PCA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris as data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "irisdata=data().data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [4.9, 3.6, 1.4, 0.1],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "irisdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=StandardScaler().fit_transform(irisdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### scaling done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert data set in pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.900681</td>\n",
       "      <td>1.019004</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.143017</td>\n",
       "      <td>-0.131979</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.385353</td>\n",
       "      <td>0.328414</td>\n",
       "      <td>-1.397064</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.506521</td>\n",
       "      <td>0.098217</td>\n",
       "      <td>-1.283389</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.021849</td>\n",
       "      <td>1.249201</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "          0         1         2         3\n",
       "0 -0.900681  1.019004 -1.340227 -1.315444\n",
       "1 -1.143017 -0.131979 -1.340227 -1.315444\n",
       "2 -1.385353  0.328414 -1.397064 -1.315444\n",
       "3 -1.506521  0.098217 -1.283389 -1.315444\n",
       "4 -1.021849  1.249201 -1.340227 -1.315444"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [ 0.33653037, -0.98840402],\n",
       "       [-0.36218338, -2.01923787],\n",
       "       [ 0.28858624, -0.85573032],\n",
       "       [ 0.09136066, -0.18119213],\n",
       "       [ 0.22771687, -0.38492008],\n",
       "       [ 0.57638829, -0.1548736 ],\n",
       "       [-0.44766702, -1.54379203],\n",
       "       [ 0.25673059, -0.5988518 ],\n",
       "       [ 1.84456887,  0.87042131],\n",
       "       [ 1.15788161, -0.69886986],\n",
       "       [ 2.20526679,  0.56201048],\n",
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       "       [ 2.42632899,  2.55666125],\n",
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       "       [ 1.26376667, -1.70674538],\n",
       "       [ 2.0377163 ,  0.91046741],\n",
       "       [ 0.97798073, -0.57176432],\n",
       "       [ 2.89765149,  0.41364106],\n",
       "       [ 1.33323218, -0.48181122],\n",
       "       [ 1.7007339 ,  1.01392187],\n",
       "       [ 1.95432671,  1.0077776 ],\n",
       "       [ 1.17510363, -0.31639447],\n",
       "       [ 1.02095055,  0.06434603],\n",
       "       [ 1.78834992, -0.18736121],\n",
       "       [ 1.86364755,  0.56229073],\n",
       "       [ 2.43595373,  0.25928443],\n",
       "       [ 2.30492772,  2.62632347],\n",
       "       [ 1.86270322, -0.17854949],\n",
       "       [ 1.11414774, -0.29292262],\n",
       "       [ 1.2024733 , -0.81131527],\n",
       "       [ 2.79877045,  0.85680333],\n",
       "       [ 1.57625591,  1.06858111],\n",
       "       [ 1.3462921 ,  0.42243061],\n",
       "       [ 0.92482492,  0.0172231 ],\n",
       "       [ 1.85204505,  0.67612817],\n",
       "       [ 2.01481043,  0.61388564],\n",
       "       [ 1.90178409,  0.68957549],\n",
       "       [ 1.15788161, -0.69886986],\n",
       "       [ 2.04055823,  0.8675206 ],\n",
       "       [ 1.9981471 ,  1.04916875],\n",
       "       [ 1.87050329,  0.38696608],\n",
       "       [ 1.56458048, -0.89668681],\n",
       "       [ 1.5211705 ,  0.26906914],\n",
       "       [ 1.37278779,  1.01125442],\n",
       "       [ 0.96065603, -0.02433167]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcadf=pd.DataFrame(pca.fit_transform(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.264703</td>\n",
       "      <td>0.480027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.080961</td>\n",
       "      <td>-0.674134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.364229</td>\n",
       "      <td>-0.341908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.299384</td>\n",
       "      <td>-0.597395</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.389842</td>\n",
       "      <td>0.646835</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-2.075631</td>\n",
       "      <td>1.489178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-2.444029</td>\n",
       "      <td>0.047644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-2.232847</td>\n",
       "      <td>0.223148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-2.334640</td>\n",
       "      <td>-1.115328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-2.184328</td>\n",
       "      <td>-0.469014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-2.166310</td>\n",
       "      <td>1.043691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-2.326131</td>\n",
       "      <td>0.133078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-2.218451</td>\n",
       "      <td>-0.728676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-2.633101</td>\n",
       "      <td>-0.961507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-2.198741</td>\n",
       "      <td>1.860057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-2.262215</td>\n",
       "      <td>2.686284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-2.207588</td>\n",
       "      <td>1.483609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-2.190350</td>\n",
       "      <td>0.488838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-1.898572</td>\n",
       "      <td>1.405019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-2.343369</td>\n",
       "      <td>1.127849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-1.914323</td>\n",
       "      <td>0.408856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-2.207013</td>\n",
       "      <td>0.924121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-2.774345</td>\n",
       "      <td>0.458344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-1.818670</td>\n",
       "      <td>0.085559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-2.227163</td>\n",
       "      <td>0.137254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-1.951846</td>\n",
       "      <td>-0.625619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-2.051151</td>\n",
       "      <td>0.242164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-2.168577</td>\n",
       "      <td>0.527150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-2.139563</td>\n",
       "      <td>0.313218</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-2.265261</td>\n",
       "      <td>-0.337732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2.037716</td>\n",
       "      <td>0.910467</td>\n",
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       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>0.977981</td>\n",
       "      <td>-0.571764</td>\n",
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       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2.897651</td>\n",
       "      <td>0.413641</td>\n",
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       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>1.333232</td>\n",
       "      <td>-0.481811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>1.700734</td>\n",
       "      <td>1.013922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1.954327</td>\n",
       "      <td>1.007778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1.175104</td>\n",
       "      <td>-0.316394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1.020951</td>\n",
       "      <td>0.064346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1.788350</td>\n",
       "      <td>-0.187361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1.863648</td>\n",
       "      <td>0.562291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2.435954</td>\n",
       "      <td>0.259284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2.304928</td>\n",
       "      <td>2.626323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>1.862703</td>\n",
       "      <td>-0.178549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>1.114148</td>\n",
       "      <td>-0.292923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>1.202473</td>\n",
       "      <td>-0.811315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>2.798770</td>\n",
       "      <td>0.856803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>1.576256</td>\n",
       "      <td>1.068581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1.346292</td>\n",
       "      <td>0.422431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>0.924825</td>\n",
       "      <td>0.017223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1.852045</td>\n",
       "      <td>0.676128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2.014810</td>\n",
       "      <td>0.613886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1.901784</td>\n",
       "      <td>0.689575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1.157882</td>\n",
       "      <td>-0.698870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>2.040558</td>\n",
       "      <td>0.867521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>1.998147</td>\n",
       "      <td>1.049169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>1.870503</td>\n",
       "      <td>0.386966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>1.564580</td>\n",
       "      <td>-0.896687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>1.521170</td>\n",
       "      <td>0.269069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>1.372788</td>\n",
       "      <td>1.011254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0.960656</td>\n",
       "      <td>-0.024332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1\n",
       "0   -2.264703  0.480027\n",
       "1   -2.080961 -0.674134\n",
       "2   -2.364229 -0.341908\n",
       "3   -2.299384 -0.597395\n",
       "4   -2.389842  0.646835\n",
       "5   -2.075631  1.489178\n",
       "6   -2.444029  0.047644\n",
       "7   -2.232847  0.223148\n",
       "8   -2.334640 -1.115328\n",
       "9   -2.184328 -0.469014\n",
       "10  -2.166310  1.043691\n",
       "11  -2.326131  0.133078\n",
       "12  -2.218451 -0.728676\n",
       "13  -2.633101 -0.961507\n",
       "14  -2.198741  1.860057\n",
       "15  -2.262215  2.686284\n",
       "16  -2.207588  1.483609\n",
       "17  -2.190350  0.488838\n",
       "18  -1.898572  1.405019\n",
       "19  -2.343369  1.127849\n",
       "20  -1.914323  0.408856\n",
       "21  -2.207013  0.924121\n",
       "22  -2.774345  0.458344\n",
       "23  -1.818670  0.085559\n",
       "24  -2.227163  0.137254\n",
       "25  -1.951846 -0.625619\n",
       "26  -2.051151  0.242164\n",
       "27  -2.168577  0.527150\n",
       "28  -2.139563  0.313218\n",
       "29  -2.265261 -0.337732\n",
       "..        ...       ...\n",
       "120  2.037716  0.910467\n",
       "121  0.977981 -0.571764\n",
       "122  2.897651  0.413641\n",
       "123  1.333232 -0.481811\n",
       "124  1.700734  1.013922\n",
       "125  1.954327  1.007778\n",
       "126  1.175104 -0.316394\n",
       "127  1.020951  0.064346\n",
       "128  1.788350 -0.187361\n",
       "129  1.863648  0.562291\n",
       "130  2.435954  0.259284\n",
       "131  2.304928  2.626323\n",
       "132  1.862703 -0.178549\n",
       "133  1.114148 -0.292923\n",
       "134  1.202473 -0.811315\n",
       "135  2.798770  0.856803\n",
       "136  1.576256  1.068581\n",
       "137  1.346292  0.422431\n",
       "138  0.924825  0.017223\n",
       "139  1.852045  0.676128\n",
       "140  2.014810  0.613886\n",
       "141  1.901784  0.689575\n",
       "142  1.157882 -0.698870\n",
       "143  2.040558  0.867521\n",
       "144  1.998147  1.049169\n",
       "145  1.870503  0.386966\n",
       "146  1.564580 -0.896687\n",
       "147  1.521170  0.269069\n",
       "148  1.372788  1.011254\n",
       "149  0.960656 -0.024332\n",
       "\n",
       "[150 rows x 2 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcadf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc5b1a9a160>"
      ]
     },
     "execution_count": 42,
     "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": [
    "plt.scatter(df[[0]],df[[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc5b1a57438>"
      ]
     },
     "execution_count": 44,
     "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": [
    "plt.scatter(pcadf[[0]],pcadf[[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#doing learning on without pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf=neighbors.KNeighborsClassifier(n_neighbors=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(df,data().target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9533333333333334"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(df,data().target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now using pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=neighbors.KNeighborsClassifier(n_neighbors=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(pcadf,data().target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9466666666666667"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(pcadf,data().target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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