SupportVectorMachines.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "675fb5e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6a3c94ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0084e2f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris=load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "67c621de",
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e5885221",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b4a10cef",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(iris.data,columns=iris.feature_names)\n",
    "df['Target']=iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9a465924",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Flower_name']=df.Target.apply(lambda x: iris.target_names[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0da0b43a",
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a7075176",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bcd92d11",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6d85cc87",
   "metadata": {},
   "outputs": [],
   "source": [
    "df0=df[df.Target==0]\n",
    "df1=df[df.Target==1]\n",
    "df2=df[df.Target==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0b7c555a",
   "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": [
    "df0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "59fea3ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x21152159180>"
      ]
     },
     "execution_count": 26,
     "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.scatter(df0['sepal length (cm)'],df0['sepal width (cm)'],color='green')\n",
    "plt.scatter(df1['sepal length (cm)'],df1['sepal width (cm)'],color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d169decb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2115297a980>"
      ]
     },
     "execution_count": 27,
     "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.scatter(df0['petal length (cm)'],df0['petal width (cm)'],color='green')\n",
    "plt.scatter(df1['petal length (cm)'],df1['petal width (cm)'],color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f51da3d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4860dd50",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop(['Target','Flower_name'],axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4f80005a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['Target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1b0209b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "27f22788",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "model=SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b24b99db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "bcc5baa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9833333333333333"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f205816",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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