Chapter_10/Lab-1-PCA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 10.4 - PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10.4 Lab 1: Principal Components Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 5)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>13.2</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>10.0</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>8.1</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>8.8</td>\n",
       "      <td>190</td>\n",
       "      <td>50</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>9.0</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Murder  Assault  UrbanPop  Rape\n",
       "0     Alabama    13.2      236        58  21.2\n",
       "1      Alaska    10.0      263        48  44.5\n",
       "2     Arizona     8.1      294        80  31.0\n",
       "3    Arkansas     8.8      190        50  19.5\n",
       "4  California     9.0      276        91  40.6"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load the data \n",
    "data = pd.read_csv(r'/home/hardik/repositories/An-Introduction-to-Statistical-Learning/data/USArrests.csv')\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data contains states of US\n",
    "- Name  - name of the states\n",
    "- Murder - number of arrests for murder per million pop. \n",
    "- Assault - number of arrests for assaults per million pop.\n",
    "- Urbanpop - percentage of population living in urban area\n",
    "- Rape - number of rape cases per million population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0            Alabama\n",
      "1             Alaska\n",
      "2            Arizona\n",
      "3           Arkansas\n",
      "4         California\n",
      "5           Colorado\n",
      "6        Connecticut\n",
      "7           Delaware\n",
      "8            Florida\n",
      "9            Georgia\n",
      "10            Hawaii\n",
      "11             Idaho\n",
      "12          Illinois\n",
      "13           Indiana\n",
      "14              Iowa\n",
      "15            Kansas\n",
      "16          Kentucky\n",
      "17         Louisiana\n",
      "18             Maine\n",
      "19          Maryland\n",
      "20     Massachusetts\n",
      "21          Michigan\n",
      "22         Minnesota\n",
      "23       Mississippi\n",
      "24          Missouri\n",
      "25           Montana\n",
      "26          Nebraska\n",
      "27            Nevada\n",
      "28     New Hampshire\n",
      "29        New Jersey\n",
      "30        New Mexico\n",
      "31          New York\n",
      "32    North Carolina\n",
      "33      North Dakota\n",
      "34              Ohio\n",
      "35          Oklahoma\n",
      "36            Oregon\n",
      "37      Pennsylvania\n",
      "38      Rhode Island\n",
      "39    South Carolina\n",
      "40      South Dakota\n",
      "41         Tennessee\n",
      "42             Texas\n",
      "43              Utah\n",
      "44           Vermont\n",
      "45          Virginia\n",
      "46        Washington\n",
      "47     West Virginia\n",
      "48         Wisconsin\n",
      "49           Wyoming\n",
      "Name: Unnamed: 0, dtype: object\n"
     ]
    }
   ],
   "source": [
    "states = data.iloc[:,0]\n",
    "#names of the states in alphabatical order\n",
    "print(states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Murder', 'Assault', 'UrbanPop', 'Rape']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.iloc[:,1:]\n",
    "list(X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.00000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.78800</td>\n",
       "      <td>170.760000</td>\n",
       "      <td>65.540000</td>\n",
       "      <td>21.232000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.35551</td>\n",
       "      <td>83.337661</td>\n",
       "      <td>14.474763</td>\n",
       "      <td>9.366385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.80000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>7.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.07500</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>54.500000</td>\n",
       "      <td>15.075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.25000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>20.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.25000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>77.750000</td>\n",
       "      <td>26.175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.40000</td>\n",
       "      <td>337.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Murder     Assault   UrbanPop       Rape\n",
       "count  50.00000   50.000000  50.000000  50.000000\n",
       "mean    7.78800  170.760000  65.540000  21.232000\n",
       "std     4.35551   83.337661  14.474763   9.366385\n",
       "min     0.80000   45.000000  32.000000   7.300000\n",
       "25%     4.07500  109.000000  54.500000  15.075000\n",
       "50%     7.25000  159.000000  66.000000  20.100000\n",
       "75%    11.25000  249.000000  77.750000  26.175000\n",
       "max    17.40000  337.000000  91.000000  46.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can from the above table that they have irregular ranges, and assault has the highest value of mean and also std (also variance which is square of std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, if we appply PCA to this data, it will give the highest priority to assault, and to avoid this, we will be scaling the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling the data \n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.255179</td>\n",
       "      <td>0.790787</td>\n",
       "      <td>-0.526195</td>\n",
       "      <td>-0.003451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.513019</td>\n",
       "      <td>1.118060</td>\n",
       "      <td>-1.224067</td>\n",
       "      <td>2.509424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.072361</td>\n",
       "      <td>1.493817</td>\n",
       "      <td>1.009122</td>\n",
       "      <td>1.053466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.234708</td>\n",
       "      <td>0.233212</td>\n",
       "      <td>-1.084492</td>\n",
       "      <td>-0.186794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.281093</td>\n",
       "      <td>1.275635</td>\n",
       "      <td>1.776781</td>\n",
       "      <td>2.088814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Murder   Assault  UrbanPop      Rape\n",
       "0  1.255179  0.790787 -0.526195 -0.003451\n",
       "1  0.513019  1.118060 -1.224067  2.509424\n",
       "2  0.072361  1.493817  1.009122  1.053466\n",
       "3  0.234708  0.233212 -1.084492 -0.186794\n",
       "4  0.281093  1.275635  1.776781  2.088814"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled_df = pd.DataFrame(X_scaled,columns = X.columns)\n",
    "X_scaled_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>5.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-8.437695e-17</td>\n",
       "      <td>1.298961e-16</td>\n",
       "      <td>-4.263256e-16</td>\n",
       "      <td>8.326673e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.010153e+00</td>\n",
       "      <td>1.010153e+00</td>\n",
       "      <td>1.010153e+00</td>\n",
       "      <td>1.010153e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.620693e+00</td>\n",
       "      <td>-1.524362e+00</td>\n",
       "      <td>-2.340661e+00</td>\n",
       "      <td>-1.502548e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-8.611383e-01</td>\n",
       "      <td>-7.486054e-01</td>\n",
       "      <td>-7.704502e-01</td>\n",
       "      <td>-6.640245e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-1.247758e-01</td>\n",
       "      <td>-1.425453e-01</td>\n",
       "      <td>3.210209e-02</td>\n",
       "      <td>-1.220847e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.029251e-01</td>\n",
       "      <td>9.483628e-01</td>\n",
       "      <td>8.521012e-01</td>\n",
       "      <td>5.330962e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.229265e+00</td>\n",
       "      <td>2.015028e+00</td>\n",
       "      <td>1.776781e+00</td>\n",
       "      <td>2.671197e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Murder       Assault      UrbanPop          Rape\n",
       "count  5.000000e+01  5.000000e+01  5.000000e+01  5.000000e+01\n",
       "mean  -8.437695e-17  1.298961e-16 -4.263256e-16  8.326673e-16\n",
       "std    1.010153e+00  1.010153e+00  1.010153e+00  1.010153e+00\n",
       "min   -1.620693e+00 -1.524362e+00 -2.340661e+00 -1.502548e+00\n",
       "25%   -8.611383e-01 -7.486054e-01 -7.704502e-01 -6.640245e-01\n",
       "50%   -1.247758e-01 -1.425453e-01  3.210209e-02 -1.220847e-01\n",
       "75%    8.029251e-01  9.483628e-01  8.521012e-01  5.330962e-01\n",
       "max    2.229265e+00  2.015028e+00  1.776781e+00  2.671197e+00"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the mean is approximately 0, and std is approximately 1 for all the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using pca to get principal components \n",
    "pca = PCA(n_components=4)\n",
    "pca.fit(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC_1</th>\n",
       "      <th>PC_2</th>\n",
       "      <th>PC_3</th>\n",
       "      <th>PC_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Murder</th>\n",
       "      <td>0.535899</td>\n",
       "      <td>0.418181</td>\n",
       "      <td>-0.341233</td>\n",
       "      <td>0.649228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assault</th>\n",
       "      <td>0.583184</td>\n",
       "      <td>0.187986</td>\n",
       "      <td>-0.268148</td>\n",
       "      <td>-0.743407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UrbanPop</th>\n",
       "      <td>0.278191</td>\n",
       "      <td>-0.872806</td>\n",
       "      <td>-0.378016</td>\n",
       "      <td>0.133878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rape</th>\n",
       "      <td>0.543432</td>\n",
       "      <td>-0.167319</td>\n",
       "      <td>0.817778</td>\n",
       "      <td>0.089024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              PC_1      PC_2      PC_3      PC_4\n",
       "Murder    0.535899  0.418181 -0.341233  0.649228\n",
       "Assault   0.583184  0.187986 -0.268148 -0.743407\n",
       "UrbanPop  0.278191 -0.872806 -0.378016  0.133878\n",
       "Rape      0.543432 -0.167319  0.817778  0.089024"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading vectors\n",
    "pd.DataFrame(pca.components_,columns = X.columns,index = ['PC_1','PC_2','PC_3','PC_4']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above table we can see the loding vectors as combinations of each principal components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 4)\n"
     ]
    },
    {
     "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>PC_1</th>\n",
       "      <th>PC_2</th>\n",
       "      <th>PC_3</th>\n",
       "      <th>PC_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.985566</td>\n",
       "      <td>1.133392</td>\n",
       "      <td>-0.444269</td>\n",
       "      <td>0.156267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.950138</td>\n",
       "      <td>1.073213</td>\n",
       "      <td>2.040003</td>\n",
       "      <td>-0.438583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.763164</td>\n",
       "      <td>-0.745957</td>\n",
       "      <td>0.054781</td>\n",
       "      <td>-0.834653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.141420</td>\n",
       "      <td>1.119797</td>\n",
       "      <td>0.114574</td>\n",
       "      <td>-0.182811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.523980</td>\n",
       "      <td>-1.542934</td>\n",
       "      <td>0.598557</td>\n",
       "      <td>-0.341996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PC_1      PC_2      PC_3      PC_4\n",
       "0  0.985566  1.133392 -0.444269  0.156267\n",
       "1  1.950138  1.073213  2.040003 -0.438583\n",
       "2  1.763164 -0.745957  0.054781 -0.834653\n",
       "3 -0.141420  1.119797  0.114574 -0.182811\n",
       "4  2.523980 -1.542934  0.598557 -0.341996"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the points in principal components\n",
    "pca_components = pca.transform(X_scaled)\n",
    "print(pca_components.shape)\n",
    "df = pd.DataFrame(pca_components,columns = ['PC_1','PC_2','PC_3','PC_4'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# biplot \n",
    "fig, ax = plt.subplots(figsize = (20,12))\n",
    "ax.grid(True)\n",
    "ax.scatter(df['PC_1'],df['PC_2'])\n",
    "\n",
    "names = list(data.iloc[:,0])\n",
    "\n",
    "for i, txt in enumerate(names):\n",
    "    ax.annotate(txt, (df['PC_1'][i], df['PC_2'][i]))\n",
    "    \n",
    "xvector = pca.components_[0] # see 'prcomp(my_data)$rotation' in R\n",
    "yvector = pca.components_[1]\n",
    "\n",
    "xs = pca.transform(X_scaled)[:,0] # see 'prcomp(my_data)$x' in R\n",
    "ys = pca.transform(X_scaled)[:,1]\n",
    "\n",
    "\n",
    "## visualize projections\n",
    "    \n",
    "## Note: scale values for arrows and text are a bit inelegant as of now,\n",
    "##       so feel free to play around with them\n",
    "\n",
    "for i in range(len(xvector)):\n",
    "# arrows project features (ie columns from csv) as vectors onto PC axes\n",
    "    plt.arrow(0, 0, xvector[i], yvector[i],\n",
    "              color='r', width=0.0005, head_width=0.0025)\n",
    "    plt.text(xvector[i]*1.2, yvector[i]*1.2,\n",
    "             list(X.columns.values)[i], color='r')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.53085875, 1.00996444, 0.36383998, 0.17696948])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# varianc explained by each pc's\n",
    "pca.explained_variance_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.62006039, 0.24744129, 0.0891408 , 0.04335752])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# variance explained ratio's \n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the first pc explaines 2.53 units of variance, which is around 62% of the total variance, the second pc expains 34.7 % of the variance in the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f45bc9c3370>"
      ]
     },
     "execution_count": 35,
     "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": [
    "pd.Series(pca.explained_variance_ratio_,index = ['PC_1','PC_2','PC_3','PC_4']).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f45bc989850>"
      ]
     },
     "execution_count": 37,
     "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": [
    "pd.Series(np.cumsum(pca.explained_variance_ratio_),index = ['PC_1','PC_2','PC_3','PC_4']).plot.bar()"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}