Iris ML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from matplotlib.colors import ListedColormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=pd.read_csv('F:\\ML_Project\\IRIS\\Iris.csv')\n",
    "iris = pd.DataFrame(a)\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 6)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43.445368</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.750000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
       "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
       "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
       "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
       "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
       "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
       "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
       "max    150.000000       7.900000      4.400000       6.900000      2.500000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 columns):\n",
      "Id               150 non-null int64\n",
      "SepalLengthCm    150 non-null float64\n",
      "SepalWidthCm     150 non-null float64\n",
      "PetalLengthCm    150 non-null float64\n",
      "PetalWidthCm     150 non-null float64\n",
      "Species          150 non-null object\n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 7.1+ KB\n"
     ]
    }
   ],
   "source": [
    "iris.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris.drop('Id',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.00000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.00600</td>\n",
       "      <td>3.418000</td>\n",
       "      <td>1.464000</td>\n",
       "      <td>0.24400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.35249</td>\n",
       "      <td>0.381024</td>\n",
       "      <td>0.173511</td>\n",
       "      <td>0.10721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.30000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.80000</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.00000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.20000</td>\n",
       "      <td>3.675000</td>\n",
       "      <td>1.575000</td>\n",
       "      <td>0.30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.80000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.60000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count       50.00000     50.000000      50.000000      50.00000\n",
       "mean         5.00600      3.418000       1.464000       0.24400\n",
       "std          0.35249      0.381024       0.173511       0.10721\n",
       "min          4.30000      2.300000       1.000000       0.10000\n",
       "25%          4.80000      3.125000       1.400000       0.20000\n",
       "50%          5.00000      3.400000       1.500000       0.20000\n",
       "75%          5.20000      3.675000       1.575000       0.30000\n",
       "max          5.80000      4.400000       1.900000       0.60000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris[iris['Species'] == 'Iris-setosa'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.936000</td>\n",
       "      <td>2.770000</td>\n",
       "      <td>4.260000</td>\n",
       "      <td>1.326000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.516171</td>\n",
       "      <td>0.313798</td>\n",
       "      <td>0.469911</td>\n",
       "      <td>0.197753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.900000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.600000</td>\n",
       "      <td>2.525000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.900000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.300000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count      50.000000     50.000000      50.000000     50.000000\n",
       "mean        5.936000      2.770000       4.260000      1.326000\n",
       "std         0.516171      0.313798       0.469911      0.197753\n",
       "min         4.900000      2.000000       3.000000      1.000000\n",
       "25%         5.600000      2.525000       4.000000      1.200000\n",
       "50%         5.900000      2.800000       4.350000      1.300000\n",
       "75%         6.300000      3.000000       4.600000      1.500000\n",
       "max         7.000000      3.400000       5.100000      1.800000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris[iris['Species'] == 'Iris-versicolor'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.00000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.58800</td>\n",
       "      <td>2.974000</td>\n",
       "      <td>5.552000</td>\n",
       "      <td>2.02600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.63588</td>\n",
       "      <td>0.322497</td>\n",
       "      <td>0.551895</td>\n",
       "      <td>0.27465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.90000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.22500</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.50000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.550000</td>\n",
       "      <td>2.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.90000</td>\n",
       "      <td>3.175000</td>\n",
       "      <td>5.875000</td>\n",
       "      <td>2.30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.90000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.50000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count       50.00000     50.000000      50.000000      50.00000\n",
       "mean         6.58800      2.974000       5.552000       2.02600\n",
       "std          0.63588      0.322497       0.551895       0.27465\n",
       "min          4.90000      2.200000       4.500000       1.40000\n",
       "25%          6.22500      2.800000       5.100000       1.80000\n",
       "50%          6.50000      3.000000       5.550000       2.00000\n",
       "75%          6.90000      3.175000       5.875000       2.30000\n",
       "max          7.90000      3.800000       6.900000       2.50000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris[iris['Species']=='Iris-virginica'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Sepal-Length-Width Vs Species')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pet_len_wid = iris[iris.Species == 'Iris-setosa'].plot(kind = 'scatter', x = 'SepalLengthCm', y = 'SepalWidthCm'\n",
    ",color = 'blue', label = 'setosa')\n",
    "iris[iris.Species == 'Iris-versicolor'].plot(kind = 'scatter', x = 'SepalLengthCm', y = 'SepalWidthCm', color = 'red'\n",
    ",label = 'versicolor', ax = pet_len_wid)\n",
    "iris[iris.Species == 'Iris-virginica'].plot(kind = 'scatter', x = 'SepalLengthCm', y = 'SepalWidthCm', color = 'green'\n",
    ",label = 'virginica', ax = pet_len_wid)\n",
    "pet_len_wid.set_xlabel('Sepal Length')\n",
    "pet_len_wid.set_ylabel('Sepal Width')\n",
    "pet_len_wid.set_title('Sepal-Length-Width Vs Species')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Petal-Length-Width Vs Species')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pet_len_wid = iris[iris.Species == 'Iris-setosa'].plot(kind = 'scatter', x = 'PetalLengthCm', y = 'PetalWidthCm'\n",
    ",color = 'blue', label = 'setosa')\n",
    "iris[iris.Species == 'Iris-versicolor'].plot(kind = 'scatter', x = 'PetalLengthCm', y = 'PetalWidthCm', color = 'red'\n",
    ",label = 'versicolor', ax = pet_len_wid)\n",
    "iris[iris.Species == 'Iris-virginica'].plot(kind = 'scatter', x = 'PetalLengthCm', y = 'PetalWidthCm', color = 'green'\n",
    ",label = 'virginica', ax = pet_len_wid)\n",
    "pet_len_wid.set_xlabel('Petal Length')\n",
    "pet_len_wid.set_ylabel('Petal Width')\n",
    "pet_len_wid.set_title('Petal-Length-Width Vs Species')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_size = 0.30\n",
    "#intilization state of a pseudo random number generator is seed\n",
    "seed = 7\n",
    "score = 'accuracy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def models(X_train, Y_train,score):\n",
    "    clfs = []\n",
    "    result = []\n",
    "    names = []\n",
    "    \n",
    "    clfs.append(('KNN', KNeighborsClassifier()))\n",
    "    clfs.append(('CART', DecisionTreeClassifier()))\n",
    "    \n",
    "    for algo_name, clf in clfs:\n",
    "        k_fold = model_selection.KFold(n_splits=10, random_state=seed)\n",
    "        cv_score = model_selection.cross_val_score(clf, X_train, Y_train, cv=k_fold, scoring=score)\n",
    "        #result = \"%s: %f (%f)\" % (algo_name, cv_score.mean(), cv_score.std())\n",
    "        result.append((algo_name,cv_score.mean(), cv_score.std()))\n",
    "        names.append(algo_name)\n",
    "    return (result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_all = iris.iloc[:,:4]\n",
    "Y_all = iris.iloc[:,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_all, X_test_all, Y_train_all, Y_test_all = model_selection.train_test_split(X_all, Y_all, test_size=test_size, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('KNN', 0.9800000000000001, 0.039999999999999994),\n",
       " ('CART', 0.96, 0.06633249580710798)]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models(X_train_all, Y_train_all, score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sep = iris[['SepalLengthCm','SepalWidthCm']]\n",
    "Y_sep = iris.Species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_sep, X_test_sep, Y_train_sep, Y_test_sep = model_selection.train_test_split(X_sep, Y_sep, test_size=test_size, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('KNN', 0.82, 0.08496961051307579),\n",
       " ('CART', 0.760909090909091, 0.06543560331973862)]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models(X_train_sep, Y_train_sep, score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7111111111111111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# Evaluation of the Classifier \n",
    "# Predictions on test dataset\n",
    "svm = SVC()\n",
    "svm.fit(X_train_sep, Y_train_sep)\n",
    "pred = svm.predict(X_test_sep)\n",
    "print(accuracy_score(Y_test_sep, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_p = iris[['PetalLengthCm','PetalWidthCm']]\n",
    "Y_p = iris.Species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_p, X_test_p, Y_train_p, Y_test_p = model_selection.train_test_split(X_p, Y_p, test_size=test_size, random_state=seed)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('KNN', 0.9800000000000001, 0.039999999999999994),\n",
       " ('CART', 0.9700000000000001, 0.04582575694955839)]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models(X_train_p, Y_train_p, score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9333333333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "svm = SVC()\n",
    "svm.fit(X_train_p, Y_train_p)\n",
    "#prediction\n",
    "pred = svm.predict(X_test_p)\n",
    "print(accuracy_score(Y_test_p, pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Take all relevant Features \n",
    "X_rel = iris[['PetalWidthCm', 'SepalLengthCm','SepalWidthCm' ]]\n",
    "Y_rel = iris.Species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_rel, X_test_rel, Y_train_rel, Y_test_rel = model_selection.train_test_split(X_rel, Y_rel, test_size=test_size, random_state=seed)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('KNN', 0.9609090909090909, 0.06556178084401014),\n",
       " ('CART', 0.9518181818181818, 0.04828462445414364)]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models(X_train_rel, Y_train_rel, score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9111111111111111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "## Evaluation of the Classifier \n",
    "# Predictions on test dataset\n",
    "svm = SVC()\n",
    "svm.fit(X_train_rel, Y_train_rel)\n",
    "#prediction\n",
    "pred = svm.predict(X_test_rel)\n",
    "print(accuracy_score(Y_test_rel, pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Take all relevant Features from correlation Matrix\n",
    "X_rel1 = iris[['PetalLengthCm', 'SepalLengthCm','SepalWidthCm' ]]\n",
    "Y_rel1 = iris.Species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_rel1, X_test_rel1, Y_train_rel1, Y_test_rel1 = model_selection.train_test_split(X_rel1, Y_rel1, test_size=test_size, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('KNN', 0.96, 0.04898979485566354),\n",
       " ('CART', 0.9700000000000001, 0.04582575694955839)]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models(X_train_rel1, Y_train_rel1, score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9111111111111111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# Evaluation of the Classifier \n",
    "# Predictions on test dataset\n",
    "svm = SVC()\n",
    "svm.fit(X_train_rel1, Y_train_rel1)\n",
    "pred = svm.predict(X_test_rel1)\n",
    "print(accuracy_score(Y_test_rel1, pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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