ml_essentials.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('kmeans_dataset.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>Hardwork</th>\n",
       "      <th>Skills</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.483358</td>\n",
       "      <td>6.135020</td>\n",
       "      <td>C Tier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52.864104</td>\n",
       "      <td>48.897037</td>\n",
       "      <td>B Tier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67.556683</td>\n",
       "      <td>19.359183</td>\n",
       "      <td>D Tier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52.797684</td>\n",
       "      <td>43.650076</td>\n",
       "      <td>B Tier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45.060080</td>\n",
       "      <td>74.978268</td>\n",
       "      <td>B Tier</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hardwork     Skills   Label\n",
       "0   1.483358   6.135020  C Tier\n",
       "1  52.864104  48.897037  B Tier\n",
       "2  67.556683  19.359183  D Tier\n",
       "3  52.797684  43.650076  B Tier\n",
       "4  45.060080  74.978268  B Tier"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>Hardwork</th>\n",
       "      <th>Skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.483358</td>\n",
       "      <td>6.135020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52.864104</td>\n",
       "      <td>48.897037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67.556683</td>\n",
       "      <td>19.359183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52.797684</td>\n",
       "      <td>43.650076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45.060080</td>\n",
       "      <td>74.978268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hardwork     Skills\n",
       "0   1.483358   6.135020\n",
       "1  52.864104  48.897037\n",
       "2  67.556683  19.359183\n",
       "3  52.797684  43.650076\n",
       "4  45.060080  74.978268"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata=data.drop('Label', axis =1)\n",
    "mydata.dropna(subset=['Hardwork'], inplace = True)\n",
    "mydata.dropna(subset=['Skills'], inplace = True)\n",
    "for x in mydata.index:\n",
    "  if mydata.loc[x, \"Hardwork\"] > 100:\n",
    "    mydata.drop(x, inplace = True)\n",
    "for x in mydata.index:\n",
    "  if mydata.loc[x, \"Skills\"] > 100:\n",
    "    mydata.drop(x, inplace = True)\n",
    "mydata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 460 entries, 0 to 459\n",
      "Data columns (total 2 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Hardwork  460 non-null    float64\n",
      " 1   Skills    460 non-null    float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 30.8 KB\n",
      "0      False\n",
      "1      False\n",
      "2      False\n",
      "3      False\n",
      "4      False\n",
      "       ...  \n",
      "455    False\n",
      "456    False\n",
      "457    False\n",
      "458    False\n",
      "459    False\n",
      "Length: 460, dtype: bool\n"
     ]
    }
   ],
   "source": [
    "mydata.info()\n",
    "print(mydata.duplicated())\n",
    "mydata.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hardwork</th>\n",
       "      <td>460.0</td>\n",
       "      <td>47.019178</td>\n",
       "      <td>24.895360</td>\n",
       "      <td>0.712341</td>\n",
       "      <td>25.287181</td>\n",
       "      <td>48.519039</td>\n",
       "      <td>67.467031</td>\n",
       "      <td>97.618528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skills</th>\n",
       "      <td>460.0</td>\n",
       "      <td>38.439590</td>\n",
       "      <td>26.136017</td>\n",
       "      <td>1.447058</td>\n",
       "      <td>13.987334</td>\n",
       "      <td>38.515372</td>\n",
       "      <td>60.161463</td>\n",
       "      <td>95.704652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count       mean        std       min        25%        50%  \\\n",
       "Hardwork  460.0  47.019178  24.895360  0.712341  25.287181  48.519039   \n",
       "Skills    460.0  38.439590  26.136017  1.447058  13.987334  38.515372   \n",
       "\n",
       "                75%        max  \n",
       "Hardwork  67.467031  97.618528  \n",
       "Skills    60.161463  95.704652  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "mydata.plot(kind = 'scatter', x = 'Hardwork', y = 'Skills')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scale the data\n",
    "from scipy.stats import zscore\n",
    "\n",
    "mydata_z = mydata.apply(zscore)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>Hardwork</th>\n",
       "      <th>Skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.831080</td>\n",
       "      <td>-1.237363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.235035</td>\n",
       "      <td>0.400552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.825851</td>\n",
       "      <td>-0.730837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.232364</td>\n",
       "      <td>0.199577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.078779</td>\n",
       "      <td>1.399542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.562602</td>\n",
       "      <td>1.374199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.338896</td>\n",
       "      <td>1.043153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.932610</td>\n",
       "      <td>0.703361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.060593</td>\n",
       "      <td>0.744035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.086345</td>\n",
       "      <td>0.781817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hardwork    Skills\n",
       "0 -1.831080 -1.237363\n",
       "1  0.235035  0.400552\n",
       "2  0.825851 -0.730837\n",
       "3  0.232364  0.199577\n",
       "4 -0.078779  1.399542\n",
       "5  1.562602  1.374199\n",
       "6 -0.338896  1.043153\n",
       "7 -0.932610  0.703361\n",
       "8 -0.060593  0.744035\n",
       "9 -0.086345  0.781817"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata_z.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Selecting k with elbow method')"
      ]
     },
     "execution_count": 90,
     "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": [
    "#finding optimal number of clusters \n",
    "from scipy.spatial.distance import cdist\n",
    "clusters=range(1,15)\n",
    "meanDistortions=[]\n",
    "\n",
    "for k in clusters:\n",
    "    model=KMeans(n_clusters=k)\n",
    "    model.fit(mydata)\n",
    "    prediction=model.predict(mydata)\n",
    "    meanDistortions.append(sum(np.min(cdist(mydata, model.cluster_centers_, 'euclidean'), axis=1)) / mydata.shape[0])\n",
    "    \n",
    "plt.plot(clusters, meanDistortions, '*-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Average Distortion')\n",
    "plt.title('Selecting k with elbow method')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "#set the value of k=4\n",
    "kmeans = KMeans(n_clusters=4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=4)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(mydata_z)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "centroids=kmeans.cluster_centers_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.21311716,  0.74382599],\n",
       "       [ 0.93296744, -0.80919971],\n",
       "       [-1.33074893, -1.01342853],\n",
       "       [ 1.35542865,  1.46490646]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "centroid_df = pd.DataFrame(centroids, columns = list(mydata_z))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Hardwork    Skills\n",
      "0 -0.213117  0.743826\n",
      "1  0.932967 -0.809200\n",
      "2 -1.330749 -1.013429\n",
      "3  1.355429  1.464906\n"
     ]
    }
   ],
   "source": [
    "print(centroid_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 1, 0, 0, 3, 0, 0, 0, 0, 1, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 3, 1, 0, 2, 2, 0, 2, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       0, 1, 1, 1, 2, 0, 1, 0, 0, 2, 1, 1, 1, 2, 0, 2, 0, 3, 1, 1, 2, 2,\n",
       "       0, 1, 2, 2, 0, 2, 3, 1, 2, 0, 0, 2, 3, 3, 2, 1, 2, 1, 1, 0, 1, 0,\n",
       "       3, 3, 1, 1, 0, 2, 2, 0, 3, 3, 0, 2, 1, 2, 0, 0, 2, 1, 2, 0, 1, 2,\n",
       "       1, 0, 2, 1, 2, 3, 0, 2, 0, 1, 0, 1, 3, 1, 3, 0, 2, 2, 2, 0, 0, 2,\n",
       "       2, 0, 0, 3, 3, 1, 0, 1, 3, 2, 0, 0, 3, 1, 2, 1, 1, 0, 1, 0, 2, 2,\n",
       "       0, 1, 1, 2, 1, 1, 0, 2, 3, 1, 1, 1, 1, 0, 1, 1, 3, 1, 2, 2, 3, 2,\n",
       "       2, 1, 0, 0, 1, 2, 1, 0, 0, 3, 2, 3, 0, 0, 1, 1, 1, 2, 0, 0, 1, 0,\n",
       "       2, 0, 0, 2, 2, 0, 0, 0, 1, 0, 1, 0, 2, 1, 1, 0, 0, 1, 1, 2, 1, 3,\n",
       "       3, 1, 0, 1, 0, 0, 0, 0, 2, 0, 1, 1, 0, 0, 0, 0, 2, 1, 2, 3, 0, 2,\n",
       "       2, 0, 2, 0, 1, 0, 3, 0, 1, 0, 0, 0, 1, 0, 1, 1, 3, 1, 0, 0, 0, 2,\n",
       "       0, 2, 1, 0, 3, 0, 1, 2, 0, 3, 1, 1, 1, 1, 2, 1, 0, 1, 2, 0, 0, 0,\n",
       "       0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 3, 2, 3, 0, 0, 0, 3,\n",
       "       0, 0, 1, 1, 1, 1, 0, 3, 1, 0, 3, 0, 1, 0, 0, 2, 0, 1, 2, 0, 1, 1,\n",
       "       1, 0, 2, 0, 0, 2, 0, 2, 3, 0, 2, 2, 0, 2, 2, 3, 2, 0, 0, 2, 1, 0,\n",
       "       2, 0, 1, 2, 2, 1, 1, 0, 1, 0, 2, 2, 0, 2, 2, 0, 0, 2, 2, 1, 1, 3,\n",
       "       3, 0, 3, 1, 2, 2, 0, 0, 0, 0, 3, 1, 1, 0, 0, 2, 1, 2, 3, 0, 3, 0,\n",
       "       1, 0, 0, 3, 2, 2, 1, 0, 2, 0, 3, 1, 1, 0, 1, 2, 1, 2, 1, 1, 3, 1,\n",
       "       2, 0, 1, 0, 1, 1, 0, 0, 0, 0, 2, 0, 2, 2, 1, 0, 0, 0, 1, 2, 1, 2,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 3, 1, 2, 1, 1, 2, 2, 0, 0, 0, 2, 0],\n",
       "      dtype=int32)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a new dataframe only for labels and concerting it into categorical variable\n",
    "df_labels = pd.DataFrame(kmeans.labels_, columns = list(['labels']))\n",
    "\n",
    "df_labels['labels'] = df_labels['labels'].astype('category')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "#joining the label dataframe with dataframe\n",
    "df_labeled = mydata.join(df_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>Hardwork</th>\n",
       "      <th>Skills</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.483358</td>\n",
       "      <td>6.135020</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52.864104</td>\n",
       "      <td>48.897037</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67.556683</td>\n",
       "      <td>19.359183</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52.797684</td>\n",
       "      <td>43.650076</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45.060080</td>\n",
       "      <td>74.978268</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>48.414533</td>\n",
       "      <td>53.807818</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>49.534222</td>\n",
       "      <td>54.425778</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>30.457458</td>\n",
       "      <td>40.396009</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>2.277720</td>\n",
       "      <td>5.085407</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>37.012233</td>\n",
       "      <td>51.093717</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>460 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Hardwork     Skills labels\n",
       "0     1.483358   6.135020      2\n",
       "1    52.864104  48.897037      0\n",
       "2    67.556683  19.359183      1\n",
       "3    52.797684  43.650076      0\n",
       "4    45.060080  74.978268      0\n",
       "..         ...        ...    ...\n",
       "455  48.414533  53.807818      0\n",
       "456  49.534222  54.425778      0\n",
       "457  30.457458  40.396009      0\n",
       "458   2.277720   5.085407      2\n",
       "459  37.012233  51.093717      0\n",
       "\n",
       "[460 rows x 3 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_analysis = (df_labeled.groupby(['labels'],axis=0)).head(4177)\n",
    "df_analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    186\n",
       "1    124\n",
       "2    104\n",
       "3     46\n",
       "Name: labels, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_labeled['labels'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=4)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km = KMeans(n_clusters=4)\n",
    "km.fit(mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f93232477f0>"
      ]
     },
     "execution_count": 103,
     "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": [
    "df0=df_labeled[df_labeled.labels==0]\n",
    "df1=df_labeled[df_labeled.labels==1]\n",
    "df2=df_labeled[df_labeled.labels==2]\n",
    "df3=df_labeled[df_labeled.labels==3]\n",
    "plt.scatter(df0.Hardwork,df0.Skills,color ='red',label='Tier D')\n",
    "plt.scatter(df1.Hardwork,df1.Skills,color ='blue',label='Tier C')\n",
    "plt.scatter(df2.Hardwork,df2.Skills,color ='green',label='Tier A')\n",
    "plt.scatter(df3.Hardwork,df3.Skills,color ='yellow',label='Tier B')\n",
    "plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='black',marker='*',label='centroid')\n",
    "plt.xlabel('Hardwork')\n",
    "plt.ylabel('Skills')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}