04_Experiment-PCA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a73d58a3",
   "metadata": {},
   "source": [
    "**Description :** so in this experiment actually i want to apply the Principal Component Analysis in the Tennesse Eastman Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5f8e0c8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyreadr\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6cb50a45",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = './data/'\n",
    "\n",
    "train_normal_path = 'TEP_FaultFree_Training.RData'\n",
    "\n",
    "train_normal_complete = pyreadr.read_r(path + train_normal_path)['fault_free_training']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1353b95e",
   "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>faultNumber</th>\n",
       "      <th>simulationRun</th>\n",
       "      <th>sample</th>\n",
       "      <th>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>9.2320</td>\n",
       "      <td>26.889</td>\n",
       "      <td>42.402</td>\n",
       "      <td>2704.3</td>\n",
       "      <td>...</td>\n",
       "      <td>53.744</td>\n",
       "      <td>24.657</td>\n",
       "      <td>62.544</td>\n",
       "      <td>22.137</td>\n",
       "      <td>39.935</td>\n",
       "      <td>42.323</td>\n",
       "      <td>47.757</td>\n",
       "      <td>47.510</td>\n",
       "      <td>41.258</td>\n",
       "      <td>18.447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.25109</td>\n",
       "      <td>3659.4</td>\n",
       "      <td>4556.6</td>\n",
       "      <td>9.4264</td>\n",
       "      <td>26.721</td>\n",
       "      <td>42.576</td>\n",
       "      <td>2705.0</td>\n",
       "      <td>...</td>\n",
       "      <td>53.414</td>\n",
       "      <td>24.588</td>\n",
       "      <td>59.259</td>\n",
       "      <td>22.084</td>\n",
       "      <td>40.176</td>\n",
       "      <td>38.554</td>\n",
       "      <td>43.692</td>\n",
       "      <td>47.427</td>\n",
       "      <td>41.359</td>\n",
       "      <td>17.194</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.25038</td>\n",
       "      <td>3660.3</td>\n",
       "      <td>4477.8</td>\n",
       "      <td>9.4426</td>\n",
       "      <td>26.875</td>\n",
       "      <td>42.070</td>\n",
       "      <td>2706.2</td>\n",
       "      <td>...</td>\n",
       "      <td>54.357</td>\n",
       "      <td>24.666</td>\n",
       "      <td>61.275</td>\n",
       "      <td>22.380</td>\n",
       "      <td>40.244</td>\n",
       "      <td>38.990</td>\n",
       "      <td>46.699</td>\n",
       "      <td>47.468</td>\n",
       "      <td>41.199</td>\n",
       "      <td>20.530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.24977</td>\n",
       "      <td>3661.3</td>\n",
       "      <td>4512.1</td>\n",
       "      <td>9.4776</td>\n",
       "      <td>26.758</td>\n",
       "      <td>42.063</td>\n",
       "      <td>2707.2</td>\n",
       "      <td>...</td>\n",
       "      <td>53.946</td>\n",
       "      <td>24.725</td>\n",
       "      <td>59.856</td>\n",
       "      <td>22.277</td>\n",
       "      <td>40.257</td>\n",
       "      <td>38.072</td>\n",
       "      <td>47.541</td>\n",
       "      <td>47.658</td>\n",
       "      <td>41.643</td>\n",
       "      <td>18.089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.29405</td>\n",
       "      <td>3679.0</td>\n",
       "      <td>4497.0</td>\n",
       "      <td>9.3381</td>\n",
       "      <td>26.889</td>\n",
       "      <td>42.650</td>\n",
       "      <td>2705.1</td>\n",
       "      <td>...</td>\n",
       "      <td>53.658</td>\n",
       "      <td>28.797</td>\n",
       "      <td>60.717</td>\n",
       "      <td>21.947</td>\n",
       "      <td>39.144</td>\n",
       "      <td>41.955</td>\n",
       "      <td>47.645</td>\n",
       "      <td>47.346</td>\n",
       "      <td>41.507</td>\n",
       "      <td>18.461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   faultNumber  simulationRun  sample  xmeas_1  xmeas_2  xmeas_3  xmeas_4  \\\n",
       "0          0.0            1.0       1  0.25038   3674.0   4529.0   9.2320   \n",
       "1          0.0            1.0       2  0.25109   3659.4   4556.6   9.4264   \n",
       "2          0.0            1.0       3  0.25038   3660.3   4477.8   9.4426   \n",
       "3          0.0            1.0       4  0.24977   3661.3   4512.1   9.4776   \n",
       "4          0.0            1.0       5  0.29405   3679.0   4497.0   9.3381   \n",
       "\n",
       "   xmeas_5  xmeas_6  xmeas_7  ...   xmv_2   xmv_3   xmv_4   xmv_5   xmv_6  \\\n",
       "0   26.889   42.402   2704.3  ...  53.744  24.657  62.544  22.137  39.935   \n",
       "1   26.721   42.576   2705.0  ...  53.414  24.588  59.259  22.084  40.176   \n",
       "2   26.875   42.070   2706.2  ...  54.357  24.666  61.275  22.380  40.244   \n",
       "3   26.758   42.063   2707.2  ...  53.946  24.725  59.856  22.277  40.257   \n",
       "4   26.889   42.650   2705.1  ...  53.658  28.797  60.717  21.947  39.144   \n",
       "\n",
       "    xmv_7   xmv_8   xmv_9  xmv_10  xmv_11  \n",
       "0  42.323  47.757  47.510  41.258  18.447  \n",
       "1  38.554  43.692  47.427  41.359  17.194  \n",
       "2  38.990  46.699  47.468  41.199  20.530  \n",
       "3  38.072  47.541  47.658  41.643  18.089  \n",
       "4  41.955  47.645  47.346  41.507  18.461  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_normal_complete.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "915ab35a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the first column index\n",
    "df_train = train_normal_complete[train_normal_complete.simulationRun==1].iloc[:,3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "52ef814a",
   "metadata": {},
   "outputs": [
    {
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       "      <th>xmeas_1</th>\n",
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       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
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       "      <td>42.402</td>\n",
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       "      <td>74.863</td>\n",
       "      <td>120.41</td>\n",
       "      <td>0.33818</td>\n",
       "      <td>...</td>\n",
       "      <td>53.744</td>\n",
       "      <td>24.657</td>\n",
       "      <td>62.544</td>\n",
       "      <td>22.137</td>\n",
       "      <td>39.935</td>\n",
       "      <td>42.323</td>\n",
       "      <td>47.757</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.25109</td>\n",
       "      <td>3659.4</td>\n",
       "      <td>4556.6</td>\n",
       "      <td>9.4264</td>\n",
       "      <td>26.721</td>\n",
       "      <td>42.576</td>\n",
       "      <td>2705.0</td>\n",
       "      <td>75.000</td>\n",
       "      <td>120.41</td>\n",
       "      <td>0.33620</td>\n",
       "      <td>...</td>\n",
       "      <td>53.414</td>\n",
       "      <td>24.588</td>\n",
       "      <td>59.259</td>\n",
       "      <td>22.084</td>\n",
       "      <td>40.176</td>\n",
       "      <td>38.554</td>\n",
       "      <td>43.692</td>\n",
       "      <td>47.427</td>\n",
       "      <td>41.359</td>\n",
       "      <td>17.194</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.25038</td>\n",
       "      <td>3660.3</td>\n",
       "      <td>4477.8</td>\n",
       "      <td>9.4426</td>\n",
       "      <td>26.875</td>\n",
       "      <td>42.070</td>\n",
       "      <td>2706.2</td>\n",
       "      <td>74.771</td>\n",
       "      <td>120.42</td>\n",
       "      <td>0.33563</td>\n",
       "      <td>...</td>\n",
       "      <td>54.357</td>\n",
       "      <td>24.666</td>\n",
       "      <td>61.275</td>\n",
       "      <td>22.380</td>\n",
       "      <td>40.244</td>\n",
       "      <td>38.990</td>\n",
       "      <td>46.699</td>\n",
       "      <td>47.468</td>\n",
       "      <td>41.199</td>\n",
       "      <td>20.530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.24977</td>\n",
       "      <td>3661.3</td>\n",
       "      <td>4512.1</td>\n",
       "      <td>9.4776</td>\n",
       "      <td>26.758</td>\n",
       "      <td>42.063</td>\n",
       "      <td>2707.2</td>\n",
       "      <td>75.224</td>\n",
       "      <td>120.39</td>\n",
       "      <td>0.33553</td>\n",
       "      <td>...</td>\n",
       "      <td>53.946</td>\n",
       "      <td>24.725</td>\n",
       "      <td>59.856</td>\n",
       "      <td>22.277</td>\n",
       "      <td>40.257</td>\n",
       "      <td>38.072</td>\n",
       "      <td>47.541</td>\n",
       "      <td>47.658</td>\n",
       "      <td>41.643</td>\n",
       "      <td>18.089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.29405</td>\n",
       "      <td>3679.0</td>\n",
       "      <td>4497.0</td>\n",
       "      <td>9.3381</td>\n",
       "      <td>26.889</td>\n",
       "      <td>42.650</td>\n",
       "      <td>2705.1</td>\n",
       "      <td>75.388</td>\n",
       "      <td>120.39</td>\n",
       "      <td>0.32632</td>\n",
       "      <td>...</td>\n",
       "      <td>53.658</td>\n",
       "      <td>28.797</td>\n",
       "      <td>60.717</td>\n",
       "      <td>21.947</td>\n",
       "      <td>39.144</td>\n",
       "      <td>41.955</td>\n",
       "      <td>47.645</td>\n",
       "      <td>47.346</td>\n",
       "      <td>41.507</td>\n",
       "      <td>18.461</td>\n",
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      ],
      "text/plain": [
       "   xmeas_1  xmeas_2  xmeas_3  xmeas_4  xmeas_5  xmeas_6  xmeas_7  xmeas_8  \\\n",
       "0  0.25038   3674.0   4529.0   9.2320   26.889   42.402   2704.3   74.863   \n",
       "1  0.25109   3659.4   4556.6   9.4264   26.721   42.576   2705.0   75.000   \n",
       "2  0.25038   3660.3   4477.8   9.4426   26.875   42.070   2706.2   74.771   \n",
       "3  0.24977   3661.3   4512.1   9.4776   26.758   42.063   2707.2   75.224   \n",
       "4  0.29405   3679.0   4497.0   9.3381   26.889   42.650   2705.1   75.388   \n",
       "\n",
       "   xmeas_9  xmeas_10  ...   xmv_2   xmv_3   xmv_4   xmv_5   xmv_6   xmv_7  \\\n",
       "0   120.41   0.33818  ...  53.744  24.657  62.544  22.137  39.935  42.323   \n",
       "1   120.41   0.33620  ...  53.414  24.588  59.259  22.084  40.176  38.554   \n",
       "2   120.42   0.33563  ...  54.357  24.666  61.275  22.380  40.244  38.990   \n",
       "3   120.39   0.33553  ...  53.946  24.725  59.856  22.277  40.257  38.072   \n",
       "4   120.39   0.32632  ...  53.658  28.797  60.717  21.947  39.144  41.955   \n",
       "\n",
       "    xmv_8   xmv_9  xmv_10  xmv_11  \n",
       "0  47.757  47.510  41.258  18.447  \n",
       "1  43.692  47.427  41.359  17.194  \n",
       "2  46.699  47.468  41.199  20.530  \n",
       "3  47.541  47.658  41.643  18.089  \n",
       "4  47.645  47.346  41.507  18.461  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "458ec6a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 500 entries, 0 to 499\n",
      "Data columns (total 52 columns):\n",
      "xmeas_1     500 non-null float64\n",
      "xmeas_2     500 non-null float64\n",
      "xmeas_3     500 non-null float64\n",
      "xmeas_4     500 non-null float64\n",
      "xmeas_5     500 non-null float64\n",
      "xmeas_6     500 non-null float64\n",
      "xmeas_7     500 non-null float64\n",
      "xmeas_8     500 non-null float64\n",
      "xmeas_9     500 non-null float64\n",
      "xmeas_10    500 non-null float64\n",
      "xmeas_11    500 non-null float64\n",
      "xmeas_12    500 non-null float64\n",
      "xmeas_13    500 non-null float64\n",
      "xmeas_14    500 non-null float64\n",
      "xmeas_15    500 non-null float64\n",
      "xmeas_16    500 non-null float64\n",
      "xmeas_17    500 non-null float64\n",
      "xmeas_18    500 non-null float64\n",
      "xmeas_19    500 non-null float64\n",
      "xmeas_20    500 non-null float64\n",
      "xmeas_21    500 non-null float64\n",
      "xmeas_22    500 non-null float64\n",
      "xmeas_23    500 non-null float64\n",
      "xmeas_24    500 non-null float64\n",
      "xmeas_25    500 non-null float64\n",
      "xmeas_26    500 non-null float64\n",
      "xmeas_27    500 non-null float64\n",
      "xmeas_28    500 non-null float64\n",
      "xmeas_29    500 non-null float64\n",
      "xmeas_30    500 non-null float64\n",
      "xmeas_31    500 non-null float64\n",
      "xmeas_32    500 non-null float64\n",
      "xmeas_33    500 non-null float64\n",
      "xmeas_34    500 non-null float64\n",
      "xmeas_35    500 non-null float64\n",
      "xmeas_36    500 non-null float64\n",
      "xmeas_37    500 non-null float64\n",
      "xmeas_38    500 non-null float64\n",
      "xmeas_39    500 non-null float64\n",
      "xmeas_40    500 non-null float64\n",
      "xmeas_41    500 non-null float64\n",
      "xmv_1       500 non-null float64\n",
      "xmv_2       500 non-null float64\n",
      "xmv_3       500 non-null float64\n",
      "xmv_4       500 non-null float64\n",
      "xmv_5       500 non-null float64\n",
      "xmv_6       500 non-null float64\n",
      "xmv_7       500 non-null float64\n",
      "xmv_8       500 non-null float64\n",
      "xmv_9       500 non-null float64\n",
      "xmv_10      500 non-null float64\n",
      "xmv_11      500 non-null float64\n",
      "dtypes: float64(52)\n",
      "memory usage: 207.0 KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0df69c05",
   "metadata": {},
   "source": [
    "## Scale the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7bbb6402",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling the data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "x = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1f247d7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x.fit_transform(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0e402944",
   "metadata": {},
   "outputs": [],
   "source": [
    "# put the data into pandas dataframe\n",
    "x = pd.DataFrame(x, columns=df_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e893c25c",
   "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>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.007459</td>\n",
       "      <td>0.343859</td>\n",
       "      <td>0.502126</td>\n",
       "      <td>-1.431359</td>\n",
       "      <td>-0.084087</td>\n",
       "      <td>0.278542</td>\n",
       "      <td>-0.009557</td>\n",
       "      <td>-0.292773</td>\n",
       "      <td>0.510938</td>\n",
       "      <td>0.098431</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.488936</td>\n",
       "      <td>0.000962</td>\n",
       "      <td>1.050090</td>\n",
       "      <td>-0.051818</td>\n",
       "      <td>-0.122736</td>\n",
       "      <td>1.319983</td>\n",
       "      <td>0.471559</td>\n",
       "      <td>-0.720571</td>\n",
       "      <td>0.279630</td>\n",
       "      <td>0.250397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.017875</td>\n",
       "      <td>-0.103316</td>\n",
       "      <td>1.215653</td>\n",
       "      <td>1.035368</td>\n",
       "      <td>-0.866377</td>\n",
       "      <td>1.068875</td>\n",
       "      <td>0.107804</td>\n",
       "      <td>-0.032734</td>\n",
       "      <td>0.510938</td>\n",
       "      <td>-0.071378</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.237481</td>\n",
       "      <td>-0.024152</td>\n",
       "      <td>-1.590601</td>\n",
       "      <td>-0.185906</td>\n",
       "      <td>0.049624</td>\n",
       "      <td>0.089123</td>\n",
       "      <td>-1.227605</td>\n",
       "      <td>-0.760505</td>\n",
       "      <td>0.460598</td>\n",
       "      <td>-0.634866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.007459</td>\n",
       "      <td>-0.075750</td>\n",
       "      <td>-0.821517</td>\n",
       "      <td>1.240929</td>\n",
       "      <td>-0.149278</td>\n",
       "      <td>-1.229449</td>\n",
       "      <td>0.308995</td>\n",
       "      <td>-0.467397</td>\n",
       "      <td>1.023929</td>\n",
       "      <td>-0.120262</td>\n",
       "      <td>...</td>\n",
       "      <td>0.901543</td>\n",
       "      <td>0.004238</td>\n",
       "      <td>0.029987</td>\n",
       "      <td>0.562959</td>\n",
       "      <td>0.098256</td>\n",
       "      <td>0.231510</td>\n",
       "      <td>0.029317</td>\n",
       "      <td>-0.740779</td>\n",
       "      <td>0.173916</td>\n",
       "      <td>1.722067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.029225</td>\n",
       "      <td>-0.045122</td>\n",
       "      <td>0.065220</td>\n",
       "      <td>1.685042</td>\n",
       "      <td>-0.694087</td>\n",
       "      <td>-1.261244</td>\n",
       "      <td>0.476653</td>\n",
       "      <td>0.392438</td>\n",
       "      <td>-0.515042</td>\n",
       "      <td>-0.128838</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.030736</td>\n",
       "      <td>0.025713</td>\n",
       "      <td>-1.110695</td>\n",
       "      <td>0.302375</td>\n",
       "      <td>0.107554</td>\n",
       "      <td>-0.068286</td>\n",
       "      <td>0.381272</td>\n",
       "      <td>-0.649365</td>\n",
       "      <td>0.969458</td>\n",
       "      <td>-0.002535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.550795</td>\n",
       "      <td>0.497001</td>\n",
       "      <td>-0.325151</td>\n",
       "      <td>-0.085064</td>\n",
       "      <td>-0.084087</td>\n",
       "      <td>1.404994</td>\n",
       "      <td>0.124570</td>\n",
       "      <td>0.703725</td>\n",
       "      <td>-0.515042</td>\n",
       "      <td>-0.918704</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.684011</td>\n",
       "      <td>1.507821</td>\n",
       "      <td>-0.418568</td>\n",
       "      <td>-0.532509</td>\n",
       "      <td>-0.688446</td>\n",
       "      <td>1.199804</td>\n",
       "      <td>0.424744</td>\n",
       "      <td>-0.799476</td>\n",
       "      <td>0.725778</td>\n",
       "      <td>0.260288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6   xmeas_7  \\\n",
       "0 -0.007459  0.343859  0.502126 -1.431359 -0.084087  0.278542 -0.009557   \n",
       "1  0.017875 -0.103316  1.215653  1.035368 -0.866377  1.068875  0.107804   \n",
       "2 -0.007459 -0.075750 -0.821517  1.240929 -0.149278 -1.229449  0.308995   \n",
       "3 -0.029225 -0.045122  0.065220  1.685042 -0.694087 -1.261244  0.476653   \n",
       "4  1.550795  0.497001 -0.325151 -0.085064 -0.084087  1.404994  0.124570   \n",
       "\n",
       "    xmeas_8   xmeas_9  xmeas_10  ...     xmv_2     xmv_3     xmv_4     xmv_5  \\\n",
       "0 -0.292773  0.510938  0.098431  ... -0.488936  0.000962  1.050090 -0.051818   \n",
       "1 -0.032734  0.510938 -0.071378  ... -1.237481 -0.024152 -1.590601 -0.185906   \n",
       "2 -0.467397  1.023929 -0.120262  ...  0.901543  0.004238  0.029987  0.562959   \n",
       "3  0.392438 -0.515042 -0.128838  ... -0.030736  0.025713 -1.110695  0.302375   \n",
       "4  0.703725 -0.515042 -0.918704  ... -0.684011  1.507821 -0.418568 -0.532509   \n",
       "\n",
       "      xmv_6     xmv_7     xmv_8     xmv_9    xmv_10    xmv_11  \n",
       "0 -0.122736  1.319983  0.471559 -0.720571  0.279630  0.250397  \n",
       "1  0.049624  0.089123 -1.227605 -0.760505  0.460598 -0.634866  \n",
       "2  0.098256  0.231510  0.029317 -0.740779  0.173916  1.722067  \n",
       "3  0.107554 -0.068286  0.381272 -0.649365  0.969458 -0.002535  \n",
       "4 -0.688446  1.199804  0.424744 -0.799476  0.725778  0.260288  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3af6be74",
   "metadata": {},
   "source": [
    "## Make PCA Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4c55ac4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca10 = PCA(n_components=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8148d661",
   "metadata": {},
   "outputs": [],
   "source": [
    "pcm10 = pca10.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b1d4afa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 52)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the data before performing pca\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7635165c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 10)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the data after performing pca\n",
    "pcm10.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3db39df0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pcm10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ef6bc1b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pca variance with 10 PCs: \n",
      "[7.43060144 4.88692958 2.92288526 2.26757644 2.09593178 1.96805068\n",
      " 1.91908036 1.63180784 1.53257435 1.42950748]\n"
     ]
    }
   ],
   "source": [
    "print(f'pca variance with 10 PCs: \\n{pca10.explained_variance_}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "23eeefd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pca variance ratio with 10 PCs: \n",
      "[0.14261039 0.09379146 0.05609691 0.04352002 0.04022577 0.03777143\n",
      " 0.03683158 0.03131816 0.02941364 0.02743555]\n"
     ]
    }
   ],
   "source": [
    "print(f'pca variance ratio with 10 PCs: \\n{pca10.explained_variance_ratio_}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "df4aba2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a3744ba5f8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(1, len(pca10.explained_variance_)+1), pca10.explained_variance_)\n",
    "plt.ylabel(\"Explained variance\")\n",
    "plt.xlabel(\"Principal Components\")\n",
    "plt.plot(range(1, len(pca10.explained_variance_)+1),\n",
    "         np.cumsum(pca10.explained_variance_),\n",
    "         c='red',\n",
    "         label=\"Cumulative Explained Variance\")\n",
    "plt.legend(loc=\"upper left\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e877fec",
   "metadata": {},
   "source": [
    "**Scree Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0a79f820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'cumulative explained variance / eigenvalue')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kyYUq0GY20swuCe7nmVlRYmOlpjNHD6JiQC4PLdSkJSIicnRh1oP+HPAn4I5g1zBgViJDpapYzLh2chWz36pj++6WqOOIiEgSC9OCvgk4F2gAcPdVwNBEhkplM6qr6Oh0Hl1cE3UUERFJYmEKdIu771/Q2MwyAV3M20MnlBcxvrxIo7lFROSowhTo2Wb2DSDPzC4F/gg8kthYqW1GdRULN+xi7bamqKOIiEiSClOg/wmoA5YAnwceB76ZyFCp7prJlZihqT9FROSIwhToPOAed/+gu38AuCfYJz1UMSCPs8cMZtbCTVrhSkREDitMgX6OgwtyHvBsYuKkj+nVVazfvoeFG3ZFHUVERJJQmAKd6+679z0I7ucnLlJ6uOKkcnIyY+rmFhGRwwpToJvM7NR9D8zsNGBv4iKlh+LcLC45sYxHFtfQ1tEZdRwREUkyYQr0F4E/mtkLZvYC8Afg5sTGSg/Tq6vY0dTKC6vqoo4iIiJJJrO7F7j762Y2HjgBMGClu7eFObiZrQMagQ6gXStgHWzauFJK8rN4cMFmLhpfFnUcERFJIt0W6MDpwKjg9dVmhrv/KuR7L3R3LYJ8GNmZMa46pYI/zd/I7pZ2CnPC/nGIiEiqCzMX96+B7wPnES/UpwNqCfeSGdVVNLd18tTS2qijiIhIEgnTZJsCTPCeXbDrwNNm5sAd7n5nD46R0k4dMZDhg/KYtXAT1502LOo4IiKSJMIMElsKlPfw+Oe6+6nA+4CbzOz8d7/AzG40s3lmNq+uLv0GS5kZMyZX8dLb29jS0Bx1HBERSRJhCvQQYLmZPWVmD+/bwhzc3TcHt1uBB4EzDvOaO919irtPKS0tPZbsKePa6io6HR5ZpHWiRUQkLkwX9209ObCZFQAxd28M7l8GfKsnx0p1Y0sLOWXYAB5csInPTh0TdRwREUkCYS6zmt3DY5cBD5rZvs/5nbs/2cNjpbzpk6v41qPLeWtLI+PKiqKOIyIiEQszivssM3vdzHabWauZdZhZQ3fvc/c17j4p2Ca6+3d6J3JqunpSJRkx0zrRIiIChDsH/TPgo8Aq4gtlfDbYJ72otCiHC8aVcv/rG2hu64g6joiIRCxMgcbd3wYy3L3D3X8BXJDQVGnqxvPHsL2plT/O2xB1FBERiViYAr3HzLKBhWb2PTP7ElCQ4Fxp6YzRg6geUcKdL6yhXQtoiIiktTAF+hNABvEFMpqA4cB1iQyVrsyMmdPGsmHHXh7XzGIiImktzCju9cHdvcC/JTaOXHpiGWNKC7j9+dVcfUoFwSh4ERFJM0dsQZvZ/cHtEjNb/O6t7yKml1jM+Pz5Y1he08ALq7TGiIhIujpaC/rW4PaqvggiB0yvruK/nnmL22ev5vxx6Tm7mohIujtiC9rda8wsA7jb3de/e+vDjGknJzODT587mrmrt7N4466o44iISASOOkjM3TuIj+Ie0Ed5JPCxM0dQlJvJ7bNXRx1FREQiEGYu7mZgiZk9Q3wUNwDufkvCUglFuVlcf9ZIbp+9mrXbmhg9RFe2iYikkzCXWT0G/B9gDjC/yyYJ9qlzR5GVEePOOWuijiIiIn0szGVW9/ZFEDnU0KJcrjt1GH9+YyNfuvR4hhblRh1JRET6SJjFMo43sz+Z2XIzW7Nv64twEp/+s62jk1++tC7qKCIi0ofCdHH/AvhfoB24EPgV8OtEhpIDRg8p4H0nlfPrV9bT2NwWdRwREekjYQp0nrs/B1hwidVtwEWJjSVdzZw2lsbmdu577Z2oo4iISB8JU6CbzSwGrDKzm81sBjA0wbmki1OGlXDO2MHc/eJaWtq1FKWISDoIU6C/COQDtwCnAdcDn0xkKDnUzGlj2dLQwkMLNkcdRURE+kCY66Db3X03sBv4VILzyBFMPX4IEyuLuX3Oaj5w2jBiMS2iISKSysK0oP/LzFaa2bfNbGLCE8lhmRmfnzaWNXVNPLNiS9RxREQkwbot0O5+IXABUAfcGaxu9c1EB5NDvf+kcoYPyuP22atx96jjiIhIAoVpQePute7+E2AmsBD4l4SmksPKzIhx49QxLHhnF6+t3RF1HBERSaAwE5WcaGa3mdlS4GfAXGBYwpPJYX1wynAGF2RrEQ0RkRQXdqKSncBl7j7N3f/X3bcmOJccQW5WBjecM4q/vlnHytqGqOOIiEiChDkHfZa7/9jddX1PkvjE2SPJz87gjtmacVVEJFWFOgctyaUkP5uPnD6ChxdtZuPOPVHHERGRBFCB7qc+O3U0Bvz8hbVRRxERkQRQge6nKkvyuGZyJX94fQM7m1qjjiMiIr3siDOJmdkjwBEvtnX3axKSSEKbOW0sD7yxiXtfXscXLxkXdRwREelFR2tBfx/4AbAW2AvcFWy7gaWJjybdGVdWxMXjh3Lv3HXsaW2POo6IiPSiIxZod5/t7rOBanf/sLs/EmwfA87ru4hyNDMvGMvOPW3c//qGqKOIiEgvCnMOutTMxux7YGajgdLERZJjcfqoQZw2ciB3vbCW9o7OqOOIiEgvCVOgvwQ8b2bPm9nzwF+JL0EpSWLmtLFs2rWXx5bURB1FRER6SbfLTbr7k2Z2PDA+2LXS3VsSG0uOxcXjh3L80EJun72GayZVYqalKEVE+rswc3HnA/8A3Ozui4ARZnZVwpNJaLGYceP5Y1hR08Dst+qijiMiIr0g7FzcrcDZweONwP9NWCLpkWsnV1ExIFeLaIiIpIgwBXqsu38PaANw972A+lCTTHZmjM+cN5pX1uxg4YZdUccREZH3KEyBbjWzPIJJS8xsLKBz0EnoI2eMoDg3k9ufVytaRKS/C1Og/xV4EhhuZr8FngO+ltBU0iOFOZn87dmjeGp5LavrdkcdR0RE3oMwy00+A/wNcANwHzDF3Z9PbCzpqRvOHUV2Roy75mgpShGR/izsYhm5wE6gAZhgZucnLpK8F0MKc/jglGE88MYmtjY0Rx1HRER6KMxlVt8FXgL+mfjlVv8AfDXBueQ9uHHqWNo7O7n7JS1FKSLSX3U7UQkwHTihp5OTmFkGMA/Y5O66froPjBicz/tPruB3r7zDTRceR3FuVtSRRETkGIXp4l4DvJf/4W8FVryH90sPzJw2lsaWdn77yjtRRxERkR4IU6D3AAvN7A4z+8m+LczBzWwYcCXw8/cSUo7dSVUDOO+4Idzz0lqa2zqijiMiIscoTIF+GPg2MBeY32UL40fEL8k64jJLZnajmc0zs3l1dZqmsjfNnDaWusYWHlywKeooIiJyjMIslnFvTw4czNe91d3nm9kFRzn+ncCdAFOmTPGefJYc3rnHDeakqmLunLOGD00ZTkZME8CJiPQXR2xBm9n9we0SM1v87i3Esc8FrjGzdcDvgYvM7De9klpCMTNmThvL2m1NPL2sNuo4IiJyDI7Wgr41uO3RyGt3/zrwdYCgBf1Vd7++J8eSnnvfSRWMHPwmt89ezRUnlWspShGRfuKILWh3rwlu1x9u67uI8l5kxIzPTR3Doo31vLJmR9RxREQkpDATlZxlZq+b2W4zazWzDjNrOJYPcffndQ10dD5w2jCGFGZrKUoRkX4kzCjunwEfBVYBecBngZ8mMpT0rtysDD517mhmv1XH8s3H9LuViIhEJNRc3O7+NpDh7h3u/gvgwsTGkt52/ZkjKcjO4I45akWLiPQHoSYqMbNs4pOVfM/MvgQUJDiX9LIB+Vl87MwRPLq4hg079kQdR0REuhGmQH8CyABuBpqA4cB1iQwlifGZ88YQM/j5C1qKUkQk2YVZD3q9u+919wZ3/zd3/3LQ5S39TPmAXKZPruIP8zawfXeP1j4REZE+crSJSg47QckxTFQiSejz08bQ3NbJvS/rSjkRkWR2tIlKdFlUCjpuaBGXTijjVy+vY+a0MeRnh1lxVERE+trRJirpOilJCzAJOAVo0UQl/dvMaWPZtaeN37+2IeooIiJyBGEmKvks8BrwN8AHgFfM7NOJDiaJc9rIgZwxahB3v7iWto4jLjQmIiIRCjOK+x+Aane/wd0/CZwG/GNiY0mizbxgDJt27eWRRZujjiIiIocRpkBvBBq7PG4E1Dfaz114wlBOKCvijtlrcNcqnyIiySZMgd4EvGpmt5nZvwKvAG+b2ZfN7MuJjSeJYmbceP4Y3tzSyF/f3Bp1HBEReZcwBXo1MAvY18x6CKgBioJN+qlrJldSOSCX25/XxCUiIskmzDU233X35q47zGyIu29LUCbpI1kZMT4zdQzffnQ589fv5LSRA6OOJCIigTAt6NfM7Kx9D8zsOmBu4iJJX/rI6cMZkJelpShFRJJMmBb0x4F7zOx5oBIYDFyUyFDSdwpyMvnk2SP5yV/e5u2tuzluaGHUkUREhHBzcS8BvgPMJL7M5M3uvjHRwaTvfPKcUeRmxbhTS1GKiCSNMBOV3A18kfgsYp8CHjGzmxIdTPrO4MIcPjRlOA8u2ERtfXP3bxARkYQLcw56KXChu69196eAs4BTExtL+trnpo6h0+Gel9ZGHUVERAjXxf1DYISZXRLsaiXeopYUMnxQPleeXMHvXn2H+r1tUccREUl7Ybq4Pwf8Cbgj2DWM+HXRkmI+P20Mu1va+c0rWgtFRCRqYbq4bwLOBRoA3H0VMDSRoSQaEysHcP64Un7x0jqa2zqijiMiktbCFOgWd2/d98DMMjkwq5ikmJnTxrBtdwt/fkMD9UVEohSmQM82s28AeWZ2KfBH4JHExpKonD1mMJOGDeCuOWvo6NTvYSIiUQlToP8JqAOWAJ8HHge+mchQEh0zY+a0sazbvocnl9ZGHUdEJG11O5OYu3cCdwWbpIHLJpYzekgBt89ezftPLsfMoo4kIpJ2wrSgJc1kxOJLUS7ZVM/c1dujjiMikpZUoOWwZlRXUVqUo0U0REQiErpAm1lBIoNIcsnNyuDT547mhVXbWLqpPuo4IiJpJ8xEJeeY2XJgRfB4kpn9T8KTSeQ+ftYICnMy1YoWEYlAmBb0D4HLge0A7r4IOD+RoSQ5FOdm8fEzR/D4khrWb2+KOo6ISFoJ1cXt7hvetUvTTKWJT583msxYjLteWBN1FBGRtBKmQG8ws3MAN7NsM/sqQXe3pL6y4lxmVFfxx3kb2ba7Jeo4IiJpI0yBnkl8Pu4qYCMwOXgsaeLGaWNo7ejk3rnroo4iIpI2whRoc/ePu3uZuw919+vdXRfHppGxpYVcNqGMX728nqaW9qjjiIikhTAFeq6ZPW1mnzGzkoQnkqQ0c9pY6ve2cd9r70QdRUQkLXRboN39eOJzb08E3jCzR83s+oQnk6RSPWIgZ44exN0vrqW1vTPqOCIiKS/sKO7X3P3LwBnADuDehKaSpDTzgrHU1Dfz8KLNUUcREUl5YSYqKTazT5rZE8BcoIZ4oZY0c8G4UsaXF3HH7NV0ailKEZGECtOCXkR85Pa33H2cu/+ju89PcC5JQvuWoly1dTd/Wbk16jgiIiktTIEe4+5fcveXj+XAZpZrZq+Z2SIzW2Zm/9bDjJJErjqlgqqSPE3/KSKSYEcs0Gb2o+Duw2Z2yBbi2C3ARe4+iXgL/AozO6sXMkuEMjNifG7qaOat38m8dTuijiMikrIyj/Lcr4Pb7/fkwO7uwO7gYVaw6cRlCvjQ6cP58XOruH32an4+alDUcUREUtIRW9BdzjNPdvfZXTfiLeJumVmGmS0EtgLPuPurh3nNjWY2z8zm1dXV9eQ7SB/Lz87kk+eM4tkVW3lrS2PUcUREUlKYc9CfPMy+G8Ic3N073H0yMAw4w8xOOsxr7nT3Ke4+pbS0NMxhJQl88uxR5GVlcMdsLaIhIpIIRzsH/VEzewQY/a7zz38lWHoyLHffBTwPXPGe0krSGFiQzYdPH85DCzexedfeqOOIiKSco7Wg5wI/AFYGt/u2rxCi0JpZ6b6pQc0sD7gkOJakiM9OHY0DP/3L28SHHIiISG854iAxd18PrAfO7uGxK4B7zSyD+C8C97v7oz08liShYQPz+dgZI/j1K+upa2zhex84hUEF2VHHEhFJCWFmEjvLzF43s91m1mpmHWbW0N373H2xu1e7+ynufpK7f6t3Iksy+bdrJvJ/rprAnLfquOJHc3jp7W1RRxIRSQlhBon9DMwiBDEAABc4SURBVPgosArIAz4L/DSRoaT/iMWMz5w3mgdvOofivCyuv/tV/uPxFVpQQ0TkPQq7WMbbQEYwKvsXwIWJjSX9zcTKATxy83l87IwR3DFnDdf971zW1O3u/o0iInJYYQr0HjPLBhaa2ffM7EtAQYJzST+Ul53Bd2aczB2fOI0NO/dw5U9e5P7XN2gAmYhID4Qp0J8AMoCbgSZgOHBdIkNJ/3b5xHKevPV8qkeU8LU/L+bm3y2gfk9b1LFERPoVS6bWzZQpU3zevHlRx5Be0tnp3PnCGr7/1JsMLcrhhx+ezJljBkcdS0QkaZjZfHefcrjnjjZRyRIzW3ykLXFxJVXEYvHlKf/8d+eQnRnjo3e9wg+efpO2Dg0gExHpztEWy7iqz1JISps0vITHbpnKbQ8v46d/eZsX397Gjz9czYjB+VFHExFJWurilj716OLNfP2BJbjDt6dPZEb1sKgjiYhEpkdd3F3e3GhmDcHWHHaiEpHDueqUSp64dSonVhTxpT8s4ou/X0BjswaQiYi8W7cF2t2L3L042HKJj+D+WeKjSaoaNjCf+z53Fl++dByPLK7h/T95gfnrd0YdS0QkqYSaqKQrd58FXJSALJJGMjNi3HLx8dz/+bNwhw/d8TI/fW4VHZ3Jc8pFRCRKRxskBoCZ/U2XhzFgCqD/RaVXnDZyEI/fOpX/M2spP3jmLV5YtY0ffmQyVSV5UUcTEYlUmBb01V22y4FG4NpEhpL0UpybxY8/Us0PPzyJZZvred+P5vDY4pqoY4mIRKrbFrS7f6ovgojMqB7GqSMGcuvvF3LT795g9lvD+NerJ1KQ0+1fUxGRlBOmi3s08AVgVNfXu/s1iYsl6Wrk4AL+OPNsfvzsKv77+bd5fd1OfvKRak4eNiDqaCIifarb66DNbBFwN7AE2D8FlLvP7u0wug5aunplzXa+9IeFbNvdwlcuO4Ebp44hFrOoY4mI9JqjXQcdpu+w2d1/0suZRLp11pjBPHHrVL7+wBL+3xMreWFVHT/44GTKB+RGHU1EJOHCDBL7sZn9q5mdbWan7tsSnkwEKMnP5n8+firfve5k3li/i/f9eA5PL6uNOpaISMKFaUGfTHzJyYs40MXt6Fpo6SNmxodPH8GUUYO45b4F3Pjr+Xz8zBF888oJ5GVnRB1PRCQhwhToGcAYd29NdBiRoxlbWsgDf38OP3j6Le6cs4ZX1+7gJx+pZkJlcdTRRER6XZgu7kVASaKDiISRk5nBN95/Ir/+zBk07G1j+n+/xD0vriWZFn0REekNYQp0GbDSzJ4ys4f3bYkOJnI0U48v5Ylbp3L+uCF869HlfOqXr1PX2BJ1LBGRXhPmMqtph9uvy6wkGbg7v3llPf/3sRUU5Wbynx+cxIUnDI06lohIKEe7zErrQUtKeGtLI7fct4CVtY186txR/OMV48nN0gAyEUluWg9aUt64siJm3XQuN5wzil+8tI7p//0Sq7Y0Rh1LRKTHtB60pIzcrAxuu2Yi99wwhbrGFq766Yv85pX1GkAmIv2S1oOWlHPR+DKe+OJUzhwzmG/OWsqNv57PjiZdJSgi/YvWg5aUNLQol1/ecDq/mLuO7z6xkit+NIcffngy5x43JOpoIiKhaD1oSVmxmPGZ80bz4E3nUJSbyfV3v8p/PLGC1vbO7t8sIhIxjeKWtLC3tYNvP7ac3736DidXDeC7153CiRVFmGl1LBGJznu6zMrM7gVudfddweOBwA/c/dO9HVQFWhLtyaW1/NMDi9m1p42inExOrCxmQkUxE4Lb48sKycnU5Vki0jfe63KTp+wrzgDuvtPMqnstnUgfuuKkck4dWcKzy7eyvKae5Zsb+MPrG9jb1gFAZsw4bmghEysH7C/aEyqKGZCfFXFyEUk3YQp0zMwGuvtOADMbFPJ9IklpaFEuHztzxP7HHZ3O+u1NLK9pYNnmBpZvbmDOqjr+/MbG/a+pKsljQmUxE7u0uKtK8tRFLiIJE6bQ/gCYa2Z/Ij56+0PAdxKaSqQPZcSMMaWFjCkt5KpTKvfv39rYzIqaRpZvbmDZ5nqW1zTw7Iot7DsrVJybGbSyB8QLd2Uxxw0tJCvjmK9eFBE5RKhBYmY2gfi1zwY85+7LExFG56Al2e1pbWdlbbxoL6+Jt7ZX1jbQ3BYfGZ6dEeP4ssL9reyJlQMYX1FEca66yEXkUJqLWySB2js6Wbe9Kd49HhTt5Zsb2N5lcpQRg/IPGow2saqY8uJcdZGLpLn3OkhMRI4iMyPGcUOLOG5oEddOrgLiq2xtbWw5qKW9vKaBJ5fV7n/fwPysAwPRgq7ysaUFZKqLXERQgRZJCDOjrDiXsuJcLhx/YPnL3S3tvFl7YDDa8poG7n15/f7JU7IzY4wvLzqotT2+opjCHP1TFUk3+lcv0ocKczI5beQgThs5aP++9o5O1mxr2l+wl22u56lltfz+9Q37XzNqcD7jy4sZNaSAUYPzGTE4n1GDCygvziUWUze5SCpSgRaJWGZGjHFlRYwrK2J69YEu8tqG5v3ns5fXNPDmlkb+snIrrR0HpirNzowxclA+IwfHC/fIIQWMHBQv3pUlueouF+nHElagzWw48CugHOgE7nT3Hyfq80RSiZlRMSCPigF5XHxi2f79HZ1OTf1e1m/fw7rtTfHbbfHbF9+u2z+aHOKTrgwflM/IoLU9YlA+o4bEi/nwgflkZ6p4iySzRLag24GvuPsbZlYEzDezZxJ1iZZIOsiIGcMG5jNsYP4hK3PtG5i2r2DvL+Dbm5i3bie7W9r3vzZmUFmSx6jBBYwcnB9sBfsLeV62pjsViVrCCrS71wA1wf1GM1sBVAEq0CIJ0HVg2pljBh/0nLuzvamV9dv3sH57E+u63D62pIZde9oOen15ce6Blndwu6+QF+mabpE+0SfnoM1sFFANvNoXnyciBzMzhhTmMKQwh9NGDjzk+fo9bazfERTubQcK+HMrt7Jtd8tBrx1SmM3IfQV7UMH+bvNRg/Mpyc/uq68kkvISPlGJmRUCs4HvuPsDh3n+RuBGgBEjRpy2fv36hOYRkWOzu6Wddw5pece7z2vqmw967YC8rPhgtaCADxuYR3FuFgU5mRTmZlIU3BbkZFKQnUmGRqBLmotsJjEzywIeBZ5y9//q7vWaSUykf2lu6+CdHXu6dJ0fOO+9aedeOrv576UgO+PQ4p19aDHfd78wJ4uCnAyKcrKCx/EtNyumWdmkX4pkJjGL/2u5G1gRpjiLSP+Tm5Wx/xKxd2tt72RLQzO7W9oPbM3x26aWdhqbu+xrPfDc9t17DjzX0k5Hd1We+OC5fcW6cH8xP8Lj3MO/pij4ZUCLnUiySOQ56HOBTwBLzGxhsO8b7v54Aj9TRJJEdmaM4YPy39Mx3J2W9s6Di/n+gt/G7paOYF9bcNsR7G9n155WNu7cs/99Ta0doT6zKDeT88eVctmEMi4cP1QLnUhkEjmK+0Xiq1+JiPSImZGblUFuVgalRTnv6VidnU5T62EKfXM7jUGrfndzOxt37uW5lVt5bHENWRnGOWOHcNnEMi6dUMbQotxe+mYi3dNqViIi79LZ6SzYsJOnlm3hqWW1rN++BzOoHl7C5RPLuXxiOaOGFEQdU1KAlpsUEekhd+etLbt5elktTy2vZemmBgDGlRVy+cRyLptQzklVxRqkJj2iAi0i0ks27tzDM8vjLevX1u6g06GqJI9LJ5Rx2cQyzhg1SHOgS2gq0CIiCbCjqZXnVmzhqWVbeGFVHS3tnZTkZ3Hx+DIun1jG1ONLNW2qHJUKtIhIgu1pbWfOW3U8tWwLz63YQkNzO3lZGZw/bgiXTyznovFDNdOaHCKS66BFRNJJfnYmV5xUwRUnVdDW0cmra3bw9PJanl4Wb2FnxIyzxgzisgnlXDaxjIoBeVFHliSnFrSISAJ1djqLN9XHB5ktq2V1XRMAk4YN4LKJ5Vw+sYzjhh460YukB3Vxi4gkibe37t7fsl64YRcAY0oLuGxCvFhPGlZCTHOUpw0VaBGRJFRb38wzy2t5evkWXl69nfZOp6w4Jz4ifEI5Z40ZTHamRoSnMhVoEZEkV7+njb++uZWnltXy/Jt17G3roCg3k4vGD+XyieVMG1dKQY6GDaUaFWgRkX6kua2DF1dt46lltTy7Ygs797SRnRlj6nHxEeEXnziUwYXvbepTSQ4axS0i0o/kZmVwyYQyLplQRntHJ/PW7+SpZfHz1s+t3ErMYMqoQcFMZmXveVESSU5qQYuI9BPuzrLNDTy9LH7eemVtIwDjy4sYU1pAeXEeFQNyKRuQS8WAXMqLcykrztV57CSmLm4RkRS0blsTzyzfwpxVdWzatZfa+mb2HGZZzSGFOfHCXRwU7gFdb/MoL87VjGcRUYEWEUkD7k5jSzu19c3U1DdTW7+XmvpmtjTsexy/rd/bdsh7B+RlHVy8i/MoH5BD+YC8/fuLcjK1KEgv0zloEZE0YGYU52ZRnJvFuLIjT36ypzVexGsbmrsU8+C2YS9LNzWwbXfLIe8ryM6gPCjW+7rTu7bGy4tzGVSQrSLeS1SgRUTSTH52JmNKCxlTWnjE17S0d7C1oYXahgOt8dr6Fmob4q3yuau3saWhmc53dcJmZ8YoLz60cMfvx4v6kMIcMjQZS7dUoEVE5BA5mRkMH5R/1BHi7R2dbNvdGrTE9x7cEq9vZsE7u6itb6a1o/Og92XEjLKinHgRL8mjMjgXXlkSL+KVQRFP9xnVVKBFRKRHMjNi+7u8GV5y2Ne4OzuaWg8U74ZmttQ3s7l+LzW7mlm+uYFnl2+hpf3gIp6VYZQV51I5II+KkgMFvCJohVeW5DEwPyulu9NVoEVEJGHMjMGFOQwuzOGkqgGHfY27s3NPG5t3xVvhNfV72bwrfluzq5n563eypaGGto6D+9NzMmNUBK3vipIDxbxrUS/O7b8D21SgRUQkUmbGoIJsBhVkH7GId3Y625paqNl1cAHfXN9Mza69vLx6+2HPiRdkZ1BRkhcU8oNb4vtuk3UK1eRMJSIi0kUsZgwtymVoUS6TjtCd3t7RydbGlnjLu76Zml0HutJr6veysraRusZDR6cX52ZSua+Il+RRUdzl3HiwPzer768TV4EWEZGUkJkRo7Ikj8qSvCO+prW9ky0Nzfu70w8U8HgRX7Sxnh1NrYe8b1BBNhUDcvn8tLFcM6kykV9jPxVoERFJG9mZsW5Hpze3dQQt8ANd6JuDAp7bh9OmqkCLiIh0kZuVweghBYweUhBpDs2gLiIikoRUoEVERJKQCrSIiEgSUoEWERFJQirQIiIiSUgFWkREJAmpQIuIiCQhFWgREZEkpAItIiKShFSgRUREkpAKtIiISBJSgRYREUlCKtAiIiJJyNw96gz7mVkdsL4XDzkE2NaLx5PD08+5b+jn3Df0c+47+lnDSHcvPdwTSVWge5uZzXP3KVHnSHX6OfcN/Zz7hn7OfUc/66NTF7eIiEgSUoEWERFJQqleoO+MOkCa0M+5b+jn3Df0c+47+lkfRUqfgxYREemvUr0FLSIi0i+lZIE2syvM7E0ze9vM/inqPKnKzIab2V/NbIWZLTOzW6POlKrMLMPMFpjZo1FnSWVmVmJmfzKzlcHf67OjzpSKzOxLwf8ZS83sPjPLjTpTMkq5Am1mGcB/A+8DJgAfNbMJ0aZKWe3AV9z9ROAs4Cb9rBPmVmBF1CHSwI+BJ919PDAJ/cx7nZlVAbcAU9z9JCAD+Ei0qZJTyhVo4AzgbXdf4+6twO+BayPOlJLcvcbd3wjuNxL/z6wq2lSpx8yGAVcCP486Syozs2LgfOBuAHdvdfdd0aZKWZlAnpllAvnA5ojzJKVULNBVwIYujzeiopFwZjYKqAZejTZJSvoR8DWgM+ogKW4MUAf8Ijid8HMzK4g6VKpx903A94F3gBqg3t2fjjZVckrFAm2H2aeh6glkZoXAn4EvuntD1HlSiZldBWx19/lRZ0kDmcCpwP+6ezXQBGgMSy8zs4HEezVHA5VAgZldH22q5JSKBXojMLzL42Go+yRhzCyLeHH+rbs/EHWeFHQucI2ZrSN+uuYiM/tNtJFS1kZgo7vv6wX6E/GCLb3rEmCtu9e5exvwAHBOxJmSUioW6NeB481stJllEx988HDEmVKSmRnx83Ur3P2/os6Titz96+4+zN1HEf+7/Bd3V2sjAdy9FthgZicEuy4GlkcYKVW9A5xlZvnB/yEXo8F4h5UZdYDe5u7tZnYz8BTx0YH3uPuyiGOlqnOBTwBLzGxhsO8b7v54hJlE3osvAL8NfrlfA3wq4jwpx91fNbM/AW8QvxJkAZpR7LA0k5iIiEgSSsUubhERkX5PBVpERCQJqUCLiIgkIRVoERGRJKQCLSIikoRUoEUiYmbPm9mUPvicW4KVmX6b6M+KUrAS1d9HnUOkt6hAi/RDwSIDYf098H53/3ii8iSJEuLfVSQlqECLHIWZjQpan3cF69c+bWZ5wXP7W8BmNiSYjhMzu8HMZpnZI2a21sxuNrMvBwswvGJmg7p8xPVmNjdYF/eM4P0FZnaPmb0evOfaLsf9o5k9AhyyuEDwGUuD7YvBvtuJLwLxsJl96V2vzzCz75vZEjNbbGZfCPZfHHzukiBHTrB/nZn9u5m9bGbzzOxUM3vKzFab2czgNReY2Rwze9DMlpvZ7WYWC577aHDMpWb23S45dpvZd8xsUfDzKQv2l5rZn4Ofw+tmdm6w/7Yg1/NmtsbMbgkO9f+AsWa20Mz+08wqgiwLg8+c2uO/CCJRcHdt2rQdYQNGEZ/taHLw+H7g+uD+88TXtAUYAqwL7t8AvA0UAaVAPTAzeO6HxBcV2ff+u4L75wNLg/v/3uUzSoC3gILguBuBQYfJeRqwJHhdIbAMqA6eWwcMOcx7/o74POqZweNBQC7x1eDGBft+1SXvOuDvunyPxV2+49Zg/wVAM/FfCjKAZ4APEF8U4Z3gtZnAX4DpwXscuDq4/z3gm8H93wHnBfdHEJ9SFuA2YC6QE/zctwNZwZ/V0i7f7yvAPwf3M4CiqP8+adN2LFvKTfUpkgBr3X3fVKbziReC7vzV42tkN5pZPfBIsH8JcEqX190H4O5zzKzYzEqAy4gvkPHV4DW5xAsUwDPuvuMwn3ce8KC7NwGY2QPAVOLTKB7JJcDt7t4eZNhhZpOC7/tW8Jp7gZuIL3kJB+a1XwIUdvmOzUF2gNfcfU2Q474gWxvwvLvXBft/S/yXkllAK/Bo8N75wKVd8k2IT9cMQLGZFQX3H3P3FqDFzLYCZYf5fq8D9wQLuszq8mco0i+oQIt0r6XL/Q4gL7jfzoHTRLlHeU9nl8edHPzv7t1z7TrxJVOvc/c3uz5hZmcSXwLxcA63zGp37DCf391xun6Pd3/Hfd/rSN/pSNrcfd97OrocJwac7e57DwoYL9jv/jM55P+y4Jee84ErgV+b2X+6+6+OkkMkqegctEjPrSPetQzxbtye+DCAmZ1HfOH6euILvXwhWOkHM6sOcZw5wPRghaACYAbwQjfveRqYuW/AWXBufCUwysyOC17zCWD2MX6nMyy+mlyM+Pd7EXgVmBacq88APhriuE8DN+97YGaTu3l9I/Eu932vH0m86/0u4quuaelI6VfUghbpue8D95vZJ4ifU+2JnWY2FygGPh3s+zbxLuXFQZFeB1x1tIO4+xtm9kvgtWDXz939aN3bAD8HxgWf00b8fPjPzOxTwB+Dwv06cPsxfqeXiQ/YOpn4Lw4PununmX0d+Cvx1vTj7v5QN8e5BfhvM1tM/P+qOcDMI73Y3beb2UtmthR4AlgK/EPw3XYDf3uM30MkUlrNSkR6jZldAHzV3Y/6C4WIdE9d3CIiIklILWgREZEkpBa0iIhIElKBFhERSUIq0CIiIklIBVpERCQJqUCLiIgkIRVoERGRJPT/AUxvhEelT6atAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(pca10.explained_variance_)\n",
    "plt.xlabel('number of components')\n",
    "plt.ylabel('cumulative explained variance / eigenvalue')\n",
    "# actually we can use until the 7th PCs(Principal Components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "69fa611c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'PC2')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scattering first and the second principal component\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(pcm10[:, 0], pcm10[:, 1], alpha=0.5)\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e6300830",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pca10.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ac9e316a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca10.n_components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f90b54d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6))\n",
    "ax = sns.heatmap(pca10.components_,\n",
    "                 cmap='YlGnBu',\n",
    "                 yticklabels=[\"PCA\" + str(x) for x in range(1, pca10.n_components_ + 1)],\n",
    "                 xticklabels=list(x.columns),\n",
    "                 cbar_kws={\"orientation\": \"horizontal\"}\n",
    "                )\n",
    "ax.set_aspect(\"equal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3356fcde",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PCA Biplot\n",
    "def myplot(score,coeff,labels=None):\n",
    "    plt.figure(figsize=(8, 6), dpi=80)\n",
    "    xs = score[:,0]\n",
    "    ys = score[:,1]\n",
    "    n = coeff.shape[0]\n",
    "    scalex = 1.0/(xs.max() - xs.min())\n",
    "    scaley = 1.0/(ys.max() - ys.min())\n",
    "    plt.scatter(xs * scalex,ys * scaley,s=5)\n",
    "    for i in range(n):\n",
    "        plt.arrow(0, 0, coeff[i,0], coeff[i,1],color = 'r',alpha = 0.5)\n",
    "        if labels is None:\n",
    "            plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, \"Var\"+str(i+1), color = 'green', ha = 'center', va = 'center')\n",
    "        else:\n",
    "            plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, labels[i], color = 'g', ha = 'center', va = 'center')\n",
    " \n",
    "    plt.xlabel(\"PC{}\".format(1))\n",
    "    plt.ylabel(\"PC{}\".format(2))\n",
    "    plt.grid()\n",
    "    \n",
    "## Biplot is actually a plot who has contain two plot which is,\n",
    "# 1.Scatter plot of PC1 and PC2 or the other name is Score Plot\n",
    "# 2.Loading plot which show how strong each caracteristic influences a principal component\n",
    "\n",
    "## Scatter plot\n",
    "# - the Scatter plot show that PCs describe variation and account for the varied influences\n",
    "#   of the original characteristics. \n",
    "\n",
    "## Loading plot\n",
    "# - the angels between the vectors tell us how characteristics correlated with one another\n",
    "# - when two vectors are close, forming a small angle, the two variables they represent are \n",
    "#   positively correlated like, B and RM\n",
    "# - when they forming an 90°, they don't have any correlation like, DIS and CRIM\n",
    "# - when they diverge and form a large angle (close to 180°), they are negative corellated \n",
    "#   like,DIS and AGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6f85aeed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "myplot(pcm10[:,0:2],np.transpose(pca10.components_[0:2, :]),list(x.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24ee6820",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8af0f3ae",
   "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}