Principal Component Analysis2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "193b3937-5108-46b9-a870-63ed7fd9e4cb",
   "metadata": {},
   "source": [
    "# NBA Players Stats 23/24"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05c8a288-f03a-4c87-80aa-26314907cec1",
   "metadata": {},
   "source": [
    "### Importing necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2d70f8d9-6ea3-49ac-900a-d3ff08da67cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print('Done.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f639a69-fec7-4032-bf76-fac36537bc35",
   "metadata": {},
   "source": [
    "### Loading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9da69fcf-0408-4fcf-8fcf-bc5c02f8e0f6",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>Player</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>ORB</th>\n",
       "      <th>DRB</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Precious Achiuwa</td>\n",
       "      <td>PF-C</td>\n",
       "      <td>24</td>\n",
       "      <td>TOT</td>\n",
       "      <td>74</td>\n",
       "      <td>18</td>\n",
       "      <td>21.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>...</td>\n",
       "      <td>.616</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Precious Achiuwa</td>\n",
       "      <td>C</td>\n",
       "      <td>24</td>\n",
       "      <td>TOR</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>3.1</td>\n",
       "      <td>6.8</td>\n",
       "      <td>...</td>\n",
       "      <td>.571</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Precious Achiuwa</td>\n",
       "      <td>PF</td>\n",
       "      <td>24</td>\n",
       "      <td>NYK</td>\n",
       "      <td>49</td>\n",
       "      <td>18</td>\n",
       "      <td>24.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.1</td>\n",
       "      <td>...</td>\n",
       "      <td>.643</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Bam Adebayo</td>\n",
       "      <td>C</td>\n",
       "      <td>26</td>\n",
       "      <td>MIA</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>14.3</td>\n",
       "      <td>...</td>\n",
       "      <td>.755</td>\n",
       "      <td>2.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>10.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>19.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Ochai Agbaji</td>\n",
       "      <td>SG</td>\n",
       "      <td>23</td>\n",
       "      <td>TOT</td>\n",
       "      <td>78</td>\n",
       "      <td>28</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>5.6</td>\n",
       "      <td>...</td>\n",
       "      <td>.661</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Rk            Player   Pos Age   Tm   G  GS    MP   FG   FGA  ...   FT%  \\\n",
       "0  1  Precious Achiuwa  PF-C  24  TOT  74  18  21.9  3.2   6.3  ...  .616   \n",
       "1  1  Precious Achiuwa     C  24  TOR  25   0  17.5  3.1   6.8  ...  .571   \n",
       "2  1  Precious Achiuwa    PF  24  NYK  49  18  24.2  3.2   6.1  ...  .643   \n",
       "3  2       Bam Adebayo     C  26  MIA  71  71  34.0  7.5  14.3  ...  .755   \n",
       "4  3      Ochai Agbaji    SG  23  TOT  78  28  21.0  2.3   5.6  ...  .661   \n",
       "\n",
       "   ORB  DRB   TRB  AST  STL  BLK  TOV   PF   PTS  \n",
       "0  2.6  4.0   6.6  1.3  0.6  0.9  1.1  1.9   7.6  \n",
       "1  2.0  3.4   5.4  1.8  0.6  0.5  1.2  1.6   7.7  \n",
       "2  2.9  4.3   7.2  1.1  0.6  1.1  1.1  2.1   7.6  \n",
       "3  2.2  8.1  10.4  3.9  1.1  0.9  2.3  2.2  19.3  \n",
       "4  0.9  1.8   2.8  1.1  0.6  0.6  0.8  1.5   5.8  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba_stats_data = pd.read_csv('/home/bree/Documents/nba-player-data.csv')\n",
    "\n",
    "nba_stats_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "666455f8-70bb-4df3-89a1-65060962ed3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(736, 30)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba_stats_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "94ffab55-8731-40d7-ba82-d3f7c0f256dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rk         0\n",
       "Player     0\n",
       "Pos        0\n",
       "Age        0\n",
       "Tm         0\n",
       "G          0\n",
       "GS         0\n",
       "MP         0\n",
       "FG         0\n",
       "FGA        0\n",
       "FG%        8\n",
       "3P         0\n",
       "3PA        0\n",
       "3P%       46\n",
       "2P         0\n",
       "2PA        0\n",
       "2P%       13\n",
       "eFG%       8\n",
       "FT         0\n",
       "FTA        0\n",
       "FT%       59\n",
       "ORB        0\n",
       "DRB        0\n",
       "TRB        0\n",
       "AST        0\n",
       "STL        0\n",
       "BLK        0\n",
       "TOV        0\n",
       "PF         0\n",
       "PTS        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba_stats_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2ef8715f-9685-485f-931a-2784eb661506",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Rk', 'Player', 'Pos', 'Age', 'Tm', 'G', 'GS', 'MP', 'FG', 'FGA', 'FG%',\n",
       "       '3P', '3PA', '3P%', '2P', '2PA', '2P%', 'eFG%', 'FT', 'FTA', 'FT%',\n",
       "       'ORB', 'DRB', 'TRB', 'AST', 'STL', 'BLK', 'TOV', 'PF', 'PTS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba_stats_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "30424684-c10c-460c-a0d1-00fc2f8af5c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "columns = ['3P%', '2P%', 'FT%', 'eFG%','FG%']\n",
    "\n",
    "for column in columns:\n",
    "    nba_stats_data[column] = pd.to_numeric(nba_stats_data[column], errors='coerce')\n",
    "\n",
    "for column in columns:\n",
    "    nba_stats_data[column].fillna(nba_stats_data[column].mean())\n",
    "\n",
    "print('Done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e4e1ab7d-51a5-4857-b8f4-176107dc6c80",
   "metadata": {},
   "outputs": [
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>736 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Rk  Player    Pos    Age     Tm      G     GS     MP     FG    FGA  \\\n",
       "0    False   False  False  False  False  False  False  False  False  False   \n",
       "1    False   False  False  False  False  False  False  False  False  False   \n",
       "2    False   False  False  False  False  False  False  False  False  False   \n",
       "3    False   False  False  False  False  False  False  False  False  False   \n",
       "4    False   False  False  False  False  False  False  False  False  False   \n",
       "..     ...     ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "731  False   False  False  False  False  False  False  False  False  False   \n",
       "732  False   False  False  False  False  False  False  False  False  False   \n",
       "733  False   False  False  False  False  False  False  False  False  False   \n",
       "734  False   False  False  False  False  False  False  False  False  False   \n",
       "735  False   False  False  False  False  False  False  False  False  False   \n",
       "\n",
       "     ...    FT%    ORB    DRB    TRB    AST    STL    BLK    TOV     PF    PTS  \n",
       "0    ...  False  False  False  False  False  False  False  False  False  False  \n",
       "1    ...  False  False  False  False  False  False  False  False  False  False  \n",
       "2    ...  False  False  False  False  False  False  False  False  False  False  \n",
       "3    ...  False  False  False  False  False  False  False  False  False  False  \n",
       "4    ...  False  False  False  False  False  False  False  False  False  False  \n",
       "..   ...    ...    ...    ...    ...    ...    ...    ...    ...    ...    ...  \n",
       "731  ...  False  False  False  False  False  False  False  False  False  False  \n",
       "732  ...  False  False  False  False  False  False  False  False  False  False  \n",
       "733  ...  False  False  False  False  False  False  False  False  False  False  \n",
       "734  ...  False  False  False  False  False  False  False  False  False  False  \n",
       "735  ...  False  False  False  False  False  False  False  False  False  False  \n",
       "\n",
       "[736 rows x 30 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba_stats_data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d81641e0-951c-484f-84ae-e629ba8059c3",
   "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>FG%</th>\n",
       "      <th>3P%</th>\n",
       "      <th>2P%</th>\n",
       "      <th>eFG%</th>\n",
       "      <th>FT%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>727.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>722.000000</td>\n",
       "      <td>727.000000</td>\n",
       "      <td>676.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.447558</td>\n",
       "      <td>0.316925</td>\n",
       "      <td>0.519686</td>\n",
       "      <td>0.515692</td>\n",
       "      <td>0.74967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.110701</td>\n",
       "      <td>0.137217</td>\n",
       "      <td>0.136813</td>\n",
       "      <td>0.114737</td>\n",
       "      <td>0.16003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.478250</td>\n",
       "      <td>0.483000</td>\n",
       "      <td>0.68550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.446000</td>\n",
       "      <td>0.346000</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.529000</td>\n",
       "      <td>0.77700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>0.581000</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>0.84025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.917000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              FG%         3P%         2P%        eFG%        FT%\n",
       "count  727.000000  689.000000  722.000000  727.000000  676.00000\n",
       "mean     0.447558    0.316925    0.519686    0.515692    0.74967\n",
       "std      0.110701    0.137217    0.136813    0.114737    0.16003\n",
       "min      0.000000    0.000000    0.000000    0.000000    0.00000\n",
       "25%      0.400000    0.280000    0.478250    0.483000    0.68550\n",
       "50%      0.446000    0.346000    0.530000    0.529000    0.77700\n",
       "75%      0.500000    0.385000    0.581000    0.575000    0.84025\n",
       "max      0.800000    1.000000    1.000000    0.917000    1.00000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba_stats_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "274a921a-5953-4661-b115-fc3754cf4189",
   "metadata": {},
   "source": [
    "### Selecting the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b6f75bac-fbe9-44df-98b7-a42da55b6f3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['FG%', '3P%', '2P%', 'eFG%', 'FT%'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    " # Select only numeric columns\n",
    "numeric_columns = nba_stats_data.select_dtypes(include=['float64', 'int64']).columns\n",
    "print(numeric_columns)\n",
    "\n",
    "# Create X with only numeric columns\n",
    "X = nba_stats_data[numeric_columns]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23a79cf6-4170-40b7-aa31-dbb7d2dcd80c",
   "metadata": {},
   "source": [
    "### Standardize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3f04c071-46f4-40ff-862e-8857c71c16b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply the StandardScaler to the numeric data\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5c49a892-251b-4fdb-9ed7-0ccc30ec9022",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in scaled data: 139\n"
     ]
    }
   ],
   "source": [
    "# Check if any missing values exist in the scaled data\n",
    "print(f\"Missing values in scaled data: {np.isnan(X_scaled).sum()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4db75ace-cac9-4bec-8ec5-b62271ad472e",
   "metadata": {},
   "source": [
    "### Impute missing values by filling them with the mean of the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "02006b78-2595-4c26-870c-bb8293de0274",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "imputer = SimpleImputer(strategy='mean')\n",
    "X_scaled_imputed = imputer.fit_transform(X_scaled)\n",
    "\n",
    "np.isnan(X_scaled_imputed).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed95962a-0b4f-4628-945e-eed75b02e2de",
   "metadata": {},
   "source": [
    "### Apply PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a1bad662-a5c4-4944-8860-cf68935ed06b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCA(n_components=0.99)\n",
      "Explained Variance: [0.53228346 0.24077864 0.16247285 0.05351329 0.01095175]\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(0.99)\n",
    "X_pca = pca.fit_transform(X_scaled_imputed)\n",
    "\n",
    "\n",
    "explained_variance = pca.explained_variance_ratio_ \n",
    "print(f'Explained Variance: {explained_variance}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "11ac808a-e2f3-440e-8e42-be9b7fdc0811",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cumulative_variance = np.cumsum(explained_variance)\n",
    "\n",
    "plt.plot(range(1, len(cumulative_variance) + 1), cumulative_variance)\n",
    "plt.xlabel('Number of Principal Components')\n",
    "plt.ylabel('Cumulative Explained Variance')\n",
    "plt.title('Scree Plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "864ed646-1146-4087-963a-c7636c1e174c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot explained variance\n",
    "plt.bar(range(1, len(explained_variance) + 1), explained_variance)\n",
    "plt.xlabel('Principal Component')\n",
    "plt.ylabel('Explained Variance')\n",
    "plt.title('Explained Variance by Principal Components')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b842b26e-e328-4b93-a723-5f373067cfd3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}