pca-using-graduate-admissions-dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Principal Component Analysis (PCA) is Easy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Admission_Predict_Ver1.1.csv']\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "import os\n",
    "print(os.listdir(\"./input\"))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Don't know what is PCA? Check out Siraj's video on PCA [here](https://youtu.be/jPmV3j1dAv4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading libraries and input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# reading dataset\n",
    "df=pd.read_csv('./input/Admission_Predict_Ver1.1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Checking the data:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>337</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>324</td>\n",
       "      <td>107</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>316</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
       "0           1        337          118                  4  4.5   4.5  9.65   \n",
       "1           2        324          107                  4  4.0   4.5  8.87   \n",
       "2           3        316          104                  3  3.0   3.5  8.00   \n",
       "3           4        322          110                  3  3.5   2.5  8.67   \n",
       "4           5        314          103                  2  2.0   3.0  8.21   \n",
       "\n",
       "   Research  Chance of Admit   \n",
       "0         1              0.92  \n",
       "1         1              0.76  \n",
       "2         1              0.72  \n",
       "3         1              0.80  \n",
       "4         0              0.65  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500 entries, 0 to 499\n",
      "Data columns (total 9 columns):\n",
      "Serial No.           500 non-null int64\n",
      "GRE Score            500 non-null int64\n",
      "TOEFL Score          500 non-null int64\n",
      "University Rating    500 non-null int64\n",
      "SOP                  500 non-null float64\n",
      "LOR                  500 non-null float64\n",
      "CGPA                 500 non-null float64\n",
      "Research             500 non-null int64\n",
      "Chance of Admit      500 non-null float64\n",
      "dtypes: float64(4), int64(5)\n",
      "memory usage: 35.2 KB\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.00000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>250.500000</td>\n",
       "      <td>316.472000</td>\n",
       "      <td>107.192000</td>\n",
       "      <td>3.114000</td>\n",
       "      <td>3.374000</td>\n",
       "      <td>3.48400</td>\n",
       "      <td>8.576440</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.72174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>144.481833</td>\n",
       "      <td>11.295148</td>\n",
       "      <td>6.081868</td>\n",
       "      <td>1.143512</td>\n",
       "      <td>0.991004</td>\n",
       "      <td>0.92545</td>\n",
       "      <td>0.604813</td>\n",
       "      <td>0.496884</td>\n",
       "      <td>0.14114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.34000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>125.750000</td>\n",
       "      <td>308.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>8.127500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.63000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>250.500000</td>\n",
       "      <td>317.000000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.50000</td>\n",
       "      <td>8.560000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>375.250000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>9.040000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.82000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>9.920000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.97000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Serial No.   GRE Score  TOEFL Score  University Rating         SOP  \\\n",
       "count  500.000000  500.000000   500.000000         500.000000  500.000000   \n",
       "mean   250.500000  316.472000   107.192000           3.114000    3.374000   \n",
       "std    144.481833   11.295148     6.081868           1.143512    0.991004   \n",
       "min      1.000000  290.000000    92.000000           1.000000    1.000000   \n",
       "25%    125.750000  308.000000   103.000000           2.000000    2.500000   \n",
       "50%    250.500000  317.000000   107.000000           3.000000    3.500000   \n",
       "75%    375.250000  325.000000   112.000000           4.000000    4.000000   \n",
       "max    500.000000  340.000000   120.000000           5.000000    5.000000   \n",
       "\n",
       "            LOR         CGPA    Research  Chance of Admit   \n",
       "count  500.00000  500.000000  500.000000         500.00000  \n",
       "mean     3.48400    8.576440    0.560000           0.72174  \n",
       "std      0.92545    0.604813    0.496884           0.14114  \n",
       "min      1.00000    6.800000    0.000000           0.34000  \n",
       "25%      3.00000    8.127500    0.000000           0.63000  \n",
       "50%      3.50000    8.560000    1.000000           0.72000  \n",
       "75%      4.00000    9.040000    1.000000           0.82000  \n",
       "max      5.00000    9.920000    1.000000           0.97000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df.head())\n",
    "df.info()\n",
    "display(df.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## No Missing data, thats great!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>337</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>324</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>316</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>314</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  Research\n",
       "0        337          118                  4  4.5   4.5  9.65         1\n",
       "1        324          107                  4  4.0   4.5  8.87         1\n",
       "2        316          104                  3  3.0   3.5  8.00         1\n",
       "3        322          110                  3  3.5   2.5  8.67         1\n",
       "4        314          103                  2  2.0   3.0  8.21         0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0    0.92\n",
       "1    0.76\n",
       "2    0.72\n",
       "3    0.80\n",
       "4    0.65\n",
       "Name: Chance of Admit , dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#dividing into features and labels\n",
    "features=df.iloc[:,1:-1]\n",
    "labels=df.iloc[:,-1]\n",
    "display(features[:5])\n",
    "display(labels[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[good, medium, medium, good, medium]\n",
       "Categories (3, object): [bad < medium < good]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_labels=pd.cut(np.array(labels),3, labels=[\"bad\", \"medium\", \"good\"])\n",
    "print(new_labels.shape)\n",
    "new_labels[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standardization/Normalization of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.81923762,  1.77886545,  0.77558214,  1.13735981,  1.09894429,\n",
       "         1.77680627,  0.88640526],\n",
       "       [ 0.66714832, -0.03160087,  0.77558214,  0.63231549,  1.09894429,\n",
       "         0.48585943,  0.88640526],\n",
       "       [-0.0418297 , -0.52536441, -0.09979274, -0.37777315,  0.01730621,\n",
       "        -0.95404281,  0.88640526],\n",
       "       [ 0.48990382,  0.46216267, -0.09979274,  0.12727117, -1.06433187,\n",
       "         0.15484742,  0.88640526],\n",
       "       [-0.21907421, -0.68995225, -0.97516761, -1.3878618 , -0.52351283,\n",
       "        -0.6064802 , -1.12815215]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalizing the data\n",
    "standardized_data=StandardScaler().fit_transform(features)\n",
    "standardized_data[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA using only Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumPy covariance matrix: \n",
      "[[1.00200401 0.82885812 0.63664951 0.61472713 0.52573085 0.82753302\n",
      "  0.56452718]\n",
      " [0.82885812 1.00200401 0.6511014  0.64570179 0.54264859 0.81219793\n",
      "  0.46794796]\n",
      " [0.63664951 0.6511014  1.00200401 0.72948254 0.60987045 0.70666768\n",
      "  0.42790326]\n",
      " [0.61472713 0.64570179 0.72948254 1.00200401 0.66503693 0.71358149\n",
      "  0.40893371]\n",
      " [0.52573085 0.54264859 0.60987045 0.66503693 1.00200401 0.6387467\n",
      "  0.37327215]\n",
      " [0.82753302 0.81219793 0.70666768 0.71358149 0.6387467  1.00200401\n",
      "  0.50231563]\n",
      " [0.56452718 0.46794796 0.42790326 0.40893371 0.37327215 0.50231563\n",
      "  1.00200401]]\n"
     ]
    }
   ],
   "source": [
    "print('NumPy covariance matrix: \\n%s' %np.cov(standardized_data.T))\n",
    "cov_mat = np.cov(standardized_data.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigenvectors \n",
      "[[ 0.40361694  0.27452263  0.36263743 -0.14482269 -0.73757122  0.25090997\n",
      "   0.04779188]\n",
      " [ 0.40120339  0.11081202  0.46108277 -0.12684268  0.2632937  -0.72652707\n",
      "  -0.03008301]\n",
      " [ 0.3834133  -0.2497745  -0.09291061  0.64173368 -0.02642093 -0.03207901\n",
      "   0.60698049]\n",
      " [ 0.38466158 -0.3434338  -0.17312554  0.32685429 -0.12279282 -0.01981706\n",
      "  -0.76276584]\n",
      " [ 0.3471675  -0.42602733 -0.46450082 -0.64859464 -0.08846344 -0.09562054\n",
      "   0.21111694]\n",
      " [ 0.42103736  0.01504455  0.24073704 -0.13665843  0.59332137  0.62726591\n",
      "  -0.01945709]\n",
      " [ 0.28872336  0.74182095 -0.58615408  0.07022078  0.10503789 -0.07193756\n",
      "  -0.04031081]]\n",
      "\n",
      "Eigenvalues \n",
      "[4.73582567 0.74323771 0.56275335 0.38112753 0.14824242 0.17863639\n",
      " 0.264205  ]\n"
     ]
    }
   ],
   "source": [
    "#Calculating the eigen values and vectors\n",
    "eig_vals, eig_vecs = np.linalg.eig(cov_mat)\n",
    "\n",
    "print('Eigenvectors \\n%s' %eig_vecs)\n",
    "print('\\nEigenvalues \\n%s' %eig_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigenvalues in descending order:\n",
      "4.735825667372012\n",
      "0.7432377092295829\n",
      "0.5627533509380565\n",
      "0.3811275278982486\n",
      "0.2642049984459163\n",
      "0.178636386749477\n",
      "0.14824241547893272\n"
     ]
    }
   ],
   "source": [
    "# Make a list of (eigenvalue, eigenvector) tuples\n",
    "eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[:,i]) for i in range(len(eig_vals))]\n",
    "\n",
    "# Sort the (eigenvalue, eigenvector) tuples from high to low\n",
    "eig_pairs.sort(key=lambda x: x[0], reverse=True)\n",
    "\n",
    "# Visually confirm that the list is correctly sorted by decreasing eigenvalues\n",
    "print('Eigenvalues in descending order:')\n",
    "for i in eig_pairs:\n",
    "    print(i[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix W:\n",
      " [[ 0.40361694  0.27452263]\n",
      " [ 0.40120339  0.11081202]\n",
      " [ 0.3834133  -0.2497745 ]\n",
      " [ 0.38466158 -0.3434338 ]\n",
      " [ 0.3471675  -0.42602733]\n",
      " [ 0.42103736  0.01504455]\n",
      " [ 0.28872336  0.74182095]]\n"
     ]
    }
   ],
   "source": [
    "matrix_w = np.hstack((eig_pairs[0][1].reshape(7,1), \n",
    "                      eig_pairs[1][1].reshape(7,1)))\n",
    "\n",
    "print('Matrix W:\\n', matrix_w)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = standardized_data.dot(matrix_w)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 2)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>1st Component</th>\n",
       "      <th>2nd Component</th>\n",
       "      <th>Chances of getting in?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.56837</td>\n",
       "      <td>0.328318</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.6392</td>\n",
       "      <td>-0.0345504</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.550991</td>\n",
       "      <td>0.720794</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.34547</td>\n",
       "      <td>1.28024</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.0358</td>\n",
       "      <td>-0.0393655</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  1st Component 2nd Component Chances of getting in?\n",
       "0       3.56837      0.328318                   good\n",
       "1        1.6392    -0.0345504                 medium\n",
       "2     -0.550991      0.720794                 medium\n",
       "3       0.34547       1.28024                   good\n",
       "4       -2.0358    -0.0393655                 medium"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_data=np.vstack((Y.T,new_labels)).T\n",
    "pca_df=pd.DataFrame(data=pca_data,columns=(\"1st Component\",\"2nd Component\",\"Chances of getting in?\"))\n",
    "pca_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "# the size of A4 paper\n",
    "fig.set_size_inches(12, 8)\n",
    "sns.scatterplot(x=\"1st Component\", y=\"2nd Component\", hue=\"Chances of getting in?\", data=pca_df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the sklearn to perform PCA. sklearn is amazing, it has it all!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import decomposition\n",
    "pca=decomposition.PCA()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The reduced shape is (500, 2)\n"
     ]
    }
   ],
   "source": [
    "pca.n_components=2\n",
    "pca_data=pca.fit_transform(standardized_data)\n",
    "print(\"The reduced shape is\", pca_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.56837458, -0.32831807],\n",
       "       [-1.6391986 ,  0.03455037],\n",
       "       [ 0.55099145, -0.72079379],\n",
       "       [-0.34547019, -1.28023739],\n",
       "       [ 2.03580394,  0.03936553]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>1st Component</th>\n",
       "      <th>2nd Component</th>\n",
       "      <th>Chances of getting in?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-3.56837</td>\n",
       "      <td>-0.328318</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.6392</td>\n",
       "      <td>0.0345504</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.550991</td>\n",
       "      <td>-0.720794</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.34547</td>\n",
       "      <td>-1.28024</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0358</td>\n",
       "      <td>0.0393655</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  1st Component 2nd Component Chances of getting in?\n",
       "0      -3.56837     -0.328318                   good\n",
       "1       -1.6392     0.0345504                 medium\n",
       "2      0.550991     -0.720794                 medium\n",
       "3      -0.34547      -1.28024                   good\n",
       "4        2.0358     0.0393655                 medium"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_data=np.vstack((pca_data.T,new_labels)).T\n",
    "pca_df=pd.DataFrame(data=pca_data,columns=(\"1st Component\",\"2nd Component\",\"Chances of getting in?\"))\n",
    "pca_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "# the size of A4 paper\n",
    "fig.set_size_inches(12, 8)\n",
    "sns.scatterplot(x=\"1st Component\", y=\"2nd Component\", hue=\"Chances of getting in?\", data=pca_df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# You can see the grouping already! Yayy!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
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