Classification_Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# importing essential libraries\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# importing dataset using pandas\n",
    "train_data= pd.read_csv(\"C:/Users/satya naidu/Desktop/Leaf Classification/train/train.csv\")\n",
    "test_data = pd.read_csv(r\"C:/Users/satya naidu/Desktop/Leaf Classification/test/test.csv\")\n",
    "results= pd.DataFrame.copy(test_data)\n",
    "test_data=test_data.drop([\"id\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(990, 194)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking shape and first few observations\n",
    "train_data.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>species</th>\n",
       "      <th>margin1</th>\n",
       "      <th>margin2</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Acer_Opalus</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Pterocarya_Stenoptera</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.015625</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Quercus_Hartwissiana</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.019531</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.068359</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.154300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020508</td>\n",
       "      <td>0.002930</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>Tilia_Tomentosa</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.023438</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.021484</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017578</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047852</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>Quercus_Variabilis</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.048828</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.013672</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "<p>5 rows × 194 columns</p>\n",
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       "   id                species   margin1   margin2   margin3   margin4  \\\n",
       "0   1            Acer_Opalus  0.007812  0.023438  0.023438  0.003906   \n",
       "1   2  Pterocarya_Stenoptera  0.005859  0.000000  0.031250  0.015625   \n",
       "2   3   Quercus_Hartwissiana  0.005859  0.009766  0.019531  0.007812   \n",
       "3   5        Tilia_Tomentosa  0.000000  0.003906  0.023438  0.005859   \n",
       "4   6     Quercus_Variabilis  0.005859  0.003906  0.048828  0.009766   \n",
       "\n",
       "    margin5   margin6   margin7  margin8    ...      texture55  texture56  \\\n",
       "0  0.011719  0.009766  0.027344      0.0    ...       0.007812   0.000000   \n",
       "1  0.025391  0.001953  0.019531      0.0    ...       0.000977   0.000000   \n",
       "2  0.003906  0.005859  0.068359      0.0    ...       0.154300   0.000000   \n",
       "3  0.021484  0.019531  0.023438      0.0    ...       0.000000   0.000977   \n",
       "4  0.013672  0.015625  0.005859      0.0    ...       0.096680   0.000000   \n",
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       "0   0.002930   0.002930   0.035156        0.0        0.0   0.004883   \n",
       "1   0.000000   0.000977   0.023438        0.0        0.0   0.000977   \n",
       "2   0.005859   0.000977   0.007812        0.0        0.0   0.000000   \n",
       "3   0.000000   0.000000   0.020508        0.0        0.0   0.017578   \n",
       "4   0.021484   0.000000   0.000000        0.0        0.0   0.000000   \n",
       "\n",
       "   texture63  texture64  \n",
       "0   0.000000   0.025391  \n",
       "1   0.039062   0.022461  \n",
       "2   0.020508   0.002930  \n",
       "3   0.000000   0.047852  \n",
       "4   0.000000   0.031250  \n",
       "\n",
       "[5 rows x 194 columns]"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 990 entries, 0 to 989\n",
      "Columns: 193 entries, species to texture64\n",
      "dtypes: float64(192), object(1)\n",
      "memory usage: 1.5+ MB\n"
     ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>margin1</th>\n",
       "      <th>margin2</th>\n",
       "      <th>margin3</th>\n",
       "      <th>margin4</th>\n",
       "      <th>margin5</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>990.000000</td>\n",
       "      <td>990.000000</td>\n",
       "      <td>990.000000</td>\n",
       "      <td>990.000000</td>\n",
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       "      <td>990.000000</td>\n",
       "      <td>990.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.017412</td>\n",
       "      <td>0.028539</td>\n",
       "      <td>0.031988</td>\n",
       "      <td>0.023280</td>\n",
       "      <td>0.014264</td>\n",
       "      <td>0.038579</td>\n",
       "      <td>0.019202</td>\n",
       "      <td>0.001083</td>\n",
       "      <td>0.007167</td>\n",
       "      <td>0.018639</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036501</td>\n",
       "      <td>0.005024</td>\n",
       "      <td>0.015944</td>\n",
       "      <td>0.011586</td>\n",
       "      <td>0.016108</td>\n",
       "      <td>0.014017</td>\n",
       "      <td>0.002688</td>\n",
       "      <td>0.020291</td>\n",
       "      <td>0.008989</td>\n",
       "      <td>0.019420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.019739</td>\n",
       "      <td>0.038855</td>\n",
       "      <td>0.025847</td>\n",
       "      <td>0.028411</td>\n",
       "      <td>0.018390</td>\n",
       "      <td>0.052030</td>\n",
       "      <td>0.017511</td>\n",
       "      <td>0.002743</td>\n",
       "      <td>0.008933</td>\n",
       "      <td>0.016071</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063403</td>\n",
       "      <td>0.019321</td>\n",
       "      <td>0.023214</td>\n",
       "      <td>0.025040</td>\n",
       "      <td>0.015335</td>\n",
       "      <td>0.060151</td>\n",
       "      <td>0.011415</td>\n",
       "      <td>0.039040</td>\n",
       "      <td>0.013791</td>\n",
       "      <td>0.022768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.013672</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004883</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.011719</td>\n",
       "      <td>0.025391</td>\n",
       "      <td>0.013672</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004883</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.012695</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.002930</td>\n",
       "      <td>0.011719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.025391</td>\n",
       "      <td>0.041016</td>\n",
       "      <td>0.044922</td>\n",
       "      <td>0.029297</td>\n",
       "      <td>0.017578</td>\n",
       "      <td>0.056153</td>\n",
       "      <td>0.029297</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.027344</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022217</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.021484</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>0.012695</td>\n",
       "      <td>0.029297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.087891</td>\n",
       "      <td>0.205080</td>\n",
       "      <td>0.156250</td>\n",
       "      <td>0.169920</td>\n",
       "      <td>0.111330</td>\n",
       "      <td>0.310550</td>\n",
       "      <td>0.091797</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.076172</td>\n",
       "      <td>0.097656</td>\n",
       "      <td>...</td>\n",
       "      <td>0.429690</td>\n",
       "      <td>0.202150</td>\n",
       "      <td>0.172850</td>\n",
       "      <td>0.200200</td>\n",
       "      <td>0.106450</td>\n",
       "      <td>0.578130</td>\n",
       "      <td>0.151370</td>\n",
       "      <td>0.375980</td>\n",
       "      <td>0.086914</td>\n",
       "      <td>0.141600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 192 columns</p>\n",
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      ],
      "text/plain": [
       "          margin1     margin2     margin3     margin4     margin5     margin6  \\\n",
       "count  990.000000  990.000000  990.000000  990.000000  990.000000  990.000000   \n",
       "mean     0.017412    0.028539    0.031988    0.023280    0.014264    0.038579   \n",
       "std      0.019739    0.038855    0.025847    0.028411    0.018390    0.052030   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.001953    0.001953    0.013672    0.005859    0.001953    0.000000   \n",
       "50%      0.009766    0.011719    0.025391    0.013672    0.007812    0.015625   \n",
       "75%      0.025391    0.041016    0.044922    0.029297    0.017578    0.056153   \n",
       "max      0.087891    0.205080    0.156250    0.169920    0.111330    0.310550   \n",
       "\n",
       "          margin7     margin8     margin9    margin10     ...       texture55  \\\n",
       "count  990.000000  990.000000  990.000000  990.000000     ...      990.000000   \n",
       "mean     0.019202    0.001083    0.007167    0.018639     ...        0.036501   \n",
       "std      0.017511    0.002743    0.008933    0.016071     ...        0.063403   \n",
       "min      0.000000    0.000000    0.000000    0.000000     ...        0.000000   \n",
       "25%      0.005859    0.000000    0.001953    0.005859     ...        0.000000   \n",
       "50%      0.015625    0.000000    0.005859    0.015625     ...        0.004883   \n",
       "75%      0.029297    0.000000    0.007812    0.027344     ...        0.043701   \n",
       "max      0.091797    0.031250    0.076172    0.097656     ...        0.429690   \n",
       "\n",
       "        texture56   texture57   texture58   texture59   texture60   texture61  \\\n",
       "count  990.000000  990.000000  990.000000  990.000000  990.000000  990.000000   \n",
       "mean     0.005024    0.015944    0.011586    0.016108    0.014017    0.002688   \n",
       "std      0.019321    0.023214    0.025040    0.015335    0.060151    0.011415   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000977    0.000000    0.004883    0.000000    0.000000   \n",
       "50%      0.000000    0.005859    0.000977    0.012695    0.000000    0.000000   \n",
       "75%      0.000000    0.022217    0.009766    0.021484    0.000000    0.000000   \n",
       "max      0.202150    0.172850    0.200200    0.106450    0.578130    0.151370   \n",
       "\n",
       "        texture62   texture63   texture64  \n",
       "count  990.000000  990.000000  990.000000  \n",
       "mean     0.020291    0.008989    0.019420  \n",
       "std      0.039040    0.013791    0.022768  \n",
       "min      0.000000    0.000000    0.000000  \n",
       "25%      0.000000    0.000000    0.000977  \n",
       "50%      0.003906    0.002930    0.011719  \n",
       "75%      0.023438    0.012695    0.029297  \n",
       "max      0.375980    0.086914    0.141600  \n",
       "\n",
       "[8 rows x 192 columns]"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# just a glimpse of data\n",
    "train_data.info()\n",
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species      0\n",
       "margin1      0\n",
       "margin2      0\n",
       "margin3      0\n",
       "margin4      0\n",
       "margin5      0\n",
       "margin6      0\n",
       "margin7      0\n",
       "margin8      0\n",
       "margin9      0\n",
       "margin10     0\n",
       "margin11     0\n",
       "margin12     0\n",
       "margin13     0\n",
       "margin14     0\n",
       "margin15     0\n",
       "margin16     0\n",
       "margin17     0\n",
       "margin18     0\n",
       "margin19     0\n",
       "margin20     0\n",
       "margin21     0\n",
       "margin22     0\n",
       "margin23     0\n",
       "margin24     0\n",
       "margin25     0\n",
       "margin26     0\n",
       "margin27     0\n",
       "margin28     0\n",
       "margin29     0\n",
       "            ..\n",
       "texture35    0\n",
       "texture36    0\n",
       "texture37    0\n",
       "texture38    0\n",
       "texture39    0\n",
       "texture40    0\n",
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       "texture58    0\n",
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       "texture60    0\n",
       "texture61    0\n",
       "texture62    0\n",
       "texture63    0\n",
       "texture64    0\n",
       "Length: 193, dtype: int64"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking data balance and unique values\n",
    "train_data.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species\n",
       "Acer_Capillipes                 10\n",
       "Acer_Circinatum                 10\n",
       "Acer_Mono                       10\n",
       "Acer_Opalus                     10\n",
       "Acer_Palmatum                   10\n",
       "Acer_Pictum                     10\n",
       "Acer_Platanoids                 10\n",
       "Acer_Rubrum                     10\n",
       "Acer_Rufinerve                  10\n",
       "Acer_Saccharinum                10\n",
       "Alnus_Cordata                   10\n",
       "Alnus_Maximowiczii              10\n",
       "Alnus_Rubra                     10\n",
       "Alnus_Sieboldiana               10\n",
       "Alnus_Viridis                   10\n",
       "Arundinaria_Simonii             10\n",
       "Betula_Austrosinensis           10\n",
       "Betula_Pendula                  10\n",
       "Callicarpa_Bodinieri            10\n",
       "Castanea_Sativa                 10\n",
       "Celtis_Koraiensis               10\n",
       "Cercis_Siliquastrum             10\n",
       "Cornus_Chinensis                10\n",
       "Cornus_Controversa              10\n",
       "Cornus_Macrophylla              10\n",
       "Cotinus_Coggygria               10\n",
       "Crataegus_Monogyna              10\n",
       "Cytisus_Battandieri             10\n",
       "Eucalyptus_Glaucescens          10\n",
       "Eucalyptus_Neglecta             10\n",
       "                                ..\n",
       "Quercus_Kewensis                10\n",
       "Quercus_Nigra                   10\n",
       "Quercus_Palustris               10\n",
       "Quercus_Phellos                 10\n",
       "Quercus_Phillyraeoides          10\n",
       "Quercus_Pontica                 10\n",
       "Quercus_Pubescens               10\n",
       "Quercus_Pyrenaica               10\n",
       "Quercus_Rhysophylla             10\n",
       "Quercus_Rubra                   10\n",
       "Quercus_Semecarpifolia          10\n",
       "Quercus_Shumardii               10\n",
       "Quercus_Suber                   10\n",
       "Quercus_Texana                  10\n",
       "Quercus_Trojana                 10\n",
       "Quercus_Variabilis              10\n",
       "Quercus_Vulcanica               10\n",
       "Quercus_x_Hispanica             10\n",
       "Quercus_x_Turneri               10\n",
       "Rhododendron_x_Russellianum     10\n",
       "Salix_Fragilis                  10\n",
       "Salix_Intergra                  10\n",
       "Sorbus_Aria                     10\n",
       "Tilia_Oliveri                   10\n",
       "Tilia_Platyphyllos              10\n",
       "Tilia_Tomentosa                 10\n",
       "Ulmus_Bergmanniana              10\n",
       "Viburnum_Tinus                  10\n",
       "Viburnum_x_Rhytidophylloides    10\n",
       "Zelkova_Serrata                 10\n",
       "Length: 99, dtype: int64"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['species'].unique()\n",
    "train_data.groupby(['species']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# removing unwanted coulmn\n",
    "train_data=train_data.drop(['id'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking columns which are completely null because of computational expenses\n",
    "columns_sum=train_data.loc[:,train_data.columns!='species'].sum()\n",
    "check_func=lambda x: x>0\n",
    "check_bool= check_func(columns_sum)\n",
    "[null_columns for null_columns, x in enumerate(check_bool) if not x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#preparing data and splitting for model purpose\n",
    "X_train_data=train_data.iloc[:,train_data.columns!='species']\n",
    "y_train_data= train_data.loc[:,'species']\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "sc_y=LabelEncoder()\n",
    "y_train_data=sc_y.fit_transform(y_train_data)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X_train_data,y_train_data,test_size=0.25,random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n",
    "test_data=sc.transform(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98517117117117137"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using features without dimensionality reduction technique\n",
    "# preparing linear SVM \n",
    "from sklearn.svm import SVC\n",
    "svc_classifier = SVC(kernel = 'linear', random_state = 0,C=1)\n",
    "accuracy=cross_val_score(svc_classifier,X_train,y_train,cv=KFold(n_splits=10,random_state=6),scoring=\"accuracy\")\n",
    "accuracy.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.24477109,  0.10449513,  0.06173856,  0.05571022,  0.04288401,\n",
       "        0.0345488 ,  0.03208416,  0.02799658,  0.0246141 ,  0.01976853])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using PCA as dimensionality reduction because of numerical data\n",
    "from sklearn.decomposition import PCA\n",
    "pca= PCA(n_components=10)\n",
    "X_train=pca.fit_transform(X_train)\n",
    "X_test=pca.transform(X_test)\n",
    "test_data=pca.transform(test_data)\n",
    "explained_variance=pca.explained_variance_ratio_\n",
    "explained_variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# preparing Kernel SVM\n",
    "from sklearn.svm import SVC\n",
    "K_svc = SVC(kernel = 'rbf', random_state = 0,C=10,gamma=0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# preparing linear SVM \n",
    "from sklearn.svm import SVC\n",
    "L_svc = SVC(kernel = 'linear', random_state = 0,C=1,gamma=0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#preparing ensemble\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfClassifier=RandomForestClassifier(n_estimators = 10, random_state = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# preparing Decision tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "DT= DecisionTreeClassifier(criterion = 'entropy', random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# finding best model using cross validation\n",
    "from sklearn.model_selection import cross_val_score,cross_val_predict,KFold\n",
    "mean=[]\n",
    "std=[]\n",
    "lists=['Linear SVC','Random Forest','Decision Trees','Kernel SVC']\n",
    "estimators=[L_svc,rfClassifier,DT,K_svc]\n",
    "for model in estimators:\n",
    "    accuracy=cross_val_score(model,X_train,y_train,cv=KFold(n_splits=10,random_state=6),scoring=\"accuracy\")\n",
    "    mean.append(accuracy.mean())\n",
    "    std.append(accuracy.std())\n",
    "model_values = pd.DataFrame({\"mean\":mean,'std':std},index=lists)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear SVC</th>\n",
       "      <td>0.940721</td>\n",
       "      <td>0.030276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.866613</td>\n",
       "      <td>0.036280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Trees</th>\n",
       "      <td>0.672486</td>\n",
       "      <td>0.055815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kernel SVC</th>\n",
       "      <td>0.865315</td>\n",
       "      <td>0.052401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    mean       std\n",
       "Linear SVC      0.940721  0.030276\n",
       "Random Forest   0.866613  0.036280\n",
       "Decision Trees  0.672486  0.055815\n",
       "Kernel SVC      0.865315  0.052401"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma=0.1, kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=0, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L_svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV,KFold\n",
    "parameters = [{'C': [1, 10,20], 'kernel': ['linear'],'gamma': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}]\n",
    "grid_search = GridSearchCV(estimator = L_svc,\n",
    "                           param_grid = parameters,\n",
    "                           scoring = 'accuracy',\n",
    "                           cv = KFold(n_splits=10,random_state=6),\n",
    "                           n_jobs = -1)\n",
    "grid_search = grid_search.fit(X_train, y_train)\n",
    "best_accuracy = grid_search.best_score_\n",
    "best_parameters = grid_search.best_params_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94070080862533689"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1, 'gamma': 0.1, 'kernel': 'linear'}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred=L_svc.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results['spices_id']=y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([51, 50,  1, 19, 14,  3, 29, 84,  8, 43, 74, 75, 10, 52, 46, 45, 73,\n",
       "       13, 71, 61, 68, 57, 77, 70, 28, 15, 35, 53, 47,  4, 36, 55, 93, 32,\n",
       "        9, 38, 23, 94, 18, 17,  5, 86, 62, 33, 98, 88, 56, 21, 59, 65, 11,\n",
       "       48, 54,  7, 31, 92, 25, 85, 24,  2, 83, 40, 22, 90, 37, 41, 95, 89,\n",
       "       12, 96, 64, 72, 67, 30, 60, 87,  6, 34, 27, 44, 39, 97, 81, 42, 20,\n",
       "       79, 58, 49, 91, 80, 78,  0, 76, 82, 26, 63, 69, 16, 66], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['spices_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>margin1</th>\n",
       "      <th>margin2</th>\n",
       "      <th>margin3</th>\n",
       "      <th>margin4</th>\n",
       "      <th>margin5</th>\n",
       "      <th>margin6</th>\n",
       "      <th>margin7</th>\n",
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       "      <th>texture64</th>\n",
       "      <th>spices_id</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>0.019531</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.078125</td>\n",
       "      <td>0.011719</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.053711</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.064453</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.013672</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.033203</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006836</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.013672</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.037109</td>\n",
       "      <td>0.044922</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.021484</td>\n",
       "      <td>0.041016</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011719</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.011719</td>\n",
       "      <td>0.017578</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.002930</td>\n",
       "      <td>0.036133</td>\n",
       "      <td>0.013672</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.089844</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008789</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.039062</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042969</td>\n",
       "      <td>0.016602</td>\n",
       "      <td>0.010742</td>\n",
       "      <td>0.041016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 194 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id   margin1   margin2   margin3   margin4   margin5   margin6   margin7  \\\n",
       "0   4  0.019531  0.009766  0.078125  0.011719  0.003906  0.015625  0.005859   \n",
       "1   7  0.007812  0.005859  0.064453  0.009766  0.003906  0.013672  0.007812   \n",
       "2   9  0.000000  0.000000  0.001953  0.021484  0.041016  0.000000  0.023438   \n",
       "3  12  0.000000  0.000000  0.009766  0.011719  0.017578  0.000000  0.003906   \n",
       "4  13  0.001953  0.000000  0.015625  0.009766  0.039062  0.000000  0.009766   \n",
       "\n",
       "   margin8   margin9    ...      texture56  texture57  texture58  texture59  \\\n",
       "0      0.0  0.005859    ...       0.000000   0.015625   0.000977   0.015625   \n",
       "1      0.0  0.033203    ...       0.000000   0.006836   0.001953   0.013672   \n",
       "2      0.0  0.011719    ...       0.000000   0.000977   0.000000   0.000000   \n",
       "3      0.0  0.003906    ...       0.015625   0.002930   0.036133   0.013672   \n",
       "4      0.0  0.005859    ...       0.042969   0.016602   0.010742   0.041016   \n",
       "\n",
       "   texture60  texture61  texture62  texture63  texture64  spices_id  \n",
       "0        0.0        0.0   0.000000   0.003906   0.053711         51  \n",
       "1        0.0        0.0   0.000977   0.037109   0.044922         50  \n",
       "2        0.0        0.0   0.015625   0.000000   0.000000          1  \n",
       "3        0.0        0.0   0.089844   0.000000   0.008789         19  \n",
       "4        0.0        0.0   0.007812   0.009766   0.007812         14  \n",
       "\n",
       "[5 rows x 194 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results.to_csv(\"C:/Users/satya naidu/Desktop/Leaf Classification/model_results_submission.csv\",index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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