HR_placements_deploy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This is an excerpt from a HR placement application built for a HR consultant written in python scripts.  New employees will be placed into clusters according to their personality types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.cluster import KMeans\n",
    "from random import sample\n",
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "#plt.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize']=(15,10)\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data-final-clean.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>EXT1</th>\n",
       "      <th>EXT2</th>\n",
       "      <th>EXT3</th>\n",
       "      <th>EXT4</th>\n",
       "      <th>EXT5</th>\n",
       "      <th>EXT6</th>\n",
       "      <th>EXT7</th>\n",
       "      <th>EXT8</th>\n",
       "      <th>EXT9</th>\n",
       "      <th>EXT10</th>\n",
       "      <th>...</th>\n",
       "      <th>OPN2</th>\n",
       "      <th>OPN3</th>\n",
       "      <th>OPN4</th>\n",
       "      <th>OPN5</th>\n",
       "      <th>OPN6</th>\n",
       "      <th>OPN7</th>\n",
       "      <th>OPN8</th>\n",
       "      <th>OPN9</th>\n",
       "      <th>OPN10</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>GB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>MY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>GB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   EXT1  EXT2  EXT3  EXT4  EXT5  EXT6  EXT7  EXT8  EXT9  EXT10  ...  OPN2  \\\n",
       "0   4.0   1.0   5.0   2.0   5.0   1.0   5.0   2.0   4.0    1.0  ...   1.0   \n",
       "1   3.0   5.0   3.0   4.0   3.0   3.0   2.0   5.0   1.0    5.0  ...   2.0   \n",
       "2   2.0   3.0   4.0   4.0   3.0   2.0   1.0   3.0   2.0    5.0  ...   1.0   \n",
       "\n",
       "   OPN3  OPN4  OPN5  OPN6  OPN7  OPN8  OPN9  OPN10  country  \n",
       "0   4.0   1.0   4.0   1.0   5.0   3.0   4.0    5.0       GB  \n",
       "1   4.0   2.0   3.0   1.0   4.0   2.0   5.0    3.0       MY  \n",
       "2   2.0   1.0   4.0   2.0   5.0   3.0   4.0    4.0       GB  \n",
       "\n",
       "[3 rows x 51 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# draw 10,000 samples\n",
    "\n",
    "data_sample = data.sample(10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>GB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152326</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235957</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
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       "        EXT1  EXT2  EXT3  EXT4  EXT5  EXT6  EXT7  EXT8  EXT9  EXT10  ...  \\\n",
       "674246   2.0   2.0   2.0   2.0   5.0   1.0   2.0   4.0   3.0    4.0  ...   \n",
       "273682   2.0   3.0   3.0   5.0   3.0   1.0   3.0   1.0   5.0    2.0  ...   \n",
       "474493   1.0   4.0   3.0   3.0   3.0   2.0   2.0   4.0   4.0    5.0  ...   \n",
       "152326   3.0   1.0   3.0   4.0   3.0   2.0   2.0   4.0   4.0    4.0  ...   \n",
       "235957   3.0   2.0   5.0   2.0   4.0   2.0   5.0   3.0   4.0    3.0  ...   \n",
       "\n",
       "        OPN2  OPN3  OPN4  OPN5  OPN6  OPN7  OPN8  OPN9  OPN10  country  \n",
       "674246   1.0   5.0   1.0   4.0   1.0   5.0   4.0   5.0    5.0       CA  \n",
       "273682   1.0   5.0   1.0   4.0   1.0   4.0   2.0   5.0    5.0       CL  \n",
       "474493   1.0   5.0   1.0   4.0   1.0   5.0   1.0   5.0    5.0       GB  \n",
       "152326   2.0   3.0   2.0   2.0   4.0   4.0   4.0   4.0    4.0       US  \n",
       "235957   1.0   5.0   1.0   5.0   1.0   5.0   5.0   4.0    5.0       NZ  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute the mode of every personality trait and assign to new dataframe\n",
    "\n",
    "data_sample['extraversion']=data_sample.loc[:,'EXT1':'EXT10'].mode(axis=1).max(axis=1)\n",
    "data_sample['emotional_stability']=data_sample.loc[:,'EST1':'EST10'].mode(axis=1).max(axis=1)\n",
    "data_sample['aggreableness']=data_sample.loc[:,'AGR1':'AGR10'].mode(axis=1).max(axis=1)\n",
    "data_sample['conscientiousness']=data_sample.loc[:,'CSN1':'CSN10'].mode(axis=1).max(axis=1)\n",
    "data_sample['openness']=data_sample.loc[:,'OPN1':'OPN10'].mode(axis=1).max(axis=1)\n",
    "\n",
    "data_mode = data_sample.loc[:,'country':'openness']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>extraversion</th>\n",
       "      <th>emotional_stability</th>\n",
       "      <th>aggreableness</th>\n",
       "      <th>conscientiousness</th>\n",
       "      <th>openness</th>\n",
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       "  </thead>\n",
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       "      <th>674246</th>\n",
       "      <td>CA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273682</th>\n",
       "      <td>CL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474493</th>\n",
       "      <td>GB</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152326</th>\n",
       "      <td>US</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235957</th>\n",
       "      <td>NZ</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  extraversion  emotional_stability  aggreableness  \\\n",
       "674246      CA           2.0                  2.0            4.0   \n",
       "273682      CL           3.0                  2.0            5.0   \n",
       "474493      GB           4.0                  5.0            4.0   \n",
       "152326      US           4.0                  4.0            4.0   \n",
       "235957      NZ           3.0                  2.0            2.0   \n",
       "\n",
       "        conscientiousness  openness  \n",
       "674246                4.0       5.0  \n",
       "273682                3.0       5.0  \n",
       "474493                4.0       5.0  \n",
       "152326                4.0       4.0  \n",
       "235957                4.0       5.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# review\n",
    "\n",
    "data_mode.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict with KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute optimum number of clusters for KMeans using Elbow Technique\n",
    "\n",
    "x = data_mode.loc[:,'extraversion':'openness']\n",
    "k = range(1,10)\n",
    "sse=[]\n",
    "for i in k:\n",
    "    km = KMeans(n_clusters=i, random_state=0).fit(x)\n",
    "    sse.append(km.inertia_)\n",
    "    \n",
    "plt.plot(k, sse);\n",
    "plt.xlabel('Number of K clusters');\n",
    "plt.ylabel('Sum of Squared Error');\n",
    "plt.title('Elbow Technique to determine K value');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction by KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>country</th>\n",
       "      <th>extraversion</th>\n",
       "      <th>emotional_stability</th>\n",
       "      <th>aggreableness</th>\n",
       "      <th>conscientiousness</th>\n",
       "      <th>openness</th>\n",
       "      <th>clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>674246</th>\n",
       "      <td>CA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273682</th>\n",
       "      <td>CL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474493</th>\n",
       "      <td>GB</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152326</th>\n",
       "      <td>US</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235957</th>\n",
       "      <td>NZ</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  extraversion  emotional_stability  aggreableness  \\\n",
       "674246      CA           2.0                  2.0            4.0   \n",
       "273682      CL           3.0                  2.0            5.0   \n",
       "474493      GB           4.0                  5.0            4.0   \n",
       "152326      US           4.0                  4.0            4.0   \n",
       "235957      NZ           3.0                  2.0            2.0   \n",
       "\n",
       "        conscientiousness  openness  clusters  \n",
       "674246                4.0       5.0         0  \n",
       "273682                3.0       5.0         0  \n",
       "474493                4.0       5.0         2  \n",
       "152326                4.0       4.0         2  \n",
       "235957                4.0       5.0         1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = data_mode.loc[:,'extraversion':'openness']\n",
    "kmeans = KMeans(n_clusters=4, random_state=42).fit(x)\n",
    "data_mode['clusters'] = kmeans.predict(x)\n",
    "data_mode.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = ['cluster 0','Cluster 3','Cluster 2','Cluster 1']\n",
    "plt.pie(data_mode['clusters'].value_counts(), labels=label, autopct='%.1f%%')\n",
    "plt.title('Proportion Of Personalities', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    2804\n",
       "2    2743\n",
       "0    2548\n",
       "1    1905\n",
       "Name: clusters, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mode['clusters'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use One way anova to test if each cluster is significantly different from the others\n",
    "### Hypthesis testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_0_extraversion = data_mode.loc[data_mode['clusters']==0, 'extraversion']\n",
    "cluster_1_extraversion = data_mode.loc[data_mode['clusters']==1, 'extraversion']\n",
    "cluster_2_extraversion = data_mode.loc[data_mode['clusters']==2, 'extraversion']\n",
    "cluster_3_extraversion = data_mode.loc[data_mode['clusters']==3, 'extraversion']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=2803.4549908735967, pvalue=0.0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import f_oneway\n",
    "\n",
    "f_oneway(cluster_0_extraversion, cluster_1_extraversion, cluster_2_extraversion,cluster_3_extraversion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_0_emotional_stability = data_mode.loc[data_mode['clusters']==0, 'emotional_stability']\n",
    "cluster_1_emotional_stability = data_mode.loc[data_mode['clusters']==1, 'emotional_stability']\n",
    "cluster_2_emotional_stability = data_mode.loc[data_mode['clusters']==2, 'emotional_stability']\n",
    "cluster_3_emotional_stability = data_mode.loc[data_mode['clusters']==3, 'emotional_stability']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=5206.364725248148, pvalue=0.0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "f_oneway(cluster_0_emotional_stability, cluster_1_emotional_stability, cluster_2_emotional_stability,\n",
    "         cluster_3_emotional_stability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_0_aggreableness = data_mode.loc[data_mode['clusters']==0, 'aggreableness']\n",
    "cluster_1_aggreableness = data_mode.loc[data_mode['clusters']==1, 'aggreableness']\n",
    "cluster_2_aggreableness = data_mode.loc[data_mode['clusters']==2, 'aggreableness']\n",
    "cluster_3_aggreableness = data_mode.loc[data_mode['clusters']==3, 'aggreableness']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=4798.8758845616385, pvalue=0.0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_oneway(cluster_0_aggreableness, cluster_1_aggreableness, cluster_2_aggreableness,\n",
    "         cluster_3_aggreableness)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_0_conscientiousness = data_mode.loc[data_mode['clusters']==0, 'conscientiousness']\n",
    "cluster_1_conscientiousness = data_mode.loc[data_mode['clusters']==1, 'conscientiousness']\n",
    "cluster_2_conscientiousness = data_mode.loc[data_mode['clusters']==2, 'conscientiousness']\n",
    "cluster_3_conscientiousness = data_mode.loc[data_mode['clusters']==3, 'conscientiousness']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=22.228652193771136, pvalue=2.4340736719941574e-14)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_oneway(cluster_0_conscientiousness, cluster_1_conscientiousness, cluster_2_conscientiousness,\n",
    "         cluster_3_conscientiousness)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_0_openness = data_mode.loc[data_mode['clusters']==0, 'openness']\n",
    "cluster_1_openness = data_mode.loc[data_mode['clusters']==1, 'openness']\n",
    "cluster_2_openness = data_mode.loc[data_mode['clusters']==2, 'openness']\n",
    "cluster_3_openness = data_mode.loc[data_mode['clusters']==3, 'openness']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=40.75265115068055, pvalue=3.626948977708739e-26)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_oneway(cluster_0_openness, cluster_1_openness, cluster_2_openness,\n",
    "         cluster_3_openness)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_0 = data_mode[data_mode['clusters']==0]\n",
    "clusters_1 = data_mode[data_mode['clusters']==1]\n",
    "clusters_2 = data_mode[data_mode['clusters']==2]\n",
    "clusters_3 = data_mode[data_mode['clusters']==3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test accuracy by Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score is 0.999\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import cross_val_score\n",
    "rfc = RandomForestClassifier()\n",
    "X = data_mode.drop(['country','clusters'],axis=1)\n",
    "y = data_mode.clusters\n",
    "sc = StandardScaler()\n",
    "X = sc.fit_transform(X)\n",
    "acc = round(np.mean(cross_val_score(rfc, X,y, cv=5, scoring='accuracy')),3)\n",
    "print(f\"Accuracy score is {acc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise KMeans Prediction With Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x2520 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize personality traits for cluster 1 prediction\n",
    "\n",
    "fig = plt.figure(figsize=(12,35))\n",
    "h=411\n",
    "grp = [clusters_0, clusters_1, clusters_2, clusters_3]\n",
    "grp_name = ['Cluster 0', 'Cluster 1', 'Cluster 2', 'Cluster 3']\n",
    "num = range(4)\n",
    "for n, k, g  in zip(num, grp, grp_name):\n",
    "    ax = fig.add_subplot(h)\n",
    "    ax.boxplot(k.drop(['country','clusters'], axis=1));\n",
    "    ax.set_xticklabels(['extraversion','emotional stability','agreeableness','conscientiousness','openness']);\n",
    "    plt.title(f'Boxplot For {g}', fontsize=15)\n",
    "    plt.ylabel('Scores', fontsize=13)\n",
    "    h+=1\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_clusters(df):\n",
    "    df['extraversion']=df.loc[:,'EXT1':'EXT10'].mode(axis=1).max(axis=1)\n",
    "    df['emotional_stability']=df.loc[:,'EST1':'EST10'].mode(axis=1).max(axis=1)\n",
    "    df['aggreableness']=df.loc[:,'AGR1':'AGR10'].mode(axis=1).max(axis=1)\n",
    "    df['conscientiousness']=df.loc[:,'CSN1':'CSN10'].mode(axis=1).max(axis=1)\n",
    "    df['openness']=df.loc[:,'OPN1':'OPN10'].mode(axis=1).max(axis=1)\n",
    "    df = df.loc[:,'country':'openness']\n",
    "    x = df.loc[:,'extraversion':'openness']\n",
    "    df['clusters'] = kmeans.predict(x)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare test sample\n",
    "\n",
    "test_sample = data.sample(500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>extraversion</th>\n",
       "      <th>emotional_stability</th>\n",
       "      <th>aggreableness</th>\n",
       "      <th>conscientiousness</th>\n",
       "      <th>openness</th>\n",
       "      <th>clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>611479</th>\n",
       "      <td>US</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445609</th>\n",
       "      <td>US</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282454</th>\n",
       "      <td>CA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670469</th>\n",
       "      <td>US</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326089</th>\n",
       "      <td>US</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  extraversion  emotional_stability  aggreableness  \\\n",
       "611479      US           3.0                  4.0            5.0   \n",
       "445609      US           5.0                  1.0            5.0   \n",
       "282454      CA           4.0                  5.0            5.0   \n",
       "670469      US           2.0                  3.0            3.0   \n",
       "326089      US           3.0                  5.0            4.0   \n",
       "\n",
       "        conscientiousness  openness  clusters  \n",
       "611479                2.0       4.0         3  \n",
       "445609                4.0       5.0         0  \n",
       "282454                5.0       5.0         2  \n",
       "670469                3.0       4.0         1  \n",
       "326089                4.0       4.0         3  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict with test sample\n",
    "\n",
    "test_pred = predict_clusters(test_sample)\n",
    "test_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    140\n",
       "0    132\n",
       "2    127\n",
       "1    101\n",
       "Name: clusters, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred.clusters.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize test sample personalities based on clusters\n",
    "\n",
    "label = ['cluster 0','Cluster 3','Cluster 2','Cluster 1']\n",
    "plt.pie(test_pred['clusters'].value_counts(), labels=label, autopct='%.1f%%')\n",
    "plt.title('Prediction Of Personalities Into 4 Org Depts aka \"clusters\"', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x2520 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize personality traits for cluster 1 prediction\n",
    "\n",
    "predict_clusters_0 = test_pred[test_pred['clusters']==0]\n",
    "predict_clusters_1 = test_pred[test_pred['clusters']==1]\n",
    "predict_clusters_2 = test_pred[test_pred['clusters']==2]\n",
    "predict_clusters_3 = test_pred[test_pred['clusters']==3]\n",
    "\n",
    "fig = plt.figure(figsize=(12,35))\n",
    "h=411\n",
    "grp = [predict_clusters_0, predict_clusters_1, predict_clusters_2, predict_clusters_3]\n",
    "grp_name = ['Predict Cluster 0', 'Predict Cluster 1', 'Predict Cluster 2', 'Predict Cluster 3']\n",
    "num = range(4)\n",
    "for n, k, g  in zip(num, grp, grp_name):\n",
    "    ax = fig.add_subplot(h)\n",
    "    ax.boxplot(k.drop(['country','clusters'], axis=1));\n",
    "    ax.set_xticklabels(['extraversion','emotional stability','agreeableness','conscientiousness','openness']);\n",
    "    plt.title(f'Boxplot For {g}', fontsize=15)\n",
    "    plt.ylabel('Scores', fontsize=13)\n",
    "    h+=1\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Chong Meng\\\\OneDrive\\\\ML learning'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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