SubTask3-Dimensionality Reduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "37c69356-8f61-44cd-bd61-4da6b9b5b7de",
   "metadata": {},
   "source": [
    "# SubTak3 Dimensionality Reduction\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cbb6d387-c439-463a-8d33-a7156c61ac48",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd                # for data manipulation \n",
    "import matplotlib.pyplot as plt   # for visualization\n",
    "from sklearn.decomposition import PCA   #importing PCA for dimensionality reduction\n",
    "from sklearn.preprocessing import StandardScaler #for scaling the dataset\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6beb5a27-4f76-43bb-ab92-f70d154b5c78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Appliances</th>\n",
       "      <th>lights</th>\n",
       "      <th>T1</th>\n",
       "      <th>RH_1</th>\n",
       "      <th>T2</th>\n",
       "      <th>RH_2</th>\n",
       "      <th>T3</th>\n",
       "      <th>RH_3</th>\n",
       "      <th>T4</th>\n",
       "      <th>RH_4</th>\n",
       "      <th>...</th>\n",
       "      <th>T8</th>\n",
       "      <th>RH_8</th>\n",
       "      <th>T9</th>\n",
       "      <th>RH_9</th>\n",
       "      <th>T_out</th>\n",
       "      <th>Press_mm_hg</th>\n",
       "      <th>RH_out</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>Tdewpoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "      <td>19.89</td>\n",
       "      <td>47.596667</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.790000</td>\n",
       "      <td>19.79</td>\n",
       "      <td>44.730000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>45.566667</td>\n",
       "      <td>...</td>\n",
       "      <td>18.2</td>\n",
       "      <td>48.900000</td>\n",
       "      <td>17.033333</td>\n",
       "      <td>45.53</td>\n",
       "      <td>6.60</td>\n",
       "      <td>733.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.693333</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.722500</td>\n",
       "      <td>19.79</td>\n",
       "      <td>44.790000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>45.992500</td>\n",
       "      <td>...</td>\n",
       "      <td>18.2</td>\n",
       "      <td>48.863333</td>\n",
       "      <td>17.066667</td>\n",
       "      <td>45.56</td>\n",
       "      <td>6.48</td>\n",
       "      <td>733.6</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>59.166667</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>30</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.300000</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.626667</td>\n",
       "      <td>19.79</td>\n",
       "      <td>44.933333</td>\n",
       "      <td>18.926667</td>\n",
       "      <td>45.890000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.2</td>\n",
       "      <td>48.730000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.50</td>\n",
       "      <td>6.37</td>\n",
       "      <td>733.7</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>55.333333</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>40</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.066667</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.590000</td>\n",
       "      <td>19.79</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>18.890000</td>\n",
       "      <td>45.723333</td>\n",
       "      <td>...</td>\n",
       "      <td>18.1</td>\n",
       "      <td>48.590000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.40</td>\n",
       "      <td>6.25</td>\n",
       "      <td>733.8</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>51.500000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>40</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.333333</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.530000</td>\n",
       "      <td>19.79</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>18.890000</td>\n",
       "      <td>45.530000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.1</td>\n",
       "      <td>48.590000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.40</td>\n",
       "      <td>6.13</td>\n",
       "      <td>733.9</td>\n",
       "      <td>92.0</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>47.666667</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Appliances  lights     T1       RH_1    T2       RH_2     T3       RH_3  \\\n",
       "0          60      30  19.89  47.596667  19.2  44.790000  19.79  44.730000   \n",
       "1          60      30  19.89  46.693333  19.2  44.722500  19.79  44.790000   \n",
       "2          50      30  19.89  46.300000  19.2  44.626667  19.79  44.933333   \n",
       "3          50      40  19.89  46.066667  19.2  44.590000  19.79  45.000000   \n",
       "4          60      40  19.89  46.333333  19.2  44.530000  19.79  45.000000   \n",
       "\n",
       "          T4       RH_4  ...    T8       RH_8         T9   RH_9  T_out  \\\n",
       "0  19.000000  45.566667  ...  18.2  48.900000  17.033333  45.53   6.60   \n",
       "1  19.000000  45.992500  ...  18.2  48.863333  17.066667  45.56   6.48   \n",
       "2  18.926667  45.890000  ...  18.2  48.730000  17.000000  45.50   6.37   \n",
       "3  18.890000  45.723333  ...  18.1  48.590000  17.000000  45.40   6.25   \n",
       "4  18.890000  45.530000  ...  18.1  48.590000  17.000000  45.40   6.13   \n",
       "\n",
       "   Press_mm_hg  RH_out  Windspeed  Visibility  Tdewpoint  \n",
       "0        733.5    92.0   7.000000   63.000000        5.3  \n",
       "1        733.6    92.0   6.666667   59.166667        5.2  \n",
       "2        733.7    92.0   6.333333   55.333333        5.1  \n",
       "3        733.8    92.0   6.000000   51.500000        5.0  \n",
       "4        733.9    92.0   5.666667   47.666667        4.9  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('energydata_complete.csv')   # import the dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "11a0cbca-c806-4048-bc7e-c704bb12eeac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data:\n",
      "(19735, 26)\n"
     ]
    }
   ],
   "source": [
    "print('shape of data:\\n{}'.format(df.shape))  # get the shape of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "13fd872a-d7ed-4d9a-b1d4-f6cc03db56b7",
   "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>Appliances</th>\n",
       "      <th>lights</th>\n",
       "      <th>T1</th>\n",
       "      <th>RH_1</th>\n",
       "      <th>T2</th>\n",
       "      <th>RH_2</th>\n",
       "      <th>T3</th>\n",
       "      <th>RH_3</th>\n",
       "      <th>T4</th>\n",
       "      <th>RH_4</th>\n",
       "      <th>...</th>\n",
       "      <th>T8</th>\n",
       "      <th>RH_8</th>\n",
       "      <th>T9</th>\n",
       "      <th>RH_9</th>\n",
       "      <th>T_out</th>\n",
       "      <th>Press_mm_hg</th>\n",
       "      <th>RH_out</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>Tdewpoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>97.694958</td>\n",
       "      <td>3.801875</td>\n",
       "      <td>21.686571</td>\n",
       "      <td>40.259739</td>\n",
       "      <td>20.341219</td>\n",
       "      <td>40.420420</td>\n",
       "      <td>22.267611</td>\n",
       "      <td>39.242500</td>\n",
       "      <td>20.855335</td>\n",
       "      <td>39.026904</td>\n",
       "      <td>...</td>\n",
       "      <td>22.029107</td>\n",
       "      <td>42.936165</td>\n",
       "      <td>19.485828</td>\n",
       "      <td>41.552401</td>\n",
       "      <td>7.412580</td>\n",
       "      <td>755.522602</td>\n",
       "      <td>79.750418</td>\n",
       "      <td>4.039752</td>\n",
       "      <td>38.330834</td>\n",
       "      <td>3.760995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>102.524891</td>\n",
       "      <td>7.935988</td>\n",
       "      <td>1.606066</td>\n",
       "      <td>3.979299</td>\n",
       "      <td>2.192974</td>\n",
       "      <td>4.069813</td>\n",
       "      <td>2.006111</td>\n",
       "      <td>3.254576</td>\n",
       "      <td>2.042884</td>\n",
       "      <td>4.341321</td>\n",
       "      <td>...</td>\n",
       "      <td>1.956162</td>\n",
       "      <td>5.224361</td>\n",
       "      <td>2.014712</td>\n",
       "      <td>4.151497</td>\n",
       "      <td>5.318464</td>\n",
       "      <td>7.399441</td>\n",
       "      <td>14.901088</td>\n",
       "      <td>2.451221</td>\n",
       "      <td>11.794719</td>\n",
       "      <td>4.195248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.790000</td>\n",
       "      <td>27.023333</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>20.463333</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>28.766667</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>27.660000</td>\n",
       "      <td>...</td>\n",
       "      <td>16.306667</td>\n",
       "      <td>29.600000</td>\n",
       "      <td>14.890000</td>\n",
       "      <td>29.166667</td>\n",
       "      <td>-5.000000</td>\n",
       "      <td>729.300000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-6.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.760000</td>\n",
       "      <td>37.333333</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>37.900000</td>\n",
       "      <td>20.790000</td>\n",
       "      <td>36.900000</td>\n",
       "      <td>19.530000</td>\n",
       "      <td>35.530000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.790000</td>\n",
       "      <td>39.066667</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>3.670000</td>\n",
       "      <td>750.933333</td>\n",
       "      <td>70.333333</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>39.656667</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>40.500000</td>\n",
       "      <td>22.100000</td>\n",
       "      <td>38.530000</td>\n",
       "      <td>20.666667</td>\n",
       "      <td>38.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>22.100000</td>\n",
       "      <td>42.375000</td>\n",
       "      <td>19.390000</td>\n",
       "      <td>40.900000</td>\n",
       "      <td>6.920000</td>\n",
       "      <td>756.100000</td>\n",
       "      <td>83.666667</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>43.066667</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>43.260000</td>\n",
       "      <td>23.290000</td>\n",
       "      <td>41.760000</td>\n",
       "      <td>22.100000</td>\n",
       "      <td>42.156667</td>\n",
       "      <td>...</td>\n",
       "      <td>23.390000</td>\n",
       "      <td>46.536000</td>\n",
       "      <td>20.600000</td>\n",
       "      <td>44.338095</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>760.933333</td>\n",
       "      <td>91.666667</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>6.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1080.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>26.260000</td>\n",
       "      <td>63.360000</td>\n",
       "      <td>29.856667</td>\n",
       "      <td>56.026667</td>\n",
       "      <td>29.236000</td>\n",
       "      <td>50.163333</td>\n",
       "      <td>26.200000</td>\n",
       "      <td>51.090000</td>\n",
       "      <td>...</td>\n",
       "      <td>27.230000</td>\n",
       "      <td>58.780000</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>53.326667</td>\n",
       "      <td>26.100000</td>\n",
       "      <td>772.300000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>15.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Appliances        lights            T1          RH_1            T2  \\\n",
       "count  19735.000000  19735.000000  19735.000000  19735.000000  19735.000000   \n",
       "mean      97.694958      3.801875     21.686571     40.259739     20.341219   \n",
       "std      102.524891      7.935988      1.606066      3.979299      2.192974   \n",
       "min       10.000000      0.000000     16.790000     27.023333     16.100000   \n",
       "25%       50.000000      0.000000     20.760000     37.333333     18.790000   \n",
       "50%       60.000000      0.000000     21.600000     39.656667     20.000000   \n",
       "75%      100.000000      0.000000     22.600000     43.066667     21.500000   \n",
       "max     1080.000000     70.000000     26.260000     63.360000     29.856667   \n",
       "\n",
       "               RH_2            T3          RH_3            T4          RH_4  \\\n",
       "count  19735.000000  19735.000000  19735.000000  19735.000000  19735.000000   \n",
       "mean      40.420420     22.267611     39.242500     20.855335     39.026904   \n",
       "std        4.069813      2.006111      3.254576      2.042884      4.341321   \n",
       "min       20.463333     17.200000     28.766667     15.100000     27.660000   \n",
       "25%       37.900000     20.790000     36.900000     19.530000     35.530000   \n",
       "50%       40.500000     22.100000     38.530000     20.666667     38.400000   \n",
       "75%       43.260000     23.290000     41.760000     22.100000     42.156667   \n",
       "max       56.026667     29.236000     50.163333     26.200000     51.090000   \n",
       "\n",
       "       ...            T8          RH_8            T9          RH_9  \\\n",
       "count  ...  19735.000000  19735.000000  19735.000000  19735.000000   \n",
       "mean   ...     22.029107     42.936165     19.485828     41.552401   \n",
       "std    ...      1.956162      5.224361      2.014712      4.151497   \n",
       "min    ...     16.306667     29.600000     14.890000     29.166667   \n",
       "25%    ...     20.790000     39.066667     18.000000     38.500000   \n",
       "50%    ...     22.100000     42.375000     19.390000     40.900000   \n",
       "75%    ...     23.390000     46.536000     20.600000     44.338095   \n",
       "max    ...     27.230000     58.780000     24.500000     53.326667   \n",
       "\n",
       "              T_out   Press_mm_hg        RH_out     Windspeed    Visibility  \\\n",
       "count  19735.000000  19735.000000  19735.000000  19735.000000  19735.000000   \n",
       "mean       7.412580    755.522602     79.750418      4.039752     38.330834   \n",
       "std        5.318464      7.399441     14.901088      2.451221     11.794719   \n",
       "min       -5.000000    729.300000     24.000000      0.000000      1.000000   \n",
       "25%        3.670000    750.933333     70.333333      2.000000     29.000000   \n",
       "50%        6.920000    756.100000     83.666667      3.666667     40.000000   \n",
       "75%       10.400000    760.933333     91.666667      5.500000     40.000000   \n",
       "max       26.100000    772.300000    100.000000     14.000000     66.000000   \n",
       "\n",
       "          Tdewpoint  \n",
       "count  19735.000000  \n",
       "mean       3.760995  \n",
       "std        4.195248  \n",
       "min       -6.600000  \n",
       "25%        0.900000  \n",
       "50%        3.430000  \n",
       "75%        6.570000  \n",
       "max       15.500000  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()    # gaining insights into attributes of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "172ae50e-283d-46ab-8f19-600040124dbe",
   "metadata": {},
   "source": [
    "## Decide on Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cdf2f7a-6d68-4279-a08e-b9ff1cc883a7",
   "metadata": {},
   "source": [
    "- As we can see the mean values and standard deviations differ significantly across columns. Additionally, the range of values \n",
    "(min to max) also varies widely across different columns. as a result normalization is essential before employing PCA when dealing with datasets where\n",
    "the features have significantly different scales. This ensures that the principal components are not skewed towards features with larger scales."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9a37b30-3854-4efb-afb7-0ae8222153d6",
   "metadata": {},
   "source": [
    "## Normalize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "145477c3-8181-4563-8061-c46674b94d85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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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>Appliances</th>\n",
       "      <th>lights</th>\n",
       "      <th>T1</th>\n",
       "      <th>RH_1</th>\n",
       "      <th>T2</th>\n",
       "      <th>RH_2</th>\n",
       "      <th>T3</th>\n",
       "      <th>RH_3</th>\n",
       "      <th>T4</th>\n",
       "      <th>RH_4</th>\n",
       "      <th>...</th>\n",
       "      <th>T8</th>\n",
       "      <th>RH_8</th>\n",
       "      <th>T9</th>\n",
       "      <th>RH_9</th>\n",
       "      <th>T_out</th>\n",
       "      <th>Press_mm_hg</th>\n",
       "      <th>RH_out</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>Tdewpoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.367676</td>\n",
       "      <td>3.301264</td>\n",
       "      <td>-1.118645</td>\n",
       "      <td>1.843821</td>\n",
       "      <td>-0.520411</td>\n",
       "      <td>1.073683</td>\n",
       "      <td>-1.235063</td>\n",
       "      <td>1.686130</td>\n",
       "      <td>-0.908217</td>\n",
       "      <td>1.506438</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.957509</td>\n",
       "      <td>1.141572</td>\n",
       "      <td>-1.217324</td>\n",
       "      <td>0.958136</td>\n",
       "      <td>-0.152788</td>\n",
       "      <td>-2.976328</td>\n",
       "      <td>0.822080</td>\n",
       "      <td>1.207694</td>\n",
       "      <td>2.091596</td>\n",
       "      <td>0.366854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.367676</td>\n",
       "      <td>3.301264</td>\n",
       "      <td>-1.118645</td>\n",
       "      <td>1.616807</td>\n",
       "      <td>-0.520411</td>\n",
       "      <td>1.057097</td>\n",
       "      <td>-1.235063</td>\n",
       "      <td>1.704566</td>\n",
       "      <td>-0.908217</td>\n",
       "      <td>1.604528</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.957509</td>\n",
       "      <td>1.134554</td>\n",
       "      <td>-1.200778</td>\n",
       "      <td>0.965363</td>\n",
       "      <td>-0.175352</td>\n",
       "      <td>-2.962813</td>\n",
       "      <td>0.822080</td>\n",
       "      <td>1.071703</td>\n",
       "      <td>1.766584</td>\n",
       "      <td>0.343017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.465215</td>\n",
       "      <td>3.301264</td>\n",
       "      <td>-1.118645</td>\n",
       "      <td>1.517959</td>\n",
       "      <td>-0.520411</td>\n",
       "      <td>1.033550</td>\n",
       "      <td>-1.235063</td>\n",
       "      <td>1.748608</td>\n",
       "      <td>-0.944115</td>\n",
       "      <td>1.580918</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.957509</td>\n",
       "      <td>1.109032</td>\n",
       "      <td>-1.233869</td>\n",
       "      <td>0.950910</td>\n",
       "      <td>-0.196035</td>\n",
       "      <td>-2.949298</td>\n",
       "      <td>0.822080</td>\n",
       "      <td>0.935713</td>\n",
       "      <td>1.441572</td>\n",
       "      <td>0.319180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.465215</td>\n",
       "      <td>4.561378</td>\n",
       "      <td>-1.118645</td>\n",
       "      <td>1.459321</td>\n",
       "      <td>-0.520411</td>\n",
       "      <td>1.024540</td>\n",
       "      <td>-1.235063</td>\n",
       "      <td>1.769092</td>\n",
       "      <td>-0.962063</td>\n",
       "      <td>1.542526</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.008631</td>\n",
       "      <td>1.082233</td>\n",
       "      <td>-1.233869</td>\n",
       "      <td>0.926821</td>\n",
       "      <td>-0.218599</td>\n",
       "      <td>-2.935783</td>\n",
       "      <td>0.822080</td>\n",
       "      <td>0.799723</td>\n",
       "      <td>1.116559</td>\n",
       "      <td>0.295343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.367676</td>\n",
       "      <td>4.561378</td>\n",
       "      <td>-1.118645</td>\n",
       "      <td>1.526336</td>\n",
       "      <td>-0.520411</td>\n",
       "      <td>1.009797</td>\n",
       "      <td>-1.235063</td>\n",
       "      <td>1.769092</td>\n",
       "      <td>-0.962063</td>\n",
       "      <td>1.497991</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.008631</td>\n",
       "      <td>1.082233</td>\n",
       "      <td>-1.233869</td>\n",
       "      <td>0.926821</td>\n",
       "      <td>-0.241162</td>\n",
       "      <td>-2.922268</td>\n",
       "      <td>0.822080</td>\n",
       "      <td>0.663733</td>\n",
       "      <td>0.791547</td>\n",
       "      <td>0.271506</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>19730</th>\n",
       "      <td>0.022483</td>\n",
       "      <td>-0.479080</td>\n",
       "      <td>2.415962</td>\n",
       "      <td>1.583299</td>\n",
       "      <td>2.530318</td>\n",
       "      <td>0.394449</td>\n",
       "      <td>2.458745</td>\n",
       "      <td>0.590209</td>\n",
       "      <td>1.882026</td>\n",
       "      <td>1.511812</td>\n",
       "      <td>...</td>\n",
       "      <td>1.365409</td>\n",
       "      <td>1.366295</td>\n",
       "      <td>1.843571</td>\n",
       "      <td>1.261649</td>\n",
       "      <td>2.874478</td>\n",
       "      <td>-0.043599</td>\n",
       "      <td>-1.616282</td>\n",
       "      <td>-0.288198</td>\n",
       "      <td>-1.243314</td>\n",
       "      <td>2.273822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19731</th>\n",
       "      <td>-0.075056</td>\n",
       "      <td>-0.479080</td>\n",
       "      <td>2.374452</td>\n",
       "      <td>1.568221</td>\n",
       "      <td>2.468301</td>\n",
       "      <td>0.407788</td>\n",
       "      <td>2.425512</td>\n",
       "      <td>0.608646</td>\n",
       "      <td>1.882026</td>\n",
       "      <td>1.511812</td>\n",
       "      <td>...</td>\n",
       "      <td>1.365409</td>\n",
       "      <td>1.311932</td>\n",
       "      <td>1.843571</td>\n",
       "      <td>1.261649</td>\n",
       "      <td>2.855675</td>\n",
       "      <td>-0.043599</td>\n",
       "      <td>-1.593912</td>\n",
       "      <td>-0.220203</td>\n",
       "      <td>-1.172659</td>\n",
       "      <td>2.273822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19732</th>\n",
       "      <td>1.680659</td>\n",
       "      <td>0.781035</td>\n",
       "      <td>2.374452</td>\n",
       "      <td>1.592514</td>\n",
       "      <td>2.411103</td>\n",
       "      <td>0.576982</td>\n",
       "      <td>2.383971</td>\n",
       "      <td>0.752037</td>\n",
       "      <td>1.882026</td>\n",
       "      <td>1.544061</td>\n",
       "      <td>...</td>\n",
       "      <td>1.365409</td>\n",
       "      <td>1.287048</td>\n",
       "      <td>1.843571</td>\n",
       "      <td>1.261649</td>\n",
       "      <td>2.836872</td>\n",
       "      <td>-0.043599</td>\n",
       "      <td>-1.571542</td>\n",
       "      <td>-0.152208</td>\n",
       "      <td>-1.102004</td>\n",
       "      <td>2.273822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19733</th>\n",
       "      <td>3.143756</td>\n",
       "      <td>0.781035</td>\n",
       "      <td>2.374452</td>\n",
       "      <td>1.691361</td>\n",
       "      <td>2.313256</td>\n",
       "      <td>0.642694</td>\n",
       "      <td>2.304213</td>\n",
       "      <td>0.629130</td>\n",
       "      <td>1.882026</td>\n",
       "      <td>1.557883</td>\n",
       "      <td>...</td>\n",
       "      <td>1.346238</td>\n",
       "      <td>1.260011</td>\n",
       "      <td>1.843571</td>\n",
       "      <td>1.268273</td>\n",
       "      <td>2.799266</td>\n",
       "      <td>-0.043599</td>\n",
       "      <td>-1.549171</td>\n",
       "      <td>-0.084213</td>\n",
       "      <td>-1.031349</td>\n",
       "      <td>2.249985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19734</th>\n",
       "      <td>3.241295</td>\n",
       "      <td>0.781035</td>\n",
       "      <td>2.374452</td>\n",
       "      <td>1.593351</td>\n",
       "      <td>2.244984</td>\n",
       "      <td>0.626828</td>\n",
       "      <td>2.270980</td>\n",
       "      <td>0.588161</td>\n",
       "      <td>1.882026</td>\n",
       "      <td>1.597810</td>\n",
       "      <td>...</td>\n",
       "      <td>1.383813</td>\n",
       "      <td>1.301596</td>\n",
       "      <td>1.843571</td>\n",
       "      <td>1.274897</td>\n",
       "      <td>2.780464</td>\n",
       "      <td>-0.043599</td>\n",
       "      <td>-1.526801</td>\n",
       "      <td>-0.016218</td>\n",
       "      <td>-0.960694</td>\n",
       "      <td>2.249985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19735 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Appliances    lights        T1      RH_1        T2      RH_2        T3  \\\n",
       "0       -0.367676  3.301264 -1.118645  1.843821 -0.520411  1.073683 -1.235063   \n",
       "1       -0.367676  3.301264 -1.118645  1.616807 -0.520411  1.057097 -1.235063   \n",
       "2       -0.465215  3.301264 -1.118645  1.517959 -0.520411  1.033550 -1.235063   \n",
       "3       -0.465215  4.561378 -1.118645  1.459321 -0.520411  1.024540 -1.235063   \n",
       "4       -0.367676  4.561378 -1.118645  1.526336 -0.520411  1.009797 -1.235063   \n",
       "...           ...       ...       ...       ...       ...       ...       ...   \n",
       "19730    0.022483 -0.479080  2.415962  1.583299  2.530318  0.394449  2.458745   \n",
       "19731   -0.075056 -0.479080  2.374452  1.568221  2.468301  0.407788  2.425512   \n",
       "19732    1.680659  0.781035  2.374452  1.592514  2.411103  0.576982  2.383971   \n",
       "19733    3.143756  0.781035  2.374452  1.691361  2.313256  0.642694  2.304213   \n",
       "19734    3.241295  0.781035  2.374452  1.593351  2.244984  0.626828  2.270980   \n",
       "\n",
       "           RH_3        T4      RH_4  ...        T8      RH_8        T9  \\\n",
       "0      1.686130 -0.908217  1.506438  ... -1.957509  1.141572 -1.217324   \n",
       "1      1.704566 -0.908217  1.604528  ... -1.957509  1.134554 -1.200778   \n",
       "2      1.748608 -0.944115  1.580918  ... -1.957509  1.109032 -1.233869   \n",
       "3      1.769092 -0.962063  1.542526  ... -2.008631  1.082233 -1.233869   \n",
       "4      1.769092 -0.962063  1.497991  ... -2.008631  1.082233 -1.233869   \n",
       "...         ...       ...       ...  ...       ...       ...       ...   \n",
       "19730  0.590209  1.882026  1.511812  ...  1.365409  1.366295  1.843571   \n",
       "19731  0.608646  1.882026  1.511812  ...  1.365409  1.311932  1.843571   \n",
       "19732  0.752037  1.882026  1.544061  ...  1.365409  1.287048  1.843571   \n",
       "19733  0.629130  1.882026  1.557883  ...  1.346238  1.260011  1.843571   \n",
       "19734  0.588161  1.882026  1.597810  ...  1.383813  1.301596  1.843571   \n",
       "\n",
       "           RH_9     T_out  Press_mm_hg    RH_out  Windspeed  Visibility  \\\n",
       "0      0.958136 -0.152788    -2.976328  0.822080   1.207694    2.091596   \n",
       "1      0.965363 -0.175352    -2.962813  0.822080   1.071703    1.766584   \n",
       "2      0.950910 -0.196035    -2.949298  0.822080   0.935713    1.441572   \n",
       "3      0.926821 -0.218599    -2.935783  0.822080   0.799723    1.116559   \n",
       "4      0.926821 -0.241162    -2.922268  0.822080   0.663733    0.791547   \n",
       "...         ...       ...          ...       ...        ...         ...   \n",
       "19730  1.261649  2.874478    -0.043599 -1.616282  -0.288198   -1.243314   \n",
       "19731  1.261649  2.855675    -0.043599 -1.593912  -0.220203   -1.172659   \n",
       "19732  1.261649  2.836872    -0.043599 -1.571542  -0.152208   -1.102004   \n",
       "19733  1.268273  2.799266    -0.043599 -1.549171  -0.084213   -1.031349   \n",
       "19734  1.274897  2.780464    -0.043599 -1.526801  -0.016218   -0.960694   \n",
       "\n",
       "       Tdewpoint  \n",
       "0       0.366854  \n",
       "1       0.343017  \n",
       "2       0.319180  \n",
       "3       0.295343  \n",
       "4       0.271506  \n",
       "...          ...  \n",
       "19730   2.273822  \n",
       "19731   2.273822  \n",
       "19732   2.273822  \n",
       "19733   2.249985  \n",
       "19734   2.249985  \n",
       "\n",
       "[19735 rows x 26 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalizing the data before applying PCA which avoids the case that all our variance will be on only one component\n",
    "def normalize_data(data):\n",
    "    scaler = StandardScaler()  # initialize standardscaler\n",
    "    normalized_data = scaler.fit_transform(data)\n",
    "    df_normalized = pd.DataFrame(normalized_data, columns = df.columns) # convert df_normalized to dataframe \n",
    "    return df_normalized\n",
    "    \n",
    "df_normalized = normalize_data(df)\n",
    "df_normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "22886b1b-4d24-4c2f-9815-c8f46e6a4c42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "explained variance ratio is :\n",
      "[0.35879536 0.27166393 0.07196565]\n",
      "\n",
      "comulative_explained_variance is:\n",
      "[0.35879536 0.63045928 0.70242493]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# #Apply PCA for dimensionality reduction on our dataset and set number of componenets =3 as we are asked in task \n",
    "def reduce_dimension(data, n_components):\n",
    "    pca = PCA(n_components = 3)              # initialise pca with set components on 3\n",
    "    df_projected = pca.fit_transform(data)   #fit_transform pca on our data to get df_projected\n",
    "    \n",
    "    fig = plt.figure(figsize = (15, 6))\n",
    "    ax = fig.add_subplot(111, projection='3d')   # set up a 3d subplot for plotting\n",
    "    \n",
    "    \n",
    "    # visualize thee different components extracted from our dataset\n",
    "    component_1 = df_projected[:, 0]    \n",
    "    component_2 = df_projected[:, 1]\n",
    "    component_3 = df_projected[:, 2]\n",
    "    \n",
    "    # Plot the values\n",
    "    ax.scatter(component_1, component_2, component_3, c = component_2, marker = 'o') \n",
    "    ax.set_xlabel('X-axis')\n",
    "    ax.set_ylabel('Y-axis')\n",
    "    ax.set_zlabel('Z-axis')\n",
    "    ax.set_title('3D Scatter Plot for 3 Principal Components')\n",
    "    \n",
    "    plt.show()\n",
    "    explained_variance_ratio = pca.explained_variance_ratio_    # portion of the variance that lies along each principal component\n",
    "    print('explained variance ratio is :\\n{}\\n'.format(explained_variance_ratio))   # the extent to which each component explain the original dataset\n",
    "    comulative_explained_variance = explained_variance_ratio.cumsum()    #cumulative sum of explained variances for each component                          \n",
    "    print('comulative_explained_variance is:\\n{}\\n'.format(comulative_explained_variance))\n",
    "    \n",
    "    \n",
    "    \n",
    "df_projected_3 = reduce_dimension(df_normalized, 3)\n",
    "df_projected_3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09c9fab2-08be-4096-8b97-bd7d7107b49a",
   "metadata": {},
   "source": [
    "- When examining the cumulative explained variance for a model with three components, we observe that the first component accounts for approximately 35.9% of the total variance, the second around 27.1%. Together, these two components explain approximately 63.0% of the total variance. Adding the third component increases the cumulative explained variance to approximately 70.2%.\n",
    "- As we can see from the comulative explained variance, with three components we get %70.24 of the original variability in the data. However, depending\n",
    "on the specific application and the desired trade-off between dimensionality reduction and information retention, the number of components may vary."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a3cd4e6-fdf5-4357-a3b3-9bf3f8fada16",
   "metadata": {},
   "source": [
    "## Bar plot to show explained variance ratio described by three components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "09e2f681-f9b8-4f75-9afc-1c9ee456f5f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw bar chart for comulative explained variance with three components\n",
    "pca = PCA(n_components = 3)\n",
    "pca.fit(df_normalized)\n",
    "# Using a colormap\n",
    "colormap = plt.cm.viridis  # Choosing the 'viridis' colormap\n",
    "\n",
    "#Since colormap values are typically used to represent scalar data, normalization helps to map the range of values of the explained variance ratios to the full range of colors in the colormap.\n",
    "norm = plt.Normalize(vmin=min(pca.explained_variance_ratio_), vmax=max(pca.explained_variance_ratio_))\n",
    "colors = colormap(norm(pca.explained_variance_ratio_))\n",
    "# calculating comulative explained\n",
    "comulative_explained_variance = pca.explained_variance_ratio_.cumsum()\n",
    "plt.bar(range(1, 4), comulative_explained_variance, color = colors)\n",
    "# annotate each bar with its value\n",
    "for i, value in enumerate(comulative_explained_variance):  # add the values on the bars\n",
    "    plt.text(i+1, value, s = f'{value:.2f}', ha='center', va='bottom')\n",
    "    \n",
    "plt.xlabel('number of components')\n",
    "plt.ylabel(' comulative explained variance ratio')\n",
    "plt.xticks([1, 2, 3, 4])\n",
    "plt.title('explained variance ratio result for 3 components')\n",
    "plt.show()                      "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6b4fd80-8bf3-4ade-84ff-43fa6ae912cd",
   "metadata": {},
   "source": [
    "- above graph shows bar plot for comulative explained variance ratio for n_componenets = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57094e12-309d-4f6a-b813-491b81528461",
   "metadata": {},
   "source": [
    "## Apply PCA on normalized dataset with "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79eba57b-4106-48e9-804a-6a3952e9e3f9",
   "metadata": {},
   "source": [
    "- We also decided on drawing plot for both individual variance ratio and comulative variance ratio, for n_components = 5, to check if adding more components help to explain more variance from data or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a7ebc745-b2ab-4d48-9476-a244e811b1fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "projected_df is :\n",
      "\n",
      "[[-2.92782906 -4.68959975  1.57251556  1.96344397 -0.3676376 ]\n",
      " [-2.94388937 -4.61803228  1.50261845  1.96411377 -0.4925733 ]\n",
      " [-2.98196892 -4.53272708  1.41869256  1.92098268 -0.62885552]\n",
      " ...\n",
      " [ 7.86997229 -2.70674434  1.41359946  0.97429933  0.61963507]\n",
      " [ 7.77405302 -2.6896907   1.70288394  1.77127556  0.68421469]\n",
      " [ 7.67316928 -2.64741559  1.65551214  1.84379762  0.67698875]]\n",
      "\n",
      "explained variance ratio is :\n",
      "\n",
      "[0.35879536 0.27166393 0.07196565 0.04876284 0.04022135]\n",
      "\n",
      "comulative_explained_variance is:\n",
      "\n",
      "[0.35879536 0.63045928 0.70242493 0.75118777 0.79140912]\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Apply PCA for dimentionality reduction on our dataset and set components=5 \n",
    "def explained_variance_ratio(data):\n",
    "    \n",
    "    pca = PCA(n_components = 5)                                          # initialise PCA and set components on 7\n",
    "    projected_df = pca.fit_transform(data)                               # fit and then transform original dataset into a newdataset\n",
    "    print('projected_df is :\\n\\n{}\\n'.format(projected_df))\n",
    "    \n",
    "    explained_variance_ratio = pca.explained_variance_ratio_              # portion of the variance that lies along each principal component\n",
    "    print('explained variance ratio is :\\n\\n{}\\n'.format(explained_variance_ratio))   # the extent to which each component explain the original dataset\n",
    "                                  \n",
    "                                                         \n",
    "    comulative_explained_variance = explained_variance_ratio.cumsum()                  #cumulative sum of explained variances for each component                          \n",
    "    print('comulative_explained_variance is:\\n\\n{}\\n'.format(comulative_explained_variance))\n",
    "    principal_components = np.arange(1, len(explained_variance_ratio) + 1) \n",
    "    \n",
    "    # individual component variance\n",
    "    plt.bar(range(1, len(principal_components) + 1), explained_variance_ratio, color = 'orange', label = 'Individual Component Variance')\n",
    "    # Text labels for individual explained variance (adjusted positions)\n",
    "    for i, value in enumerate(explained_variance_ratio):\n",
    "        if i > 0:\n",
    "            bar_width = 0.5\n",
    "            x_pos = principal_components[i] - bar_width/4                # Adjust x-position for better placement\n",
    "            y_pos = value + 0.02                                         # Adjust y-position to avoid overlapping bars\n",
    "            plt.text(x_pos, y_pos, str(round(value, 2)), ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "    \n",
    "    plt.title('individual variance explained ration')\n",
    "\n",
    "    # Cumulative explained variance\n",
    "    plt.plot(np.arange(1, len(principal_components) + 1), comulative_explained_variance, marker = 'o', label='Cumulative Explained Variance', color = 'blue')\n",
    "    for i, value in enumerate(comulative_explained_variance):\n",
    "      plt.annotate(str(round(value, 2)), (principal_components[i], value + 0.01), ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "    \n",
    "    plt.xlabel('Principal Components')\n",
    "    plt.ylabel('Explained Variance')\n",
    "    plt.title('Explained Variance by Different Principal Components')\n",
    "    plt.xticks(np.arange(1, 8))\n",
    "    plt.yticks(np.arange(0, 1.1, 0.1))\n",
    "    plt.grid(True, linestyle='--', linewidth=0.5)\n",
    "    plt.legend(bbox_to_anchor=(0.5, 0.5), loc='lower left', fontsize=10)\n",
    "\n",
    "explained_variance_ratio(df_normalized)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8344ac81-910f-4192-9798-cc3512d8cc69",
   "metadata": {},
   "source": [
    "- As we can see from the above graph, with **3 componenets**, we already capture approximately **70%** of the total variance.\n",
    "- By adding the 4th and 5th components, the cumulative explained variance increases but at a diminishing rate. This means that the additional components\n",
    "provide less explanatory power compared to the first few components.\n",
    "- Therefore, considering the trade-off between capturing variance and the complexity of the model, choosing 3 components seems justified as it captures\n",
    "a significant portion of the total variance while keeping the model relatively simple\n",
    "- As we can see from the bar plot of individual explained variance ratio, the elbow point is on n_componenets = 3. from this point explained variance decrease in a very diminishing rate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fc2853c-624e-46fa-acf2-92219bea7d4f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
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