy score is different everytime.This shows that accuracy score depends upon how the datasets got split.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print(scores.mean())",
      "execution_count": 18,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "0.969413263275\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "In K-fold cross validation we generally take the mean of all the scores.",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# CV on rbf kernel",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.cross_validation import cross_val_score\nsvc=SVC(kernel='rbf')\nscores = cross_val_score(svc, X, y, cv=10, scoring='accuracy') #cv is cross validation\nprint(scores)",
      "execution_count": 19,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "[ 0.93396226  0.95597484  0.96855346  0.9591195   0.96835443  0.99683544\n  0.98734177  0.98101266  0.91455696  0.99367089]\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print(scores.mean())",
      "execution_count": 20,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "0.965938221479\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# CV on Polynomial kernel",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.cross_validation import cross_val_score\nsvc=SVC(kernel='poly')\nscores = cross_val_score(svc, X, y, cv=10, scoring='accuracy') #cv is cross validation\nprint(scores)",
      "execution_count": 21,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "[ 0.89937107  0.94654088  0.93081761  0.91509434  0.95886076  1.\n  0.98101266  0.96835443  0.87974684  0.9778481 ]\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print(scores.mean())",
      "execution_count": 22,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "0.945764668418\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "**When K-fold cross validation is done we can see different score in each iteration.This happens because when we use train_test_split method,the dataset get split in random manner into testing and training dataset.Thus it depends on how the dataset got split and which samples are training set and which samples are in testing set.**\n\n**With K-fold cross validation we can see that the dataset got split into 10 equal parts thus covering all the data into training as well into testing set.This is the reason we got 10 different accuracy score.**",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Taking all the values of C and checking out the accuracy score with kernel as linear.",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "**The C parameter tells the SVM optimization how much you want to avoid misclassifying each training example. For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly. Conversely, a very small value of C will cause the optimizer to look for a larger-margin separating hyperplane, even if that hyperplane misclassifies more points.**\n\n**Thus for a very large values we can cause overfitting of the model and for a very small value of C we can cause underfitting.Thus the value of C must be chosen in such a manner that it generalised the unseen data well**",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "C_range=list(range(1,26))\nacc_score=[]\nfor c in C_range:\n    svc = SVC(kernel='linear', C=c)\n    scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n    acc_score.append(scores.mean())\nprint(acc_score)    \n    ",
      "execution_count": 23,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "[0.96941326327521682, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96909879786641184, 0.96941326327521682, 0.96909879786641184, 0.96878433245760687, 0.96846986704880178, 0.96846986704880178, 0.96846986704880178, 0.96846986704880178, 0.96815341135259914, 0.96815341135259914, 0.96815341135259914, 0.96815341135259914, 0.9678369556563966, 0.9678369556563966, 0.9678369556563966, 0.9678369556563966, 0.96815341135259914, 0.96846787676140433, 0.96846787676140433, 0.9681514210652018, 0.9681514210652018]\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\n%matplotlib inline\n\n\nC_values=list(range(1,26))\n# plot the value of C for SVM (x-axis) versus the cross-validated accuracy (y-axis)\nplt.plot(C_values,acc_score)\nplt.xticks(np.arange(0,27,2))\nplt.xlabel('Value of C for SVC')\nplt.ylabel('Cross-Validated Accuracy')",
      "execution_count": 24,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.text.Text at 0x7fc4d9e78fd0>"
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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CzN5sZj8BTgB/C1TgnZau/fcjJv3oinxpdw0lh9m+90Kbqygx89pcgWDSdioF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\nzl/Kxo3NbTxjxzL72aVsaErKXBrfkysNWQcgIiK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rpcvd/R/A74nE5zSinFY+Y+1nRGnz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\nSG78PzKHEgrxf7bYAAAAAElFTkSuQmCC\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7fc4dd0ec978>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "**From the above plot we can see that accuracy has been close to 97% for C=1 and C=6 and then it drops around 96.8% and remains constant.**",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Let us look into more detail of what is the exact value of C which is giving us a good accuracy score",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "C_range=list(np.arange(0.1,6,0.1))\nacc_score=[]\nfor c in C_range:\n    svc = SVC(kernel='linear', C=c)\n    scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n    acc_score.append(scores.mean())\nprint(acc_score)    \n    ",
      "execution_count": 25,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "[0.97004816495501944, 0.96910078815380951, 0.96909879786641184, 0.96909879786641184, 0.96941326327521682, 0.96941326327521682, 0.96972772868402191, 0.96941127298781937, 0.9690968075790144, 0.96941326327521682, 0.96941326327521682, 0.96941326327521682, 0.96941326327521682, 0.96878234217020953, 0.96909879786641184, 0.96909879786641184, 0.96909879786641184, 0.96909879786641184, 0.96878433245760687, 0.96878433245760687, 0.96909879786641184, 0.96909879786641184, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96878433245760687, 0.96909879786641184, 0.96909879786641184, 0.96909879786641184, 0.96909879786641184, 0.96909879786641184, 0.96909879786641184, 0.96909879786641184, 0.96909879786641184, 0.96909879786641184, 0.96941326327521682, 0.96941326327521682, 0.96941326327521682, 0.96941326327521682]\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\n%matplotlib inline\n\nC_values=list(np.arange(0.1,6,0.1))\n# plot the value of C for SVM (x-axis) versus the cross-validated accuracy (y-axis)\nplt.plot(C_values,acc_score)\nplt.xticks(np.arange(0.0,6,0.3))\nplt.xlabel('Value of C for SVC ')\nplt.ylabel('Cross-Validated Accuracy')",
      "execution_count": 26,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.text.Text at 0x7fc4d7948b38>"
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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3HIFEpSbXGj01u/dNkJ8Jp2EsldQkV0apWVhERBqp3NVPD1LZGU/ZpS+RUrqj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\nRESkEUpqREREpC0oqREREZG2oKRGRERE2oKSGhEREWkLSmp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pzMHAd9K2DwNrEL1ZJetT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\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7fc4dd0ec710>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Accuracy score is highest for C=0.1.",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Taking kernel as **rbf** and taking different values gamma",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "**Technically, the gamma parameter is the inverse of the standard deviation of the RBF kernel (Gaussian function), which is used as similarity measure between two points. Intuitively, a small gamma value define a Gaussian function with a large variance. In this case, two points can be considered similar even if are far from each other. In the other hand, a large gamma value means define a Gaussian function with a small variance and in this case, two points are considered similar just if they are close to each other**",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "gamma_range=[0.0001,0.001,0.01,0.1,1,10,100]\nacc_score=[]\nfor g in gamma_range:\n    svc = SVC(kernel='rbf', gamma=g)\n    scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n    acc_score.append(scores.mean())\nprint(acc_score)    \n    ",
      "execution_count": 27,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "[0.88781147997770871, 0.95524639757981067, 0.9681514210652018, 0.96309609107555116, 0.9072366849773108, 0.59553777565480459, 0.5]\n[0.88781147997770871, 0.95524639757981067, 0.9681514210652018, 0.96309609107555116, 0.9072366849773108, 0.59553777565480459, 0.5]\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\n%matplotlib inline\n\ngamma_range=[0.0001,0.001,0.01,0.1,1,10,100]\n\n# plot the value of C for SVM (x-axis) versus the cross-validated accuracy (y-axis)\nplt.plot(gamma_range,acc_score)\nplt.xlabel('Value of gamma for SVC ')\nplt.xticks(np.arange(0.0001,100,5))\nplt.ylabel('Cross-Validated Accuracy')",
      "execution_count": 28,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.text.Text at 0x7fc4d7380198>"
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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isjJEWd76DuBGYK+Z3QQcA9YDzyFoR/7C+NJbfXbt6MIPDPDA4SEmp1I01NdU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\n0sHnfwDojPsT/Df3ySau4gSckdn7D/E9MLM9+AT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            "text/plain": "<matplotlib.figure.Figure at 0x7fc4d73f0da0>"
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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isjJEWd76DuBGYK+Z3QQcA9YDzyFoR/7C+NJbfXbt6MIPDPDA4SEmp1I01NdU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\nRK5CYII=\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7fc4d73f0da0>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "**We can see that for gamma=10 and 100 the kernel is performing poorly.We can also see a slight dip in accuracy score when gamma is 1.Let us look into more details for the range 0.0001 to 0.1.**",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "gamma_range=[0.0001,0.001,0.01,0.1]\nacc_score=[]\nfor g in gamma_range:\n    svc = SVC(kernel='rbf', gamma=g)\n    scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n    acc_score.append(scores.mean())\nprint(acc_score)    \n    ",
      "execution_count": 29,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "[0.88781147997770871, 0.95524639757981067, 0.9681514210652018, 0.96309609107555116]\n[0.88781147997770871, 0.95524639757981067, 0.9681514210652018, 0.96309609107555116]\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\n%matplotlib inline\n\ngamma_range=[0.0001,0.001,0.01,0.1]\n\n# plot the value of C for SVM (x-axis) versus the cross-validated accuracy (y-axis)\nplt.plot(gamma_range,acc_score)\nplt.xlabel('Value of gamma for SVC ')\nplt.ylabel('Cross-Validated Accuracy')",
      "execution_count": 30,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.text.Text at 0x7fc4d7363828>"
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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Du88ys3HABOC05OtAOAdYFXjAzFqAGcBVwCmEPXAWY2anAQcAH3L3riwvVAkz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\nAAAAAElFTkSuQmCC\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7fc4d734af28>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "The score increases steadily and raches its peak at 0.01 and then decreases till gamma=1.Thus Gamma should be around 0.01.",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let us look into more detail for gamma values",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "gamma_range=[0.01,0.02,0.03,0.04,0.05]\nacc_score=[]\nfor g in gamma_range:\n    svc = SVC(kernel='rbf', gamma=g)\n    scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n    acc_score.append(scores.mean())\nprint(acc_score)    \n    ",
      "execution_count": 31,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "[0.9681514210652018, 0.96783894594379449, 0.96783496536899916, 0.96688957885518645, 0.96593822147918151]\n[0.9681514210652018, 0.96783894594379449, 0.96783496536899916, 0.96688957885518645, 0.96593822147918151]\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\n%matplotlib inline\n\ngamma_range=[0.01,0.02,0.03,0.04,0.05]\n\n# plot the value of C for SVM (x-axis) versus the cross-validated accuracy (y-axis)\nplt.plot(gamma_range,acc_score)\nplt.xlabel('Value of gamma for SVC ')\nplt.ylabel('Cross-Validated Accuracy')",
      "execution_count": 32,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.text.Text at 0x7fc4d73aff60>"
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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/fPULysrDfC+/W7JDE5EodZpJMrPHgA3AK8CJwNPAfznnDgH+BwgcqABFRNJR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\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7fc4d7356a90>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "**We can see there is constant decrease in the accuracy score as gamma value increase.Thus gamma=0.01 is the best parameter.**",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Taking polynomial kernel with different degree",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "degree=[2,3,4,5,6]\nacc_score=[]\nfor d in degree:\n    svc = SVC(kernel='poly', degree=d)\n    scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n    acc_score.append(scores.mean())\nprint(acc_score)    \n    ",
      "execution_count": 33,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "[0.85069262001432988, 0.94576466841811957, 0.83121168696759806, 0.86598200780192658, 0.77475718493750489]\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\n%matplotlib inline\n\ndegree=[2,3,4,5,6]\n\n# plot the value of C for SVM (x-axis) versus the cross-validated accuracy (y-axis)\nplt.plot(degree,acc_score,color='r')\nplt.xlabel('degrees for SVC ')\nplt.ylabel('Cross-Validated Accuracy')",
      "execution_count": 34,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.text.Text at 0x7fc4d72d2080>"
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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M5st7tp9/ItCpLUYySbJuXYILl8mCd8JSicOPINLzFgBcb7xG4dJF1gYSQoit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\nRK5CYII=\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7fc4d73569b0>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "**Score is high for third degree polynomial and then there is drop in the accuracy score as degree of polynomial increases.Thus increase in polynomial degree results in high complexity of the model and thus causes overfitting.**",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Now performing SVM by taking hyperparameter C=0.1 and kernel as linear \n\n\n----------",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.svm import SVC\nsvc= SVC(kernel='linear',C=0.1)\nsvc.fit(X_train,y_train)\ny_predict=svc.predict(X_test)\naccuracy_score= metrics.accuracy_score(y_test,y_predict)\nprint(accuracy_score)",
      "execution_count": 35,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "0.97476340694\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# With K-fold cross validation(where K=10)",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.cross_validation import cross_val_score\nsvc=SVC(kernel='linear',C=0.1)\nscores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\nprint(scores)",
      "execution_count": 36,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "[ 0.90880503  0.97169811  0.97484277  0.96855346  0.96202532  0.99050633\n  0.99050633  0.98101266  0.95886076  0.99367089]\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Taking the mean of all the scores",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print(scores.mean())",
      "execution_count": 37,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "0.970048164955\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "The accuracy is slightly good without K-fold cross validation but it may fail to generalise the unseen data.Hence it is advisable to perform K-fold cross validation where all the data is covered so it may predict unseen data well.",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Now performing SVM by taking hyperparameter gamma=0.01 and kernel as rbf",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.svm import SVC\nsvc= SVC(kernel='rbf',gamma=0.01)\nsvc.fit(X_train,y_train)\ny_predict=svc.predict(X_test)\nmetrics.accuracy_score(y_test,y_predict)",
      "execution_count": 38,
      "outputs": [
        {
          "data": {
            "text/plain": "0.96687697160883279"
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# With K-fold cross validation(where K=10)",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "svc=SVC(kernel='linear',gamma=0.01)\nscores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\nprint(scores)\nprint(scores.mean())",
      "execution_count": 39,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "[ 0.91194969  0.97169811  0.97169811  0.97169811  0.96202532  0.98734177\n  0.99050633  0.98101266  0.95253165  0.99367089]\n0.969413263275\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Now performing SVM by taking hyperparameter degree=3 and kernel as poly",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.svm import SVC\nsvc= SVC(kernel='poly',degree=3)\nsvc.fit(X_train,y_train)\ny_predict=svc.predict(X_test)\naccuracy_score= metrics.accuracy_score(y_test,y_predict)\nprint(accuracy_score)",
      "execution_count": 40,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "0.958990536278\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# With K-fold cross validation(where K=10)",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "svc=SVC(kernel='poly',degree=3)\nscores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\nprint(scores)\nprint(scores.mean())",
      "execution_count": 41,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "[ 0.89937107  0.94654088  0.93081761  0.91509434  0.95886076  1.\n  0.98101266  0.96835443  0.87974684  0.9778481 ]\n0.945764668418\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "#Let us perform Grid search technique to find the best parameter",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.svm import SVC\nsvm_model= SVC()",
      "execution_count": 42,
      "outputs": [],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "tuned_parameters = {\n 'C': (np.arange(0.1,1,0.1)) , 'kernel': ['linear'],\n 'C': (np.arange(0.1,1,0.1)) , 'gamma': [0.01,0.02,0.03,0.04,0.05], 'kernel': ['rbf'],\n 'degree': [2,3,4] ,'gamma':[0.01,0.02,0.03,0.04,0.05], 'C':(np.arange(0.1,1,0.1)) , 'kernel':['poly']\n                   }",
      "execution_count": 52,
      "outputs": [],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.grid_search import GridSearchCV\n\nmodel_svm = GridSearchCV(svm_model, tuned_parameters,cv=10,scoring='accuracy')",
      "execution_count": 53,
      "outputs": [],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "model_svm.fit(X_train, y_train)\nprint(model_svm.best_score_)",
      "execution_count": 54,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "0.9569850039463299\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print(model_svm.grid_scores_)",
      "execution_count": 55,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "[mean: 0.72612, std: 0.02657, params: {'C': 0.10000000000000001, 'kernel': 'poly', 'degree': 2, 'gamma': 0.01}, mean: 0.66732, std: 0.01472, params: {'C': 0.10000000000000001, 'kernel': 'poly', 'degree': 2, 'gamma': 0.02}, mean: 0.73639, std: 0.02183, params: {'C': 0.10000000000000001, 'kernel': 'poly', 'degree': 2, 'gamma': 0.03}, mean: 0.79242, std: 0.02043, params: {'C': 0.10000000000000001, 'kernel': 'poly', 'degree': 2, 'gamma': 0.04}, mean: 0.82952, std: 0.02263, params: {'C': 0.10000000000000001, 'kernel': 'poly', 'degree': 2, 'gamma': 0.05}, mean: 0.57893, std: 0.01547, params: {'C': 0.10000000000000001, 'kernel': 'poly', 'degree': 3, 'gamma': 0.01}, mean: 0.88477, std: 0.01732, params: {'C': 0.10000000000000001, 'kernel': 'poly', 'degree': 3, 'gamma': 0.02}, mean: 0.89227, std: 0.01451, params: {'C': 0.10000000000000001, 'kernel': 'poly', 'degree': 3, 'gamma': 0.03}, mean: 0.90489, std: 0.01413, params: {'C': 0.10000000000000001, 'kernel': 'poly', 'degree': 3, 'gamma': 0.04}, mean: 0.92305, std: 0.01402, params: {'C': 0.10000000000000001, 'kernel': 'poly', 'degree': 3, 'gamma': 0.05}, mean: 0.52013, std: 0.00624, params: {'C': 0.10000000000000001, 'kernel': 'poly', 'degree': 4, 'gamma': 0.01}, mean: 0.64957, std: 0.08461, params: {'C': 0.10000000000000001, 'kernel': 'poly', 'degree': 4, 'gamma': 0.02}, mean: 0.62628, std: 0.02313, params: {'C': 0.10000000000000001, 'kernel': 'poly', 'degree': 4, 'gamma': 0.03}, mean: 0.69140, std: 0.01449, params: {'C': 0.10000000000000001, 'kernel': 'poly', 'degree': 4, 'gamma': 0.04}, mean: 0.73717, std: 0.01137, params: {'C': 0.10000000000000001, 'kernel': 'poly', 'degree': 4, 'gamma': 0.05}, mean: 0.61918, std: 0.01962, params: {'C': 0.20000000000000001, 'kernel': 'poly', 'degree': 2, 'gamma': 0.01}, mean: 0.72928, std: 0.02083, params: {'C': 0.20000000000000001, 'kernel': 'poly', 'degree': 2, 'gamma': 0.02}, mean: 0.80584, std: 0.01981, params: {'C': 0.20000000000000001, 'kernel': 'poly', 'degree': 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mean: 0.93765, std: 0.01419, params: {'C': 0.59999999999999998, 'kernel': 'poly', 'degree': 3, 'gamma': 0.04}, mean: 0.94988, std: 0.01275, params: {'C': 0.59999999999999998, 'kernel': 'poly', 'degree': 3, 'gamma': 0.05}, mean: 0.52526, std: 0.00844, params: {'C': 0.59999999999999998, 'kernel': 'poly', 'degree': 4, 'gamma': 0.01}, mean: 0.63299, std: 0.02192, params: {'C': 0.59999999999999998, 'kernel': 'poly', 'degree': 4, 'gamma': 0.02}, mean: 0.72731, std: 0.01406, params: {'C': 0.59999999999999998, 'kernel': 'poly', 'degree': 4, 'gamma': 0.03}, mean: 0.79795, std: 0.00926, params: {'C': 0.59999999999999998, 'kernel': 'poly', 'degree': 4, 'gamma': 0.04}, mean: 0.85201, std: 0.01153, params: {'C': 0.59999999999999998, 'kernel': 'poly', 'degree': 4, 'gamma': 0.05}, mean: 0.71744, std: 0.01718, params: {'C': 0.70000000000000007, 'kernel': 'poly', 'degree': 2, 'gamma': 0.01}, mean: 0.83702, std: 0.02210, params: {'C': 0.70000000000000007, 'kernel': 'poly', 'degree': 2, 'gamma': 0.02}, mean: 0.86661, std: 0.01615, params: {'C': 0.70000000000000007, 'kernel': 'poly', 'degree': 2, 'gamma': 0.03}, mean: 0.88200, std: 0.01385, params: {'C': 0.70000000000000007, 'kernel': 'poly', 'degree': 2, 'gamma': 0.04}, mean: 0.89503, std: 0.01488, params: {'C': 0.70000000000000007, 'kernel': 'poly', 'degree': 2, 'gamma': 0.05}, mean: 0.88832, std: 0.01891, params: {'C': 0.70000000000000007, 'kernel': 'poly', 'degree': 3, 'gamma': 0.01}, mean: 0.89976, std: 0.01100, params: {'C': 0.70000000000000007, 'kernel': 'poly', 'degree': 3, 'gamma': 0.02}, mean: 0.92620, std: 0.01254, params: {'C': 0.70000000000000007, 'kernel': 'poly', 'degree': 3, 'gamma': 0.03}, mean: 0.93962, std: 0.01391, params: {'C': 0.70000000000000007, 'kernel': 'poly', 'degree': 3, 'gamma': 0.04}, mean: 0.95185, std: 0.01249, params: {'C': 0.70000000000000007, 'kernel': 'poly', 'degree': 3, 'gamma': 0.05}, mean: 0.52802, std: 0.00792, params: {'C': 0.70000000000000007, 'kernel': 'poly', 'degree': 4, 'gamma': 0.01}, mean: 0.63694, std: 0.02086, params: {'C': 0.70000000000000007, 'kernel': 'poly', 'degree': 4, 'gamma': 0.02}, mean: 0.73283, std: 0.01160, params: {'C': 0.70000000000000007, 'kernel': 'poly', 'degree': 4, 'gamma': 0.03}, mean: 0.80900, std: 0.01167, params: {'C': 0.70000000000000007, 'kernel': 'poly', 'degree': 4, 'gamma': 0.04}, mean: 0.85872, std: 0.01067, params: {'C': 0.70000000000000007, 'kernel': 'poly', 'degree': 4, 'gamma': 0.05}, mean: 0.72928, std: 0.02083, params: {'C': 0.80000000000000004, 'kernel': 'poly', 'degree': 2, 'gamma': 0.01}, mean: 0.83860, std: 0.02137, params: {'C': 0.80000000000000004, 'kernel': 'poly', 'degree': 2, 'gamma': 0.02}, mean: 0.86977, std: 0.01539, params: {'C': 0.80000000000000004, 'kernel': 'poly', 'degree': 2, 'gamma': 0.03}, mean: 0.88871, std: 0.01719, params: {'C': 0.80000000000000004, 'kernel': 'poly', 'degree': 2, 'gamma': 0.04}, mean: 0.89740, std: 0.01486, params: {'C': 0.80000000000000004, 'kernel': 'poly', 'degree': 2, 'gamma': 0.05}, mean: 0.88477, std: 0.01732, params: {'C': 0.80000000000000004, 'kernel': 'poly', 'degree': 3, 'gamma': 0.01}, mean: 0.90489, std: 0.01413, params: {'C': 0.80000000000000004, 'kernel': 'poly', 'degree': 3, 'gamma': 0.02}, mean: 0.92739, std: 0.01256, params: {'C': 0.80000000000000004, 'kernel': 'poly', 'degree': 3, 'gamma': 0.03}, mean: 0.94041, std: 0.01350, params: {'C': 0.80000000000000004, 'kernel': 'poly', 'degree': 3, 'gamma': 0.04}, mean: 0.95304, std: 0.01282, params: {'C': 0.80000000000000004, 'kernel': 'poly', 'degree': 3, 'gamma': 0.05}, mean: 0.53118, std: 0.00929, params: {'C': 0.80000000000000004, 'kernel': 'poly', 'degree': 4, 'gamma': 0.01}, mean: 0.64365, std: 0.01861, params: {'C': 0.80000000000000004, 'kernel': 'poly', 'degree': 4, 'gamma': 0.02}, mean: 0.74033, std: 0.00960, params: {'C': 0.80000000000000004, 'kernel': 'poly', 'degree': 4, 'gamma': 0.03}, mean: 0.81650, std: 0.01457, params: {'C': 0.80000000000000004, 'kernel': 'poly', 'degree': 4, 'gamma': 0.04}, mean: 0.86306, std: 0.01004, params: {'C': 0.80000000000000004, 'kernel': 'poly', 'degree': 4, 'gamma': 0.05}, mean: 0.73639, std: 0.02183, params: {'C': 0.90000000000000002, 'kernel': 'poly', 'degree': 2, 'gamma': 0.01}, mean: 0.84491, std: 0.01931, params: {'C': 0.90000000000000002, 'kernel': 'poly', 'degree': 2, 'gamma': 0.02}, mean: 0.87332, std: 0.01381, params: {'C': 0.90000000000000002, 'kernel': 'poly', 'degree': 2, 'gamma': 0.03}, mean: 0.89029, std: 0.01614, params: {'C': 0.90000000000000002, 'kernel': 'poly', 'degree': 2, 'gamma': 0.04}, mean: 0.90016, std: 0.01470, params: {'C': 0.90000000000000002, 'kernel': 'poly', 'degree': 2, 'gamma': 0.05}, mean: 0.88635, std: 0.02196, params: {'C': 0.90000000000000002, 'kernel': 'poly', 'degree': 3, 'gamma': 0.01}, mean: 0.90845, std: 0.01163, params: {'C': 0.90000000000000002, 'kernel': 'poly', 'degree': 3, 'gamma': 0.02}, mean: 0.92976, std: 0.01369, params: {'C': 0.90000000000000002, 'kernel': 'poly', 'degree': 3, 'gamma': 0.03}, mean: 0.94278, std: 0.01301, params: {'C': 0.90000000000000002, 'kernel': 'poly', 'degree': 3, 'gamma': 0.04}, mean: 0.95699, std: 0.01139, params: {'C': 0.90000000000000002, 'kernel': 'poly', 'degree': 3, 'gamma': 0.05}, mean: 0.53591, std: 0.00947, params: {'C': 0.90000000000000002, 'kernel': 'poly', 'degree': 4, 'gamma': 0.01}, mean: 0.65075, std: 0.02041, params: {'C': 0.90000000000000002, 'kernel': 'poly', 'degree': 4, 'gamma': 0.02}, mean: 0.74467, std: 0.00863, params: {'C': 0.90000000000000002, 'kernel': 'poly', 'degree': 4, 'gamma': 0.03}, mean: 0.82755, std: 0.01468, params: {'C': 0.90000000000000002, 'kernel': 'poly', 'degree': 4, 'gamma': 0.04}, mean: 0.86622, std: 0.01030, params: {'C': 0.90000000000000002, 'kernel': 'poly', 'degree': 4, 'gamma': 0.05}]\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print(model_svm.best_params_)",
      "execution_count": 56,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "{'C': 0.90000000000000002, 'kernel': 'poly', 'degree': 3, 'gamma': 0.05}\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "y_pred= model_svm.predict(X_test)\nprint(metrics.accuracy_score(y_pred,y_test))",
      "execution_count": 57,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "0.958990536278\n"
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "You can find my notebook on Github:\n(\"https://github.com/nirajvermafcb/Data-Science-with-python\")",
      "metadata": {}
    }
  ]
}