work9_PCA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from scipy import stats\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DISSATIS</th>\n",
       "      <th>PRIVILEGE</th>\n",
       "      <th>CHANCE</th>\n",
       "      <th>ADVANCE</th>\n",
       "      <th>CRITISM</th>\n",
       "      <th>PROMOTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>30</td>\n",
       "      <td>39</td>\n",
       "      <td>61</td>\n",
       "      <td>92</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64</td>\n",
       "      <td>51</td>\n",
       "      <td>54</td>\n",
       "      <td>63</td>\n",
       "      <td>73</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>68</td>\n",
       "      <td>69</td>\n",
       "      <td>76</td>\n",
       "      <td>86</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>54</td>\n",
       "      <td>84</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78</td>\n",
       "      <td>56</td>\n",
       "      <td>66</td>\n",
       "      <td>71</td>\n",
       "      <td>83</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DISSATIS  PRIVILEGE  CHANCE  ADVANCE  CRITISM  PROMOTION\n",
       "0        51         30      39       61       92         45\n",
       "1        64         51      54       63       73         47\n",
       "2        70         68      69       76       86         48\n",
       "3        63         45      47       54       84         35\n",
       "4        78         56      66       71       83         47"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_supervisor = pd.read_csv(\"실습파일/직무능력평가.csv\", engine = 'python')\n",
    "ds_supervisor_x = ds_supervisor.drop(\"EVAL\", axis =1)\n",
    "ds_supervisor_y = ds_supervisor[\"EVAL\"]\n",
    "ds_supervisor_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>DISSATIS</th>\n",
       "      <th>PRIVILEGE</th>\n",
       "      <th>CHANCE</th>\n",
       "      <th>ADVANCE</th>\n",
       "      <th>CRITISM</th>\n",
       "      <th>PROMOTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DISSATIS</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.558288</td>\n",
       "      <td>0.596736</td>\n",
       "      <td>0.669197</td>\n",
       "      <td>0.187714</td>\n",
       "      <td>0.224580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRIVILEGE</th>\n",
       "      <td>0.558288</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.493331</td>\n",
       "      <td>0.445478</td>\n",
       "      <td>0.147233</td>\n",
       "      <td>0.343293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHANCE</th>\n",
       "      <td>0.596736</td>\n",
       "      <td>0.493331</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.640314</td>\n",
       "      <td>0.115965</td>\n",
       "      <td>0.531620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADVANCE</th>\n",
       "      <td>0.669197</td>\n",
       "      <td>0.445478</td>\n",
       "      <td>0.640314</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.376883</td>\n",
       "      <td>0.574186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRITISM</th>\n",
       "      <td>0.187714</td>\n",
       "      <td>0.147233</td>\n",
       "      <td>0.115965</td>\n",
       "      <td>0.376883</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.283343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROMOTION</th>\n",
       "      <td>0.224580</td>\n",
       "      <td>0.343293</td>\n",
       "      <td>0.531620</td>\n",
       "      <td>0.574186</td>\n",
       "      <td>0.283343</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           DISSATIS  PRIVILEGE    CHANCE   ADVANCE   CRITISM  PROMOTION\n",
       "DISSATIS   1.000000   0.558288  0.596736  0.669197  0.187714   0.224580\n",
       "PRIVILEGE  0.558288   1.000000  0.493331  0.445478  0.147233   0.343293\n",
       "CHANCE     0.596736   0.493331  1.000000  0.640314  0.115965   0.531620\n",
       "ADVANCE    0.669197   0.445478  0.640314  1.000000  0.376883   0.574186\n",
       "CRITISM    0.187714   0.147233  0.115965  0.376883  1.000000   0.283343\n",
       "PROMOTION  0.224580   0.343293  0.531620  0.574186  0.283343   1.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_supervisor_x.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fa7224f9860>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(ds_supervisor_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaled Data :\n",
      "[[-1.192 -1.923 -1.505 -0.355  1.771  0.204]\n",
      " [-0.199 -0.177 -0.205 -0.16  -0.182  0.402]\n",
      " [ 0.26   1.236  1.095  1.112  1.155  0.501]\n",
      " [-0.275 -0.676 -0.812 -1.04   0.949 -0.784]\n",
      " [ 0.871  0.238  0.835  0.623  0.846  0.402]]\n",
      "Shape :\n",
      "(30, 6)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/preprocessing/data.py:625: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/base.py:462: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "ds_supervisor_x_scaled = scaler.fit_transform(ds_supervisor_x)\n",
    "\n",
    "print(\"Scaled Data :\\n{}\".format(ds_supervisor_x_scaled[:5].round(3)))\n",
    "print(\"Shape :\\n{}\".format(ds_supervisor_x_scaled.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCA Data : \n",
      "[[ 1.67606983 -2.73856419 -0.12843595 -0.50321261  0.24330658  0.22576117]\n",
      " [ 0.21834397 -0.12615302  0.43115839  0.08726221  0.19566103  0.1941439 ]\n",
      " [-2.10515127 -0.51500683 -0.27683087  0.49131136 -0.44691698 -0.63174369]\n",
      " [ 1.35994032 -0.86064574 -0.91799066  0.03173793 -0.40137177  0.44095403]\n",
      " [-1.5120347  -0.35277019 -0.32684445 -0.32877858 -0.32709718  0.21246099]]\n",
      "Shape : \n",
      "(30, 6)\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components = 6)\n",
    "x_pca = pca.fit_transform(ds_supervisor_x_scaled)\n",
    "\n",
    "print(\"PCA Data : \\n{}\".format(x_pca[:5]))\n",
    "print(\"Shape : \\n{}\".format(x_pca.shape))\n",
    "\n",
    "ds_x_pca = pd.DataFrame(x_pca, columns=[\"Prin1\",\"Prin2\",\"Prin3\",\"Prin4\",\"Prin5\",\"Prin6\" ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Prin</th>\n",
       "      <th>EigenValues</th>\n",
       "      <th>EigenValueRatio</th>\n",
       "      <th>CumsumEigenValueRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.279</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.041</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.328</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.032</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Prin  EigenValues  EigenValueRatio  CumsumEigenValueRatio\n",
       "0     1        3.279            0.528                  0.528\n",
       "1     2        1.041            0.168                  0.696\n",
       "2     3        0.789            0.127                  0.823\n",
       "3     4        0.572            0.092                  0.915\n",
       "4     5        0.328            0.053                  0.968\n",
       "5     6        0.198            0.032                  1.000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_pca = pd.DataFrame()\n",
    "ds_pca[\"Prin\"] = [ i for i in range(1, 7)]\n",
    "ds_pca[\"EigenValues\"] = pca.explained_variance_\n",
    "ds_pca[\"EigenValueRatio\"] = pca.explained_variance_ratio_\n",
    "ds_pca[\"CumsumEigenValueRatio\"] = np.cumsum(pca.explained_variance_ratio_)\n",
    "ds_pca.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Feature</th>\n",
       "      <th>Prin1</th>\n",
       "      <th>Prin2</th>\n",
       "      <th>Prin3</th>\n",
       "      <th>Prin4</th>\n",
       "      <th>Prin5</th>\n",
       "      <th>Prin6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DISSATIS</td>\n",
       "      <td>-0.439375</td>\n",
       "      <td>-0.394711</td>\n",
       "      <td>-0.461401</td>\n",
       "      <td>-0.492658</td>\n",
       "      <td>-0.224813</td>\n",
       "      <td>-0.380801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PRIVILEGE</td>\n",
       "      <td>0.312642</td>\n",
       "      <td>0.308751</td>\n",
       "      <td>0.217087</td>\n",
       "      <td>-0.115532</td>\n",
       "      <td>-0.802247</td>\n",
       "      <td>-0.320706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHANCE</td>\n",
       "      <td>-0.445167</td>\n",
       "      <td>-0.217414</td>\n",
       "      <td>0.271981</td>\n",
       "      <td>-0.005605</td>\n",
       "      <td>-0.457246</td>\n",
       "      <td>0.686643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ADVANCE</td>\n",
       "      <td>-0.316019</td>\n",
       "      <td>0.814847</td>\n",
       "      <td>-0.224796</td>\n",
       "      <td>-0.365108</td>\n",
       "      <td>0.099947</td>\n",
       "      <td>0.205742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CRITISM</td>\n",
       "      <td>0.191521</td>\n",
       "      <td>0.037686</td>\n",
       "      <td>-0.775648</td>\n",
       "      <td>0.460364</td>\n",
       "      <td>-0.288875</td>\n",
       "      <td>0.254728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PROMOTION</td>\n",
       "      <td>0.611949</td>\n",
       "      <td>-0.190294</td>\n",
       "      <td>-0.117671</td>\n",
       "      <td>-0.631404</td>\n",
       "      <td>0.057847</td>\n",
       "      <td>0.416465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Feature     Prin1     Prin2     Prin3     Prin4     Prin5     Prin6\n",
       "0   DISSATIS -0.439375 -0.394711 -0.461401 -0.492658 -0.224813 -0.380801\n",
       "1  PRIVILEGE  0.312642  0.308751  0.217087 -0.115532 -0.802247 -0.320706\n",
       "2     CHANCE -0.445167 -0.217414  0.271981 -0.005605 -0.457246  0.686643\n",
       "3    ADVANCE -0.316019  0.814847 -0.224796 -0.365108  0.099947  0.205742\n",
       "4    CRITISM  0.191521  0.037686 -0.775648  0.460364 -0.288875  0.254728\n",
       "5  PROMOTION  0.611949 -0.190294 -0.117671 -0.631404  0.057847  0.416465"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_feature = pd.DataFrame({\"Feature\": ds_supervisor_x.columns})\n",
    "\n",
    "ds_eigenvectors = pd.concat([ds_feature,\n",
    "                            pd.DataFrame(pca.components_, columns=[\"Prin1\",\"Prin2\",\"Prin3\",\"Prin4\",\"Prin5\",\"Prin6\"])],\n",
    "                           axis= 1)\n",
    "ds_eigenvectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa71cfa8320>"
      ]
     },
     "execution_count": 15,
     "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.plot(ds_pca[\"Prin\"], ds_pca[\"EigenValueRatio\"], label = \"Ratio\")\n",
    "\n",
    "plt.plot(ds_pca[\"Prin\"], ds_pca[\"CumsumEigenValueRatio\"], label = \"Cumsum Ratio\")\n",
    "\n",
    "plt.ylabel(\"Eigen Value ratio\")\n",
    "plt.xlabel(\"Prin\")\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fa71ecf2048>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(ds_x_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Prin1</th>\n",
       "      <th>Prin2</th>\n",
       "      <th>Prin3</th>\n",
       "      <th>Prin4</th>\n",
       "      <th>Prin5</th>\n",
       "      <th>Prin6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Prin1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prin2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prin3</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prin4</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prin5</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prin6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Prin1  Prin2  Prin3  Prin4  Prin5  Prin6\n",
       "Prin1    1.0    0.0   -0.0   -0.0   -0.0    0.0\n",
       "Prin2    0.0    1.0    0.0   -0.0   -0.0    0.0\n",
       "Prin3   -0.0    0.0    1.0    0.0    0.0   -0.0\n",
       "Prin4   -0.0   -0.0    0.0    1.0   -0.0   -0.0\n",
       "Prin5   -0.0   -0.0    0.0   -0.0    1.0   -0.0\n",
       "Prin6    0.0    0.0   -0.0   -0.0   -0.0    1.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_x_pca.corr().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   EVAL   R-squared:                       0.685\n",
      "Model:                            OLS   Adj. R-squared:                  0.650\n",
      "Method:                 Least Squares   F-statistic:                     19.53\n",
      "Date:                Wed, 13 Feb 2019   Prob (F-statistic):           3.09e-05\n",
      "Time:                        13:10:36   Log-Likelihood:                -71.658\n",
      "No. Observations:                  21   AIC:                             149.3\n",
      "Df Residuals:                      18   BIC:                             152.4\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         64.5550      1.744     37.022      0.000      60.892      68.218\n",
      "Prin1         -5.3136      0.917     -5.796      0.000      -7.240      -3.387\n",
      "Prin2          3.0825      1.551      1.987      0.062      -0.177       6.342\n",
      "==============================================================================\n",
      "Omnibus:                        0.925   Durbin-Watson:                   3.074\n",
      "Prob(Omnibus):                  0.630   Jarque-Bera (JB):                0.866\n",
      "Skew:                          -0.299   Prob(JB):                        0.649\n",
      "Kurtosis:                       2.205   Cond. No.                         1.94\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_var = [\"Prin1\", \"Prin2\"]\n",
    "\n",
    "ds_x_pca_const = sm.add_constant(ds_x_pca[x_var])\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(ds_x_pca_const, ds_supervisor_y,\n",
    "                                                   test_size = 0.3, random_state = 2222)\n",
    "\n",
    "reg_model = sm.OLS(y_train, x_train)\n",
    "\n",
    "reg_model = reg_model.fit()\n",
    "print(reg_model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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