SkLearn_08 - SVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "51e75eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "95393a05",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ab5e15cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR',\n",
       " 'data',\n",
       " 'data_module',\n",
       " 'feature_names',\n",
       " 'filename',\n",
       " 'frame',\n",
       " 'target',\n",
       " 'target_names']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access the iris dataset\n",
    "dir(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "582d691c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "48565bea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1d8b0d43",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
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       "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",
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       "       [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",
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       "       [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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fdf2e56d",
   "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",
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       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
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     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# target means numerical rep of each flower\n",
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "45fb0db0",
   "metadata": {},
   "outputs": [
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       "  <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",
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       "  <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",
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       "    <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",
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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",
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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",
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      "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataset frame\n",
    "iris_df = pd.DataFrame(iris.data,columns=iris.feature_names)\n",
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0160ae7d",
   "metadata": {},
   "outputs": [
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       "      <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>target</th>\n",
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       "  <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",
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       "    <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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       "      <td>1.5</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1.4</td>\n",
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       "      <td>0</td>\n",
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       "   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",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a target dataframe\n",
    "iris_df[\"target\"] = iris.target\n",
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "35fff91f",
   "metadata": {},
   "outputs": [
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       "    <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>target</th>\n",
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       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "50                7.0               3.2                4.7               1.4   \n",
       "51                6.4               3.2                4.5               1.5   \n",
       "52                6.9               3.1                4.9               1.5   \n",
       "53                5.5               2.3                4.0               1.3   \n",
       "54                6.5               2.8                4.6               1.5   \n",
       "\n",
       "    target  \n",
       "50       1  \n",
       "51       1  \n",
       "52       1  \n",
       "53       1  \n",
       "54       1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df[iris_df.target==1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5531f8e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set a new column\n",
    "\n",
    "iris_df[\"flower_name\"]=iris_df.target.apply(lambda x:iris.target_names[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8ed5dbbb",
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "      <th>flower_name</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",
       "      <td>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>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>setosa</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   \n",
       "\n",
       "   target flower_name  \n",
       "0       0      setosa  \n",
       "1       0      setosa  \n",
       "2       0      setosa  \n",
       "3       0      setosa  \n",
       "4       0      setosa  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a40b4b7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Searching for all the flowers\n",
    "\n",
    "iris_setosa = iris_df[iris_df.target==0]   # setosa flower start from 0\n",
    "iris_versi = iris_df[iris_df.target==1]   # versicolor start frpm 50\n",
    "iris_virgin = iris_df[iris_df.target==2]   # virginica start frpm 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1c296621",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we will graphically compare sepal length and width\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1ad09aa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d5befa3b50>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"Length of Sepal\")\n",
    "plt.ylabel(\"Width of Sepal\")\n",
    "# setosa compare\n",
    "plt.scatter(iris_setosa[\"sepal length (cm)\"],iris_setosa[\"sepal width (cm)\"],color=\"red\",marker=\"*\")\n",
    "\n",
    "# versicolor compare\n",
    "\n",
    "plt.scatter(iris_versi[\"sepal length (cm)\"],iris_versi[\"sepal width (cm)\"],color=\"blue\",marker=\"+\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9299f00a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d5beb45360>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"Length of Sepal\")\n",
    "plt.ylabel(\"Width of Sepal\")\n",
    "# setosa compare\n",
    "plt.scatter(iris_setosa[\"sepal length (cm)\"],iris_setosa[\"sepal width (cm)\"],color=\"red\",marker=\"*\")\n",
    "\n",
    "# virginia compare\n",
    "\n",
    "plt.scatter(iris_virgin[\"sepal length (cm)\"],iris_versi[\"sepal width (cm)\"],color=\"blue\",marker=\"+\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6f9e1c20",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train_Test\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a3fc86bb",
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "      <th>flower_name</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",
       "      <td>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>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>virginica</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",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>virginica</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",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 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",
       "     target flower_name  \n",
       "0         0      setosa  \n",
       "1         0      setosa  \n",
       "2         0      setosa  \n",
       "3         0      setosa  \n",
       "4         0      setosa  \n",
       "..      ...         ...  \n",
       "145       2   virginica  \n",
       "146       2   virginica  \n",
       "147       2   virginica  \n",
       "148       2   virginica  \n",
       "149       2   virginica  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "123484f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create train data - only values\n",
    "iris_X = iris_df.drop([\"target\",\"flower_name\"],axis=\"columns\")\n",
    "iris_Y = iris_df.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "66734663",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test = train_test_split(iris_X,iris_Y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e254c034",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create SVC -- SVM\n",
    "from sklearn.svm import SVC\n",
    "# X - petal and sepal value\n",
    "# Y - target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0fc66b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_model = SVC()"
   ]
  },
  {
   "cell_type": "code",
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       "SVC()"
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   "execution_count": 51,
   "id": "f397f394",
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     "text": [
      "C:\\Users\\RAJARSHI RAY\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
      "  warnings.warn(\n"
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       "array([2])"
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     "execution_count": 51,
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   "execution_count": null,
   "id": "8811f7d2",
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   "outputs": [],
   "source": []
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