SVM & PCA .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = sns.load_dataset('iris')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</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>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>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>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>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>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1cbc980df70>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 804.75x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(iris,hue='species',palette='Dark2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "setosa=iris[iris['species']=='setosa']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sepal_width', ylabel='sepal_length'>"
      ]
     },
     "execution_count": 6,
     "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": [
    "sns.kdeplot(x='sepal_width',y='sepal_length',data=setosa,cmap='plasma',shade=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=iris.drop('species',axis=1)\n",
    "y=iris['species']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm= SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=svm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      setosa       1.00      1.00      1.00        13\n",
      "  versicolor       1.00      0.95      0.97        20\n",
      "   virginica       0.92      1.00      0.96        12\n",
      "\n",
      "    accuracy                           0.98        45\n",
      "   macro avg       0.97      0.98      0.98        45\n",
      "weighted avg       0.98      0.98      0.98        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmat=confusion_matrix(y_test,prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAAD4CAYAAADbyJysAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAW1UlEQVR4nO3df5RcZZ3n8fenQ2fA8FMZIelkDJoMA4IkMyGKWR2Q4UcygcTVDckAKrLbgwoLnjmojIyuZwaH3T2ywsSB6ZEAuhBkRQQhKlkPY2AETWADkx/8CJCVTjdkFYFAMpN01Xf/qEtSaau7qrur+9568nnlPKfr/qh7v1zgm28/93nuVURgZmb5a8s7ADMzq3BCNjMrCCdkM7OCcEI2MysIJ2Qzs4LYb7RPsO3Tcz2MY5Qd9s0n8g7BrCn6dm7RSI+x61fPNZxz2g9/54jP10yukM3MCmLUK2QzszFVLuUdwbA5IZtZWkp9eUcwbE7IZpaUiHLeIQybE7KZpaXshGxmVgyukM3MCsI39czMCsIVsplZMYRHWZiZFYRv6pmZFYS7LMzMCsI39czMCsIVsplZQTTxpp6kZcB8YGtEHJet+w5wdLbLocArETGjxnc3A9uAEtAXEbPqnc8J2czS0tybejcDS4FvvbkiIs5587OkrwGvDvL9UyLiV42ezAnZzJIS0bw+5IhYJWlqrW2SBCwCPtSs8/l5yGaWlig33kbmA8BLEfHMQJEA90t6VFJnIwd0hWxmaRlCl0WWKKuTZVdEdDX49SXA8kG2z4mIHklvB1ZKejIiVg12QCdkM0vLECrfLPk2moB3k7Qf8O+BPxrk2D3Zz62S7gJmA4MmZHdZmFlaSrsab8P3J8CTEdFda6OkCZIOevMzcDqwrt5BnZDNLC3lcuOtDknLgYeBoyV1S7ow27SYft0VkiZJWpEtHgE8JOlx4BfAfRHxo3rnc5eFmaWliRNDImLJAOs/UWNdDzAv+/wccMJQz+eEbGZp8cOFzMwKwgnZzKwYYmQ363LlhGxmafHDhczMCsJdFmZmBeEK2cysIFwhm5kVhCtkM7OC6Gvdt07v81On9z/vs0z4r8t5y5XX7143fv75vOWLf89brljKAZdchQ55a44RpueM009m/bpVPLnhIT53+WfyDidJ+/Q1HrvHbzbdPp+Qdz2ykh1Lr9xr3c7/fSfbr/o02//2Yvr+5eeMn/dnOUWXnra2Nq679irmn3Uex59wCuecs5Bjjpmed1hJ2eevcROfZTHW9vmEXNq0jnhj294r/3X77o/6nf0rj5m2pph94kyefXYzzz//S3bt2sUdd9zN2WedkXdYSdnnr3ELV8h1+5Al/QGwAOigkpp6gHsiYuMox5ar8Wd/nPb3nkrseIMdX/9C3uEkY1LHkbzQ3bN7uXtLL7NPnJljROnZ569xASvfRg1aIUv6PHA7ICqPkFudfV4uKekstfOeW3jjix+jb/UDtP/xWXmHk4zKa8j2FuFfQZppn7/GLVwh1+uyuBA4MSKujoj/mbWrqTz5/sKBviSpU9IaSWtu2vBCM+Mdc7tW/xP7zZyTdxjJ2NLdy5TJk3YvT+6YSG/vSzlGlJ59/hr39TXeCqZeQi4Dk2qsn5htqykiuiJiVkTMuuDYKSOJLxf63T3/yPu9532UX6z5UgAbhtVr1jJt2lFMnTqF9vZ2Fi1awA/uvT/vsJKyz1/jiMZbwdTrQ74M+ImkZ4A3S93fA6YBF49iXGNm/ws+z7jffw868GAmXPVtdt73bca9+0TajpgMEcTLW/nX2/4u7zCTUSqVuPSyK1lx322Ma2vj5lu+w4YNT+cdVlL2+Wvcwn3Iqte3JKmNShdFB5X+425gdUSUGjnBtk/PLd5fQ4k57JtP5B2CWVP07dzy2x3gQ7Tj1r9qOOcccO5fj/h8zVR3lEVElIFHxiAWM7ORK+DNukZ56rSZpaXU0C/vhbTPTwwxs8Q0963TyyRtlbSuat1/kbRF0tqszRvgu2dKekrSpkaHCTshm1lamjt1+mbgzBrr/0dEzMjaiv4bJY0DvgHMBY4Flkg6tt7JnJDNLC1NnBgSEauAl4cRxWxgU0Q8FxE7qUywW1DvS07IZpaUKEfDrXoSW9Y6GzzNxZKeyLo0DquxvYM9Q4WhMjqto95BnZDNLC1D6LKonsSWta4GznA98C5gBtALfK3GPrWG09UdjudRFmaWllEeZRERu+ehS/pH4N4au3UD1dOUJ1N5MNugXCGbWVpG+XnIkiZWLX4YWFdjt9XAdElHSRoPLAbuqXdsV8hmlpYmTp2WtBw4GThcUjfwZeBkSTOodEFsBv4823cS8M2ImBcRfZIuBn4MjAOWRcT6eudzQjaztDTxoUERsaTG6hsH2LcHmFe1vAL4rSFxg3FCNrO0tPDDhZyQzSwt5dZ9npkTspmlpYWfZeGEbGZJCXdZmJkVhLsszMwKws9DNjMrCFfIZmYF0eebemZmxeAuCzOzgnCXhZlZMXjYm5lZUbhCNjMrCCdkM7OC8NRpM7NiCFfIZmYF4YRsZlYQHmVhZlYQrpDNzArCCdnMrBii5C6LAR32zSdG+xT7vB09D+YdQvImvWtu3iFYo5pYIUtaBswHtkbEcdm6/w6cBewEngUuiIhXanx3M7ANKAF9ETGr3vnamha5mVkBRDkabg24GTiz37qVwHER8R7gaeCKQb5/SkTMaCQZgxOymaWmHI23OiJiFfByv3X3R0RftvgIMLlZoTshm1layo03SZ2S1lS1ziGe7ZPADwfYFsD9kh5t9Li+qWdmSYm+xm/qRUQX0DWc80j6ItAH3DrALnMiokfS24GVkp7MKu4BuUI2s7QMoUIeLkkfp3Kz79yIqNn3ERE92c+twF3A7HrHdUI2s6Q0+abeb5F0JvB54OyI2D7APhMkHfTmZ+B0YF29Yzshm1lamlghS1oOPAwcLalb0oXAUuAgKt0QayXdkO07SdKK7KtHAA9Jehz4BXBfRPyo3vnch2xmSWnm094iYkmN1TcOsG8PMC/7/BxwwlDP54RsZmlp3Yl6TshmlpbdI4RbkBOymSUlXCGbmRWEE7KZWTG4QjYzKwgnZDOzgoiS8g5h2JyQzSwprpDNzAoiyq6QzcwKwRWymVlBRLhCNjMrBFfIZmYFUfYoCzOzYvBNPTOzgnBCNjMriNovVGoNTshmlhRXyGZmBeFhb2ZmBVFq4VEWfsmpmSUlQg23eiQtk7RV0rqqdW+VtFLSM9nPwwb47pmSnpK0SdIXGondCdnMkhJlNdwacDNwZr91XwB+EhHTgZ9ky3uRNA74BjAXOBZYIunYeidzQjazpEQ03uofK1YBL/dbvQC4Jft8C7CwxldnA5si4rmI2Ancnn1vUE7IZpaUoVTIkjolralqnQ2c4oiI6AXIfr69xj4dwAtVy93ZukH5pp6ZJaVUbrzOjIguoGsUwqjVH1K3JneFXOWM009m/bpVPLnhIT53+WfyDicZV371Gj74p4tZeN5Fu9c9+cxznNv5WT58/qf4zOe+zOtvvJFjhGm5dulX2bDpZ6x6+Ad5h5KLZnZZDOAlSRMBsp9ba+zTDUypWp4M9NQ7sBNypq2tjeuuvYr5Z53H8SecwjnnLOSYY6bnHVYSFs47jRuu+Zu91n356q9z2acu4K5vX8+pH3w/N916Z07Rpef2277H4o/8x7zDyE051HAbpnuAj2efPw7cXWOf1cB0SUdJGg8szr43KCfkzOwTZ/Lss5t5/vlfsmvXLu64427OPuuMvMNKwqwZx3PIwQfttW7zL7uZNeN4AE468Q9Z+dOH8ggtSQ//bA2/+c2reYeRmyYPe1sOPAwcLalb0oXA1cBpkp4BTsuWkTRJ0opKDNEHXAz8GNgI3BER6+udb9h9yJIuiIibhvv9opnUcSQvdO/5jaJ7Sy+zT5yZY0Rpm/bOqTzw0CN86AMncf8DD/LiS7/KOyRLRDOfZRERSwbYdGqNfXuAeVXLK4AVQznfSCrkrwy0ofrOZbncGn2D0m//bRmt/JSSgvvrv/wsy+/8AYs+eQlvbN9Be7vvL1tzjEGXxagZ9P8CSU8MtAk4YqDvVd+53G98R0tktS3dvUyZPGn38uSOifT2vpRjRGl75zum8I9f/ypQ6b5Y9bNf5ByRpWIooyyKpl5ZcgRwBvCbfusF/GxUIsrJ6jVrmTbtKKZOncKWLS+yaNECzv+YR1qMll//5hXedtihlMtl/uGW21m0cF79L5k1oCUqwAHUS8j3AgdGxNr+GyT902gElJdSqcSll13JivtuY1xbGzff8h02bHg677CScPmXr2b1/3mCV155jVMXnsenLzyf7Tt2cPv37gXgT/74/Xz4T0/POcp0/MONX2POv5vNW992GI9v+Cn/7W//jlu//d28wxozReyKaJRGu5+0VbosWtmOngfzDiF5k941N+8Q9gn/79WnRpxN//nIjzacc+a8+N1CZW/fSTGzpLTwS6edkM0sLVFz1nJrcEI2s6T0tXAfshOymSXFFbKZWUG4D9nMrCBcIZuZFYQrZDOzgii5QjYzK4bG3l1aTE7IZpaUsitkM7NiaOVnNTghm1lSfFPPzKwgyjVeNtEqnJDNLCmlvAMYASdkM0tKK4+yaN13nZiZ1VBGDbfBSDpa0tqq9pqky/rtc7KkV6v2+dJIYneFbGZJadYoi4h4CpgBIGkcsAW4q8auD0bE/Gac0wnZzJIySl0WpwLPRsT/HZWjZ9xlYWZJKQ+hSeqUtKaqdQ5w2MXA8gG2nSTpcUk/lPTukcTuCtnMklIaQoUcEV1A12D7SBoPnA1cUWPzY8A7IuJ1SfOA7wPTG49gb66QzSwpQ6mQGzQXeCwiXuq/ISJei4jXs88rgHZJhw83didkM0vKKCTkJQzQXSHpSKkyE0XSbCo59dfDjd1dFmaWlGa+Uk/SW4DTgD+vWncRQETcAHwU+JSkPmAHsDgihj3QwwnZzJLSzGdZRMR24G391t1Q9XkpsLRZ53NCNrOkeOq0mVlBtPLUaSdkM0uKH79pZlYQTshmZgXhN4aYmRWE+5DNzArCoywsVwdM+kDeISRv69xpeYdgDSq3cKeFE7KZJcU39czMCqJ162MnZDNLjCtkM7OC6FPr1shOyGaWlNZNx07IZpYYd1mYmRWEh72ZmRVE66ZjJ2QzS4y7LMzMCqLUwjWyE7KZJcUVsplZQUQTK2RJm4FtVJ5Z1BcRs/ptF3AtMA/YDnwiIh4b7vmckM0sKaNQIZ8SEb8aYNtcYHrW3gtcn/0cFidkM0vKGA97WwB8KyICeETSoZImRkTvcA7W1tzYzMzyFUNoDR7ufkmPSuqssb0DeKFquTtbNyyukM0sKX1DqJCzJFudaLsioqtqeU5E9Eh6O7BS0pMRsar6EDUOO+wS3QnZzJIylJt6WfLtGmR7T/Zzq6S7gNlAdULuBqZULU8GeoYSbzV3WZhZUspDaIORNEHSQW9+Bk4H1vXb7R7gY6p4H/DqcPuPwRWymSWmicPejgDuqoxsYz/gtoj4kaSLACLiBmAFlSFvm6gMe7tgJCd0QjazpDRr2FtEPAecUGP9DVWfA/hMk07phGxmaSmFp06bmRWCH79pZlYQzZw6PdackM0sKX64kJlZQbjLwsysINxlYWZWEB5lYWZWEO6yMDMrCN/UMzMrCPchm5kVRCt3Wfhpb1XOOP1k1q9bxZMbHuJzlzdterr14+vcfBMu+TyH3vJ9Dr7upt3rDvjERRzyjW9x8LXLOPCKv0ETDswxwrETEQ23onFCzrS1tXHdtVcx/6zzOP6EUzjnnIUcc8z0vMNKjq/z6Pi3n/yQbV+5fK91u9au4dVLLuC1Sz9JacsL7P+Rc3OKbmyViIZb0TghZ2afOJNnn93M88//kl27dnHHHXdz9lln5B1WcnydR0ffhieI17ftvW7tGiiXKp+f3kDb4b+bR2hjrkw03IqmbkKW9AeSTpV0YL/1Z45eWGNvUseRvNC950H/3Vt6mTTpyBwjSpOvcz5+59R57Hr053mHMSaS7bKQ9J+Bu4FLgHWSFlRt/upoBjbWsodQ76WI/8Jana/z2Nv/P5wH5RI7f7oy71DGRCtXyPVGWfwn4I8i4nVJU4HvSpoaEddS++V+wN4vDtS4Q2hrm9CseEfNlu5epkyetHt5csdEentfyjGiNPk6j63xp5zB+Fnv57W/+mzeoYyZVh72Vq/LYlxEvA4QEZuBk4G5kq5hkIQcEV0RMSsiZrVCMgZYvWYt06YdxdSpU2hvb2fRogX84N778w4rOb7OY6d95mwO+Mifse2qK2Dnv+UdzpgpRTTciqZehfyipBkRsRYgq5TnA8uA40c7uLFUKpW49LIrWXHfbYxra+PmW77Dhg1P5x1WcnydR8eEv/gS7cfNQAcfwqE3/i+2L7+JAz56LrSP56CvfA2o3Njbfv01OUc6+orYFdEoDdZ/J2ky0BcRL9bYNici/rneCfYb39G6V8css3XutLxD2Ce89e6fDvibd6NO6jil4Zzz8JYHRny+Zhq0yyIiumsl42xb3WRsZjbWmjXKQtIUSQ9I2ihpvaRLa+xzsqRXJa3N2pdGErunTptZUprYZdEH/EVEPCbpIOBRSSsjYkO//R6MiPnNOKETspklpVmjLCKiF+jNPm+TtBHoAPon5KbxTD0zS0opyg03SZ2S1lS1zlrHzIb9zgRqza45SdLjkn4o6d0jid0VspklZSgTjSKiC+gabJ9slvKdwGUR8Vq/zY8B78hGoM0Dvg8M++EsrpDNLCnNnKknqZ1KMr41Ir7Xf3tEvFY1V2MF0C7p8OHG7oRsZkmJIfwZjCrz/G8ENkZEzQHcko7M9kPSbCo59dfDjd1dFmaWlHLzZuDNAc4H/kXS2mzdXwK/BxARNwAfBT4lqQ/YASyOETycxQnZzJLSxFEWDzHIIyKyfZYCS5tyQpyQzSwxpWjd15w6IZtZUprYZTHmnJDNLCmt/PhNJ2QzS4orZDOzgnCFbGZWEKUo5R3CsDkhm1lSWvkdjU7IZpaUVn5jiBOymSXFFbKZWUF4lIWZWUF4lIWZWUF46rSZWUG4D9nMrCDch2xmVhCukM3MCsLjkM3MCsIVsplZQXiUhZlZQfimnplZQbRyl0Vb3gGYmTVTDOFPPZLOlPSUpE2SvlBjuyRdl21/QtIfjiR2J2QzS0pENNwGI2kc8A1gLnAssETSsf12mwtMz1oncP1IYndCNrOklCMabnXMBjZFxHMRsRO4HVjQb58FwLei4hHgUEkThxv7qPch9+3cotE+R7NJ6oyIrrzjSJmv8ejbV6/xUHKOpE4qle2buqquWQfwQtW2buC9/Q5Ra58OoLfhgKu4Qq6ts/4uNkK+xqPP17iOiOiKiFlVrfovsFqJvX9Z3cg+DXNCNjOrrRuYUrU8GegZxj4Nc0I2M6ttNTBd0lGSxgOLgXv67XMP8LFstMX7gFcjYljdFeBxyAPZ5/rdcuBrPPp8jUcgIvokXQz8GBgHLIuI9ZIuyrbfAKwA5gGbgO3ABSM5p1p5ELWZWUrcZWFmVhBOyGZmBeGEXKXeNEkbOUnLJG2VtC7vWFIlaYqkByRtlLRe0qV5x2SNcR9yJpsm+TRwGpWhLKuBJRGxIdfAEiPpg8DrVGY3HZd3PCnKZopNjIjHJB0EPAos9H/LxecKeY9GpknaCEXEKuDlvONIWUT0RsRj2edtwEYqs8es4JyQ9xhoCqRZy5I0FZgJ/DznUKwBTsh7NHUKpFneJB0I3AlcFhGv5R2P1eeEvEdTp0Ca5UlSO5VkfGtEfC/veKwxTsh7NDJN0qzwJAm4EdgYEdfkHY81zgk5ExF9wJvTJDcCd0TE+nyjSo+k5cDDwNGSuiVdmHdMCZoDnA98SNLarM3LOyirz8PezMwKwhWymVlBOCGbmRWEE7KZWUE4IZuZFYQTsplZQTghm5kVhBOymVlB/H8D2qgH6JluDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cmat,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid ={'C':[0.1,1,10,100,1000],'gamma':[1,0.1,0.01,0.001]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid=GridSearchCV(svm,param_grid,verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ...................... C=0.1, gamma=1, score=0.905, total=   0.0s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ...................... C=0.1, gamma=1, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ...................... C=0.1, gamma=1, score=0.905, total=   0.0s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ...................... C=0.1, gamma=1, score=0.905, total=   0.0s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ...................... C=0.1, gamma=1, score=0.952, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] .................... C=0.1, gamma=0.1, score=0.857, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] .................... C=0.1, gamma=0.1, score=0.714, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] .................... C=0.1, gamma=0.1, score=0.857, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] .................... C=0.1, gamma=0.1, score=0.810, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] .................... C=0.1, gamma=0.1, score=0.810, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ................... C=0.1, gamma=0.01, score=0.714, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ................... C=0.1, gamma=0.01, score=0.714, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ................... C=0.1, gamma=0.01, score=0.714, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ................... C=0.1, gamma=0.01, score=0.714, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ................... C=0.1, gamma=0.01, score=0.714, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] .................. C=0.1, gamma=0.001, score=0.333, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] .................. C=0.1, gamma=0.001, score=0.333, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] .................. C=0.1, gamma=0.001, score=0.714, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] .................. C=0.1, gamma=0.001, score=0.714, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] .................. C=0.1, gamma=0.001, score=0.714, total=   0.0s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ........................ C=1, gamma=1, score=1.000, total=   0.0s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ........................ C=1, gamma=1, score=0.905, total=   0.0s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ........................ C=1, gamma=1, score=0.905, total=   0.0s\n",
      "[CV] C=1, gamma=1 ....................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ........................ C=1, gamma=1, score=0.952, total=   0.1s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
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      "[CV] C=1, gamma=0.1 ..................................................\n",
      "[CV] ...................... C=1, gamma=0.1, score=1.000, total=   0.0s\n",
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      "[CV] ...................... C=1, gamma=0.1, score=1.000, total=   0.0s\n",
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      "[CV] C=1, gamma=0.1 ..................................................\n",
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      "[CV] C=1, gamma=0.1 ..................................................\n",
      "[CV] ...................... C=1, gamma=0.1, score=1.000, total=   0.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] ..................... C=1, gamma=0.01, score=0.952, total=   0.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] ..................... C=1, gamma=0.01, score=0.810, total=   0.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] ..................... C=1, gamma=0.01, score=0.857, total=   0.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] ..................... C=1, gamma=0.01, score=0.857, total=   0.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] ..................... C=1, gamma=0.01, score=0.905, total=   0.0s\n",
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      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] .................... C=10, gamma=0.01, score=1.000, total=   0.0s\n",
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      "[CV] .................... C=10, gamma=0.01, score=1.000, total=   0.0s\n",
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      "[CV] .................... C=10, gamma=0.01, score=0.905, total=   0.0s\n",
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      "[CV] .................... C=10, gamma=0.01, score=0.905, total=   0.0s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] .................... C=10, gamma=0.01, score=1.000, total=   0.0s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
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      "[CV] C=10, gamma=0.001 ...............................................\n",
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      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] ...................... C=100, gamma=1, score=1.000, total=   0.0s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
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      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] .................... C=100, gamma=0.1, score=1.000, total=   0.0s\n",
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      "[CV] .................... C=100, gamma=0.1, score=0.905, total=   0.0s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] .................... C=100, gamma=0.1, score=0.857, total=   0.0s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] .................... C=100, gamma=0.1, score=0.905, total=   0.0s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] .................... C=100, gamma=0.1, score=0.952, total=   0.0s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ................... C=100, gamma=0.01, score=1.000, total=   0.0s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ................... C=100, gamma=0.01, score=1.000, total=   0.0s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ................... C=100, gamma=0.01, score=0.905, total=   0.0s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ................... C=100, gamma=0.01, score=0.905, total=   0.0s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ................... C=100, gamma=0.01, score=0.952, total=   0.0s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] .................. C=100, gamma=0.001, score=1.000, total=   0.0s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] .................. C=100, gamma=0.001, score=1.000, total=   0.0s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] .................. C=100, gamma=0.001, score=0.905, total=   0.0s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] .................. C=100, gamma=0.001, score=0.905, total=   0.0s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] .................. C=100, gamma=0.001, score=1.000, total=   0.0s\n",
      "[CV] C=1000, gamma=1 .................................................\n",
      "[CV] ..................... C=1000, gamma=1, score=1.000, total=   0.0s\n",
      "[CV] C=1000, gamma=1 .................................................\n",
      "[CV] ..................... C=1000, gamma=1, score=0.905, total=   0.0s\n",
      "[CV] C=1000, gamma=1 .................................................\n",
      "[CV] ..................... C=1000, gamma=1, score=0.857, total=   0.0s\n",
      "[CV] C=1000, gamma=1 .................................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ..................... C=1000, gamma=1, score=0.905, total=   0.0s\n",
      "[CV] C=1000, gamma=1 .................................................\n",
      "[CV] ..................... C=1000, gamma=1, score=0.952, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1 ...............................................\n",
      "[CV] ................... C=1000, gamma=0.1, score=1.000, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1 ...............................................\n",
      "[CV] ................... C=1000, gamma=0.1, score=0.905, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1 ...............................................\n",
      "[CV] ................... C=1000, gamma=0.1, score=0.857, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1 ...............................................\n",
      "[CV] ................... C=1000, gamma=0.1, score=0.905, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1 ...............................................\n",
      "[CV] ................... C=1000, gamma=0.1, score=0.952, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01 ..............................................\n",
      "[CV] .................. C=1000, gamma=0.01, score=1.000, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01 ..............................................\n",
      "[CV] .................. C=1000, gamma=0.01, score=1.000, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01 ..............................................\n",
      "[CV] .................. C=1000, gamma=0.01, score=0.857, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01 ..............................................\n",
      "[CV] .................. C=1000, gamma=0.01, score=0.905, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01 ..............................................\n",
      "[CV] .................. C=1000, gamma=0.01, score=0.952, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001 .............................................\n",
      "[CV] ................. C=1000, gamma=0.001, score=1.000, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001 .............................................\n",
      "[CV] ................. C=1000, gamma=0.001, score=1.000, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001 .............................................\n",
      "[CV] ................. C=1000, gamma=0.001, score=0.905, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001 .............................................\n",
      "[CV] ................. C=1000, gamma=0.001, score=0.905, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001 .............................................\n",
      "[CV] ................. C=1000, gamma=0.001, score=0.952, total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=SVC(),\n",
       "             param_grid={'C': [0.1, 1, 10, 100, 1000],\n",
       "                         'gamma': [1, 0.1, 0.01, 0.001]},\n",
       "             verbose=3)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, gamma=0.1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1, 'gamma': 0.1}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_pred=grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      setosa       1.00      1.00      1.00        13\n",
      "  versicolor       1.00      0.95      0.97        20\n",
      "   virginica       0.92      1.00      0.96        12\n",
      "\n",
      "    accuracy                           0.98        45\n",
      "   macro avg       0.97      0.98      0.98        45\n",
      "weighted avg       0.98      0.98      0.98        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,grid_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[13  0  0]\n",
      " [ 0 19  1]\n",
      " [ 0  0 12]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,grid_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "setosa        50\n",
       "versicolor    50\n",
       "virginica     50\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['species'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal_length  150 non-null    float64\n",
      " 1   sepal_width   150 non-null    float64\n",
      " 2   petal_length  150 non-null    float64\n",
      " 3   petal_width   150 non-null    float64\n",
      " 4   species       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "iris.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Principal Component Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    " cancer = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils.Bunch"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(cancer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _breast_cancer_dataset:\n",
      "\n",
      "Breast cancer wisconsin (diagnostic) dataset\n",
      "--------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 569\n",
      "\n",
      "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
      "\n",
      "    :Attribute Information:\n",
      "        - radius (mean of distances from center to points on the perimeter)\n",
      "        - texture (standard deviation of gray-scale values)\n",
      "        - perimeter\n",
      "        - area\n",
      "        - smoothness (local variation in radius lengths)\n",
      "        - compactness (perimeter^2 / area - 1.0)\n",
      "        - concavity (severity of concave portions of the contour)\n",
      "        - concave points (number of concave portions of the contour)\n",
      "        - symmetry\n",
      "        - fractal dimension (\"coastline approximation\" - 1)\n",
      "\n",
      "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
      "        worst/largest values) of these features were computed for each image,\n",
      "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
      "        10 is Radius SE, field 20 is Worst Radius.\n",
      "\n",
      "        - class:\n",
      "                - WDBC-Malignant\n",
      "                - WDBC-Benign\n",
      "\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ===================================== ====== ======\n",
      "                                           Min    Max\n",
      "    ===================================== ====== ======\n",
      "    radius (mean):                        6.981  28.11\n",
      "    texture (mean):                       9.71   39.28\n",
      "    perimeter (mean):                     43.79  188.5\n",
      "    area (mean):                          143.5  2501.0\n",
      "    smoothness (mean):                    0.053  0.163\n",
      "    compactness (mean):                   0.019  0.345\n",
      "    concavity (mean):                     0.0    0.427\n",
      "    concave points (mean):                0.0    0.201\n",
      "    symmetry (mean):                      0.106  0.304\n",
      "    fractal dimension (mean):             0.05   0.097\n",
      "    radius (standard error):              0.112  2.873\n",
      "    texture (standard error):             0.36   4.885\n",
      "    perimeter (standard error):           0.757  21.98\n",
      "    area (standard error):                6.802  542.2\n",
      "    smoothness (standard error):          0.002  0.031\n",
      "    compactness (standard error):         0.002  0.135\n",
      "    concavity (standard error):           0.0    0.396\n",
      "    concave points (standard error):      0.0    0.053\n",
      "    symmetry (standard error):            0.008  0.079\n",
      "    fractal dimension (standard error):   0.001  0.03\n",
      "    radius (worst):                       7.93   36.04\n",
      "    texture (worst):                      12.02  49.54\n",
      "    perimeter (worst):                    50.41  251.2\n",
      "    area (worst):                         185.2  4254.0\n",
      "    smoothness (worst):                   0.071  0.223\n",
      "    compactness (worst):                  0.027  1.058\n",
      "    concavity (worst):                    0.0    1.252\n",
      "    concave points (worst):               0.0    0.291\n",
      "    symmetry (worst):                     0.156  0.664\n",
      "    fractal dimension (worst):            0.055  0.208\n",
      "    ===================================== ====== ======\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
      "\n",
      "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
      "\n",
      "    :Donor: Nick Street\n",
      "\n",
      "    :Date: November, 1995\n",
      "\n",
      "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
      "https://goo.gl/U2Uwz2\n",
      "\n",
      "Features are computed from a digitized image of a fine needle\n",
      "aspirate (FNA) of a breast mass.  They describe\n",
      "characteristics of the cell nuclei present in the image.\n",
      "\n",
      "Separating plane described above was obtained using\n",
      "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
      "Construction Via Linear Programming.\" Proceedings of the 4th\n",
      "Midwest Artificial Intelligence and Cognitive Science Society,\n",
      "pp. 97-101, 1992], a classification method which uses linear\n",
      "programming to construct a decision tree.  Relevant features\n",
      "were selected using an exhaustive search in the space of 1-4\n",
      "features and 1-3 separating planes.\n",
      "\n",
      "The actual linear program used to obtain the separating plane\n",
      "in the 3-dimensional space is that described in:\n",
      "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
      "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
      "Optimization Methods and Software 1, 1992, 23-34].\n",
      "\n",
      "This database is also available through the UW CS ftp server:\n",
      "\n",
      "ftp ftp.cs.wisc.edu\n",
      "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
      "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
      "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
      "     San Jose, CA, 1993.\n",
      "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
      "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
      "     July-August 1995.\n",
      "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
      "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
      "     163-171.\n"
     ]
    }
   ],
   "source": [
    "print(cancer['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(cancer['data'],columns=cancer['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.380</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.990</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.570</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.910</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.540</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>...</td>\n",
       "      <td>25.450</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>...</td>\n",
       "      <td>23.690</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>...</td>\n",
       "      <td>18.980</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>...</td>\n",
       "      <td>25.740</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>...</td>\n",
       "      <td>9.456</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0          17.99         10.38          122.80     1001.0          0.11840   \n",
       "1          20.57         17.77          132.90     1326.0          0.08474   \n",
       "2          19.69         21.25          130.00     1203.0          0.10960   \n",
       "3          11.42         20.38           77.58      386.1          0.14250   \n",
       "4          20.29         14.34          135.10     1297.0          0.10030   \n",
       "..           ...           ...             ...        ...              ...   \n",
       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
       "566        16.60         28.08          108.30      858.1          0.08455   \n",
       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
       "568         7.76         24.54           47.92      181.0          0.05263   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0             0.27760         0.30010              0.14710         0.2419   \n",
       "1             0.07864         0.08690              0.07017         0.1812   \n",
       "2             0.15990         0.19740              0.12790         0.2069   \n",
       "3             0.28390         0.24140              0.10520         0.2597   \n",
       "4             0.13280         0.19800              0.10430         0.1809   \n",
       "..                ...             ...                  ...            ...   \n",
       "564           0.11590         0.24390              0.13890         0.1726   \n",
       "565           0.10340         0.14400              0.09791         0.1752   \n",
       "566           0.10230         0.09251              0.05302         0.1590   \n",
       "567           0.27700         0.35140              0.15200         0.2397   \n",
       "568           0.04362         0.00000              0.00000         0.1587   \n",
       "\n",
       "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
       "0                   0.07871  ...        25.380          17.33   \n",
       "1                   0.05667  ...        24.990          23.41   \n",
       "2                   0.05999  ...        23.570          25.53   \n",
       "3                   0.09744  ...        14.910          26.50   \n",
       "4                   0.05883  ...        22.540          16.67   \n",
       "..                      ...  ...           ...            ...   \n",
       "564                 0.05623  ...        25.450          26.40   \n",
       "565                 0.05533  ...        23.690          38.25   \n",
       "566                 0.05648  ...        18.980          34.12   \n",
       "567                 0.07016  ...        25.740          39.42   \n",
       "568                 0.05884  ...         9.456          30.37   \n",
       "\n",
       "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
       "0             184.60      2019.0           0.16220            0.66560   \n",
       "1             158.80      1956.0           0.12380            0.18660   \n",
       "2             152.50      1709.0           0.14440            0.42450   \n",
       "3              98.87       567.7           0.20980            0.86630   \n",
       "4             152.20      1575.0           0.13740            0.20500   \n",
       "..               ...         ...               ...                ...   \n",
       "564           166.10      2027.0           0.14100            0.21130   \n",
       "565           155.00      1731.0           0.11660            0.19220   \n",
       "566           126.70      1124.0           0.11390            0.30940   \n",
       "567           184.60      1821.0           0.16500            0.86810   \n",
       "568            59.16       268.6           0.08996            0.06444   \n",
       "\n",
       "     worst concavity  worst concave points  worst symmetry  \\\n",
       "0             0.7119                0.2654          0.4601   \n",
       "1             0.2416                0.1860          0.2750   \n",
       "2             0.4504                0.2430          0.3613   \n",
       "3             0.6869                0.2575          0.6638   \n",
       "4             0.4000                0.1625          0.2364   \n",
       "..               ...                   ...             ...   \n",
       "564           0.4107                0.2216          0.2060   \n",
       "565           0.3215                0.1628          0.2572   \n",
       "566           0.3403                0.1418          0.2218   \n",
       "567           0.9387                0.2650          0.4087   \n",
       "568           0.0000                0.0000          0.2871   \n",
       "\n",
       "     worst fractal dimension  \n",
       "0                    0.11890  \n",
       "1                    0.08902  \n",
       "2                    0.08758  \n",
       "3                    0.17300  \n",
       "4                    0.07678  \n",
       "..                       ...  \n",
       "564                  0.07115  \n",
       "565                  0.06637  \n",
       "566                  0.07820  \n",
       "567                  0.12400  \n",
       "568                  0.07039  \n",
       "\n",
       "[569 rows x 30 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['malignant', 'benign'], dtype='<U9')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer['target_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale_data=scaler.transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca=PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=2)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(scale_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_pca=pca.transform(scale_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 2)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Second Principal Component')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(x_pca[:,0],x_pca[:,1],c=cancer['target'])\n",
    "plt.xlabel(\"First Principal Component\")\n",
    "plt.ylabel(\"Second Principal Component\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.21890244,  0.10372458,  0.22753729,  0.22099499,  0.14258969,\n",
       "         0.23928535,  0.25840048,  0.26085376,  0.13816696,  0.06436335,\n",
       "         0.20597878,  0.01742803,  0.21132592,  0.20286964,  0.01453145,\n",
       "         0.17039345,  0.15358979,  0.1834174 ,  0.04249842,  0.10256832,\n",
       "         0.22799663,  0.10446933,  0.23663968,  0.22487053,  0.12795256,\n",
       "         0.21009588,  0.22876753,  0.25088597,  0.12290456,  0.13178394],\n",
       "       [-0.23385713, -0.05970609, -0.21518136, -0.23107671,  0.18611302,\n",
       "         0.15189161,  0.06016536, -0.0347675 ,  0.19034877,  0.36657547,\n",
       "        -0.10555215,  0.08997968, -0.08945723, -0.15229263,  0.20443045,\n",
       "         0.2327159 ,  0.19720728,  0.13032156,  0.183848  ,  0.28009203,\n",
       "        -0.21986638, -0.0454673 , -0.19987843, -0.21935186,  0.17230435,\n",
       "         0.14359317,  0.09796411, -0.00825724,  0.14188335,  0.27533947]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-48-ed973fc7d36e>:1: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  df.comp=pd.DataFrame(pca.components_,columns=cancer['feature_names'])\n"
     ]
    }
   ],
   "source": [
    "df.comp=pd.DataFrame(pca.components_,columns=cancer['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.218902</td>\n",
       "      <td>0.103725</td>\n",
       "      <td>0.227537</td>\n",
       "      <td>0.220995</td>\n",
       "      <td>0.142590</td>\n",
       "      <td>0.239285</td>\n",
       "      <td>0.258400</td>\n",
       "      <td>0.260854</td>\n",
       "      <td>0.138167</td>\n",
       "      <td>0.064363</td>\n",
       "      <td>...</td>\n",
       "      <td>0.227997</td>\n",
       "      <td>0.104469</td>\n",
       "      <td>0.236640</td>\n",
       "      <td>0.224871</td>\n",
       "      <td>0.127953</td>\n",
       "      <td>0.210096</td>\n",
       "      <td>0.228768</td>\n",
       "      <td>0.250886</td>\n",
       "      <td>0.122905</td>\n",
       "      <td>0.131784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.233857</td>\n",
       "      <td>-0.059706</td>\n",
       "      <td>-0.215181</td>\n",
       "      <td>-0.231077</td>\n",
       "      <td>0.186113</td>\n",
       "      <td>0.151892</td>\n",
       "      <td>0.060165</td>\n",
       "      <td>-0.034768</td>\n",
       "      <td>0.190349</td>\n",
       "      <td>0.366575</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.219866</td>\n",
       "      <td>-0.045467</td>\n",
       "      <td>-0.199878</td>\n",
       "      <td>-0.219352</td>\n",
       "      <td>0.172304</td>\n",
       "      <td>0.143593</td>\n",
       "      <td>0.097964</td>\n",
       "      <td>-0.008257</td>\n",
       "      <td>0.141883</td>\n",
       "      <td>0.275339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0     0.218902      0.103725        0.227537   0.220995         0.142590   \n",
       "1    -0.233857     -0.059706       -0.215181  -0.231077         0.186113   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0          0.239285        0.258400             0.260854       0.138167   \n",
       "1          0.151892        0.060165            -0.034768       0.190349   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                0.064363  ...      0.227997       0.104469         0.236640   \n",
       "1                0.366575  ...     -0.219866      -0.045467        -0.199878   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0    0.224871          0.127953           0.210096         0.228768   \n",
       "1   -0.219352          0.172304           0.143593         0.097964   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0              0.250886        0.122905                 0.131784  \n",
       "1             -0.008257        0.141883                 0.275339  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(df.comp,cmap='plasma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x_pca,cancer['target'],test_size=0.3,random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm1=SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm1.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=svm1.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.85      0.88        66\n",
      "           1       0.91      0.95      0.93       105\n",
      "\n",
      "    accuracy                           0.91       171\n",
      "   macro avg       0.91      0.90      0.91       171\n",
      "weighted avg       0.91      0.91      0.91       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmat=confusion_matrix(y_test,prediction)\n",
    "sns.heatmap(cmat,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.95      0.94        66\n",
      "           1       0.97      0.95      0.96       105\n",
      "\n",
      "    accuracy                           0.95       171\n",
      "   macro avg       0.95      0.95      0.95       171\n",
      "weighted avg       0.95      0.95      0.95       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmat=confusion_matrix(y_test,pred)\n",
    "sns.heatmap(cmat,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}