svm voice classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "XE5h55dmOkSL"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "g76JNaeBRxc8"
   },
   "outputs": [],
   "source": [
    "df=pd.read_excel(\"voice.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m4tk14Vmd7G-",
    "outputId": "7cdef058-13f5-47ba-d9ec-79fbc1562abb"
   },
   "outputs": [
    {
     "data": {
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       "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"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
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    "id": "VmKQHopOSmKf",
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       "      <th></th>\n",
       "      <th>meanfreq</th>\n",
       "      <th>sd</th>\n",
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       "      <th>Q25</th>\n",
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       "      <th>...</th>\n",
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       "      <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",
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       "      <th>label</th>\n",
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       "      <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",
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       "    <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",
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       "    <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",
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       "<p>5 rows × 21 columns</p>\n",
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      "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",
       "          kurt    sp.ent       sfm  ...  centroid   meanfun    minfun  \\\n",
       "0   274.402906  0.893369  0.491918  ...  0.059781  0.084279  0.015702   \n",
       "1   634.613855  0.892193  0.513724  ...  0.066009  0.107937  0.015826   \n",
       "2  1024.927705  0.846389  0.478905  ...  0.077316  0.098706  0.015656   \n",
       "3     4.177296  0.963322  0.727232  ...  0.151228  0.088965  0.017798   \n",
       "4     4.333713  0.971955  0.783568  ...  0.135120  0.106398  0.016931   \n",
       "\n",
       "     maxfun   meandom    mindom    maxdom   dfrange   modindx  label  \n",
       "0  0.275862  0.007812  0.007812  0.007812  0.000000  0.000000   male  \n",
       "1  0.250000  0.009014  0.007812  0.054688  0.046875  0.052632   male  \n",
       "2  0.271186  0.007990  0.007812  0.015625  0.007812  0.046512   male  \n",
       "3  0.250000  0.201497  0.007812  0.562500  0.554688  0.247119   male  \n",
       "4  0.266667  0.712812  0.007812  5.484375  5.476562  0.208274   male  \n",
       "\n",
       "[5 rows x 21 columns]"
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VhsnxFS-R2tv",
    "outputId": "5aa325d5-05cd-438a-eb1d-a1c5c0d71077"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3168 entries, 0 to 3167\n",
      "Data columns (total 21 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   meanfreq  3168 non-null   float64\n",
      " 1   sd        3168 non-null   float64\n",
      " 2   median    3168 non-null   float64\n",
      " 3   Q25       3168 non-null   float64\n",
      " 4   Q75       3168 non-null   float64\n",
      " 5   IQR       3168 non-null   float64\n",
      " 6   skew      3168 non-null   float64\n",
      " 7   kurt      3168 non-null   float64\n",
      " 8   sp.ent    3168 non-null   float64\n",
      " 9   sfm       3168 non-null   float64\n",
      " 10  mode      3168 non-null   float64\n",
      " 11  centroid  3168 non-null   float64\n",
      " 12  meanfun   3168 non-null   float64\n",
      " 13  minfun    3168 non-null   float64\n",
      " 14  maxfun    3168 non-null   float64\n",
      " 15  meandom   3168 non-null   float64\n",
      " 16  mindom    3168 non-null   float64\n",
      " 17  maxdom    3168 non-null   float64\n",
      " 18  dfrange   3168 non-null   float64\n",
      " 19  modindx   3168 non-null   float64\n",
      " 20  label     3168 non-null   object \n",
      "dtypes: float64(20), object(1)\n",
      "memory usage: 519.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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     "base_uri": "https://localhost:8080/",
     "height": 304
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    "id": "tciuCUg9R_iM",
    "outputId": "984cdbf1-df47-4a37-f2c1-0ad0f4e59539",
    "scrolled": true
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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",
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       "      <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3168.000000</td>\n",
       "      <td>3168.000000</td>\n",
       "      <td>3168.000000</td>\n",
       "      <td>3168.000000</td>\n",
       "      <td>3168.000000</td>\n",
       "      <td>3168.000000</td>\n",
       "      <td>3168.000000</td>\n",
       "      <td>3168.000000</td>\n",
       "      <td>3168.000000</td>\n",
       "      <td>3168.000000</td>\n",
       "      <td>3168.000000</td>\n",
       "      <td>3168.000000</td>\n",
       "      <td>3168.000000</td>\n",
       "      <td>3168.000000</td>\n",
       "      <td>3168.000000</td>\n",
       "      <td>3168.000000</td>\n",
       "      <td>3168.000000</td>\n",
       "      <td>3168.000000</td>\n",
       "      <td>3168.000000</td>\n",
       "      <td>3168.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.180907</td>\n",
       "      <td>0.057126</td>\n",
       "      <td>0.185621</td>\n",
       "      <td>0.140456</td>\n",
       "      <td>0.224765</td>\n",
       "      <td>0.084309</td>\n",
       "      <td>3.140168</td>\n",
       "      <td>36.568461</td>\n",
       "      <td>0.895127</td>\n",
       "      <td>0.408216</td>\n",
       "      <td>0.165282</td>\n",
       "      <td>0.180907</td>\n",
       "      <td>0.142807</td>\n",
       "      <td>0.036802</td>\n",
       "      <td>0.258842</td>\n",
       "      <td>0.829211</td>\n",
       "      <td>0.052647</td>\n",
       "      <td>5.047277</td>\n",
       "      <td>4.994630</td>\n",
       "      <td>0.173752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.029918</td>\n",
       "      <td>0.016652</td>\n",
       "      <td>0.036360</td>\n",
       "      <td>0.048680</td>\n",
       "      <td>0.023639</td>\n",
       "      <td>0.042783</td>\n",
       "      <td>4.240529</td>\n",
       "      <td>134.928661</td>\n",
       "      <td>0.044980</td>\n",
       "      <td>0.177521</td>\n",
       "      <td>0.077203</td>\n",
       "      <td>0.029918</td>\n",
       "      <td>0.032304</td>\n",
       "      <td>0.019220</td>\n",
       "      <td>0.030077</td>\n",
       "      <td>0.525205</td>\n",
       "      <td>0.063299</td>\n",
       "      <td>3.521157</td>\n",
       "      <td>3.520039</td>\n",
       "      <td>0.119454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.039363</td>\n",
       "      <td>0.018363</td>\n",
       "      <td>0.010975</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>0.042946</td>\n",
       "      <td>0.014558</td>\n",
       "      <td>0.141735</td>\n",
       "      <td>2.068455</td>\n",
       "      <td>0.738651</td>\n",
       "      <td>0.036876</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.039363</td>\n",
       "      <td>0.055565</td>\n",
       "      <td>0.009775</td>\n",
       "      <td>0.103093</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.004883</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.163662</td>\n",
       "      <td>0.041954</td>\n",
       "      <td>0.169593</td>\n",
       "      <td>0.111087</td>\n",
       "      <td>0.208747</td>\n",
       "      <td>0.042560</td>\n",
       "      <td>1.649569</td>\n",
       "      <td>5.669547</td>\n",
       "      <td>0.861811</td>\n",
       "      <td>0.258041</td>\n",
       "      <td>0.118016</td>\n",
       "      <td>0.163662</td>\n",
       "      <td>0.116998</td>\n",
       "      <td>0.018223</td>\n",
       "      <td>0.253968</td>\n",
       "      <td>0.419828</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>2.070312</td>\n",
       "      <td>2.044922</td>\n",
       "      <td>0.099766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.184838</td>\n",
       "      <td>0.059155</td>\n",
       "      <td>0.190032</td>\n",
       "      <td>0.140286</td>\n",
       "      <td>0.225684</td>\n",
       "      <td>0.094280</td>\n",
       "      <td>2.197101</td>\n",
       "      <td>8.318463</td>\n",
       "      <td>0.901767</td>\n",
       "      <td>0.396335</td>\n",
       "      <td>0.186599</td>\n",
       "      <td>0.184838</td>\n",
       "      <td>0.140519</td>\n",
       "      <td>0.046110</td>\n",
       "      <td>0.271186</td>\n",
       "      <td>0.765795</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>4.992188</td>\n",
       "      <td>4.945312</td>\n",
       "      <td>0.139357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.199146</td>\n",
       "      <td>0.067020</td>\n",
       "      <td>0.210618</td>\n",
       "      <td>0.175939</td>\n",
       "      <td>0.243660</td>\n",
       "      <td>0.114175</td>\n",
       "      <td>2.931694</td>\n",
       "      <td>13.648905</td>\n",
       "      <td>0.928713</td>\n",
       "      <td>0.533676</td>\n",
       "      <td>0.221104</td>\n",
       "      <td>0.199146</td>\n",
       "      <td>0.169581</td>\n",
       "      <td>0.047904</td>\n",
       "      <td>0.277457</td>\n",
       "      <td>1.177166</td>\n",
       "      <td>0.070312</td>\n",
       "      <td>7.007812</td>\n",
       "      <td>6.992188</td>\n",
       "      <td>0.209183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.251124</td>\n",
       "      <td>0.115273</td>\n",
       "      <td>0.261224</td>\n",
       "      <td>0.247347</td>\n",
       "      <td>0.273469</td>\n",
       "      <td>0.252225</td>\n",
       "      <td>34.725453</td>\n",
       "      <td>1309.612887</td>\n",
       "      <td>0.981997</td>\n",
       "      <td>0.842936</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.251124</td>\n",
       "      <td>0.237636</td>\n",
       "      <td>0.204082</td>\n",
       "      <td>0.279114</td>\n",
       "      <td>2.957682</td>\n",
       "      <td>0.458984</td>\n",
       "      <td>21.867188</td>\n",
       "      <td>21.843750</td>\n",
       "      <td>0.932374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          meanfreq           sd       median          Q25          Q75  \\\n",
       "count  3168.000000  3168.000000  3168.000000  3168.000000  3168.000000   \n",
       "mean      0.180907     0.057126     0.185621     0.140456     0.224765   \n",
       "std       0.029918     0.016652     0.036360     0.048680     0.023639   \n",
       "min       0.039363     0.018363     0.010975     0.000229     0.042946   \n",
       "25%       0.163662     0.041954     0.169593     0.111087     0.208747   \n",
       "50%       0.184838     0.059155     0.190032     0.140286     0.225684   \n",
       "75%       0.199146     0.067020     0.210618     0.175939     0.243660   \n",
       "max       0.251124     0.115273     0.261224     0.247347     0.273469   \n",
       "\n",
       "               IQR         skew         kurt       sp.ent          sfm  \\\n",
       "count  3168.000000  3168.000000  3168.000000  3168.000000  3168.000000   \n",
       "mean      0.084309     3.140168    36.568461     0.895127     0.408216   \n",
       "std       0.042783     4.240529   134.928661     0.044980     0.177521   \n",
       "min       0.014558     0.141735     2.068455     0.738651     0.036876   \n",
       "25%       0.042560     1.649569     5.669547     0.861811     0.258041   \n",
       "50%       0.094280     2.197101     8.318463     0.901767     0.396335   \n",
       "75%       0.114175     2.931694    13.648905     0.928713     0.533676   \n",
       "max       0.252225    34.725453  1309.612887     0.981997     0.842936   \n",
       "\n",
       "              mode     centroid      meanfun       minfun       maxfun  \\\n",
       "count  3168.000000  3168.000000  3168.000000  3168.000000  3168.000000   \n",
       "mean      0.165282     0.180907     0.142807     0.036802     0.258842   \n",
       "std       0.077203     0.029918     0.032304     0.019220     0.030077   \n",
       "min       0.000000     0.039363     0.055565     0.009775     0.103093   \n",
       "25%       0.118016     0.163662     0.116998     0.018223     0.253968   \n",
       "50%       0.186599     0.184838     0.140519     0.046110     0.271186   \n",
       "75%       0.221104     0.199146     0.169581     0.047904     0.277457   \n",
       "max       0.280000     0.251124     0.237636     0.204082     0.279114   \n",
       "\n",
       "           meandom       mindom       maxdom      dfrange      modindx  \n",
       "count  3168.000000  3168.000000  3168.000000  3168.000000  3168.000000  \n",
       "mean      0.829211     0.052647     5.047277     4.994630     0.173752  \n",
       "std       0.525205     0.063299     3.521157     3.520039     0.119454  \n",
       "min       0.007812     0.004883     0.007812     0.000000     0.000000  \n",
       "25%       0.419828     0.007812     2.070312     2.044922     0.099766  \n",
       "50%       0.765795     0.023438     4.992188     4.945312     0.139357  \n",
       "75%       1.177166     0.070312     7.007812     6.992188     0.209183  \n",
       "max       2.957682     0.458984    21.867188    21.843750     0.932374  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xH5HLfjESKTj",
    "outputId": "dc972351-a736-41cf-e905-e08d9875e21f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['female', 'male'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "obj1 =LabelEncoder()\n",
    "df[\"label\"] = obj1.fit_transform(df[\"label\"])\n",
    "obj1.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SFvQcERUefmB"
   },
   "source": [
    "# **Scaling Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 215
    },
    "id": "hHire76fSdfW",
    "outputId": "92bbf5b7-1399-449b-dc3d-f72eb48bf4e9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.096419</td>\n",
       "      <td>0.473409</td>\n",
       "      <td>0.084125</td>\n",
       "      <td>0.060063</td>\n",
       "      <td>0.204956</td>\n",
       "      <td>0.254828</td>\n",
       "      <td>0.367853</td>\n",
       "      <td>0.208279</td>\n",
       "      <td>0.635798</td>\n",
       "      <td>0.564526</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096419</td>\n",
       "      <td>0.157706</td>\n",
       "      <td>0.030501</td>\n",
       "      <td>0.981526</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006452</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.125828</td>\n",
       "      <td>0.505075</td>\n",
       "      <td>0.116900</td>\n",
       "      <td>0.077635</td>\n",
       "      <td>0.215683</td>\n",
       "      <td>0.246961</td>\n",
       "      <td>0.644279</td>\n",
       "      <td>0.483766</td>\n",
       "      <td>0.630964</td>\n",
       "      <td>0.591578</td>\n",
       "      <td>...</td>\n",
       "      <td>0.125828</td>\n",
       "      <td>0.287642</td>\n",
       "      <td>0.031140</td>\n",
       "      <td>0.834600</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.006452</td>\n",
       "      <td>0.002144</td>\n",
       "      <td>0.002146</td>\n",
       "      <td>0.056449</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.179222</td>\n",
       "      <td>0.675536</td>\n",
       "      <td>0.102873</td>\n",
       "      <td>0.034284</td>\n",
       "      <td>0.385912</td>\n",
       "      <td>0.457148</td>\n",
       "      <td>0.885255</td>\n",
       "      <td>0.782275</td>\n",
       "      <td>0.442738</td>\n",
       "      <td>0.548382</td>\n",
       "      <td>...</td>\n",
       "      <td>0.179222</td>\n",
       "      <td>0.236945</td>\n",
       "      <td>0.030264</td>\n",
       "      <td>0.954963</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.006452</td>\n",
       "      <td>0.000357</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>0.049885</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.528261</td>\n",
       "      <td>0.554611</td>\n",
       "      <td>0.587559</td>\n",
       "      <td>0.389906</td>\n",
       "      <td>0.715802</td>\n",
       "      <td>0.407358</td>\n",
       "      <td>0.031549</td>\n",
       "      <td>0.001613</td>\n",
       "      <td>0.923261</td>\n",
       "      <td>0.856457</td>\n",
       "      <td>...</td>\n",
       "      <td>0.528261</td>\n",
       "      <td>0.183442</td>\n",
       "      <td>0.041287</td>\n",
       "      <td>0.834600</td>\n",
       "      <td>0.065659</td>\n",
       "      <td>0.006452</td>\n",
       "      <td>0.025375</td>\n",
       "      <td>0.025393</td>\n",
       "      <td>0.265043</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.452195</td>\n",
       "      <td>0.627209</td>\n",
       "      <td>0.454272</td>\n",
       "      <td>0.317627</td>\n",
       "      <td>0.707515</td>\n",
       "      <td>0.474474</td>\n",
       "      <td>0.027742</td>\n",
       "      <td>0.001732</td>\n",
       "      <td>0.958736</td>\n",
       "      <td>0.926348</td>\n",
       "      <td>...</td>\n",
       "      <td>0.452195</td>\n",
       "      <td>0.279190</td>\n",
       "      <td>0.036829</td>\n",
       "      <td>0.929285</td>\n",
       "      <td>0.238994</td>\n",
       "      <td>0.006452</td>\n",
       "      <td>0.250536</td>\n",
       "      <td>0.250715</td>\n",
       "      <td>0.223380</td>\n",
       "      <td>1.0</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  \\\n",
       "0  0.096419  0.473409  0.084125  0.060063  0.204956  0.254828  0.367853   \n",
       "1  0.125828  0.505075  0.116900  0.077635  0.215683  0.246961  0.644279   \n",
       "2  0.179222  0.675536  0.102873  0.034284  0.385912  0.457148  0.885255   \n",
       "3  0.528261  0.554611  0.587559  0.389906  0.715802  0.407358  0.031549   \n",
       "4  0.452195  0.627209  0.454272  0.317627  0.707515  0.474474  0.027742   \n",
       "\n",
       "       kurt    sp.ent       sfm  ...  centroid   meanfun    minfun    maxfun  \\\n",
       "0  0.208279  0.635798  0.564526  ...  0.096419  0.157706  0.030501  0.981526   \n",
       "1  0.483766  0.630964  0.591578  ...  0.125828  0.287642  0.031140  0.834600   \n",
       "2  0.782275  0.442738  0.548382  ...  0.179222  0.236945  0.030264  0.954963   \n",
       "3  0.001613  0.923261  0.856457  ...  0.528261  0.183442  0.041287  0.834600   \n",
       "4  0.001732  0.958736  0.926348  ...  0.452195  0.279190  0.036829  0.929285   \n",
       "\n",
       "    meandom    mindom    maxdom   dfrange   modindx  label  \n",
       "0  0.000000  0.006452  0.000000  0.000000  0.000000    1.0  \n",
       "1  0.000407  0.006452  0.002144  0.002146  0.056449    1.0  \n",
       "2  0.000060  0.006452  0.000357  0.000358  0.049885    1.0  \n",
       "3  0.065659  0.006452  0.025375  0.025393  0.265043    1.0  \n",
       "4  0.238994  0.006452  0.250536  0.250715  0.223380    1.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "obj2=MinMaxScaler()\n",
    "df[:]=obj2.fit_transform(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2gzFyI4sdEiw"
   },
   "source": [
    "# **Spliting Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "TVPIwpUsTx-o"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "id": "M9d02rzjWr5z",
    "outputId": "f0980777-4d75-42d8-bae3-fb54ef825c85"
   },
   "outputs": [],
   "source": [
    "x=df.iloc[:,:-1]\n",
    "y=df['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "Ebc6F9bxag-y"
   },
   "outputs": [],
   "source": [
    "x_train1,x_test1,y_train,y_test=train_test_split(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "upahpnqFapvZ"
   },
   "outputs": [],
   "source": [
    "x_train2=x_train1[[\"meanfun\",\"IQR\",\"Q25\"]]\n",
    "x_test2=x_test1[[\"meanfun\",\"IQR\",\"Q25\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "nFZAW5WXbjXQ"
   },
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nps9NdPXdJ4K"
   },
   "source": [
    "# **Running Models and comparing results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "veAynPfSbUhi",
    "outputId": "157ce57f-a8b4-42ac-b587-c61609b6b026"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rbf  accuracy is:  0.9709595959595959\n",
      "poly  accuracy is:  0.9659090909090909\n",
      "linear  accuracy is:  0.9633838383838383\n",
      "Highest accuracy is 0.9709595959595959 occurs at rbf kernel.\n"
     ]
    }
   ],
   "source": [
    "list1=[]\n",
    "kernel=['rbf','poly','linear']\n",
    "for i in kernel:\n",
    "        model=svm.SVC(kernel=i).fit(x_train2,y_train)\n",
    "        y_pred=model.predict(x_test2)\n",
    "        print(i, ' accuracy is: ', accuracy_score(y_test,y_pred))\n",
    "        list1.append(accuracy_score(y_test,y_pred))\n",
    "pos = list1.index(max(list1))\n",
    "print(\"Highest accuracy is %s occurs at %s kernel.\" % (list1[pos], kernel[pos]))"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "voice.ipynb",
   "provenance": []
  },
  "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": "3.7.6"
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