iris.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [4.9, 3.6, 1.4, 0.1],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>class</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>0</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>0</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>0</td>\n",
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       "    <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>0</td>\n",
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       "    <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>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                  5.1               3.5                1.4               0.2   \n",
       "1                  4.9               3.0                1.4               0.2   \n",
       "2                  4.7               3.2                1.3               0.2   \n",
       "3                  4.6               3.1                1.5               0.2   \n",
       "4                  5.0               3.6                1.4               0.2   \n",
       "..                 ...               ...                ...               ...   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     class  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  \n",
       "..     ...  \n",
       "145      2  \n",
       "146      2  \n",
       "147      2  \n",
       "148      2  \n",
       "149      2  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class']= iris.target\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = iris.data[:,[2,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7df623b2a390>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(x[:,0],x[:,1],c=y)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}