Kmeans_clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>chol</th>\n",
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       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>241</td>\n",
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       "      <td>131</td>\n",
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       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  chol\n",
       "0     63    1   233\n",
       "1     37    1   250\n",
       "2     41    0   204\n",
       "3     56    1   236\n",
       "4     57    0   354\n",
       "..   ...  ...   ...\n",
       "298   57    0   241\n",
       "299   45    1   264\n",
       "300   68    1   193\n",
       "301   57    1   131\n",
       "302   57    0   236\n",
       "\n",
       "[303 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medical_df=pd.read_csv(\"D:\\\\python odyssey\\\\day8\\\\kmeans\\\\heart_dataset_mini.csv\")\n",
    "medical_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>chol</th>\n",
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       "<p>303 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "     age  chol\n",
       "0     63   233\n",
       "1     37   250\n",
       "2     41   204\n",
       "3     56   236\n",
       "4     57   354\n",
       "..   ...   ...\n",
       "298   57   241\n",
       "299   45   264\n",
       "300   68   193\n",
       "301   57   131\n",
       "302   57   236\n",
       "\n",
       "[303 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=medical_df[[\"age\",\"chol\"]]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age     0\n",
       "chol    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>chol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>246.264026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>51.830751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>126.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>240.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>274.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>564.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age        chol\n",
       "count  303.000000  303.000000\n",
       "mean    54.366337  246.264026\n",
       "std      9.082101   51.830751\n",
       "min     29.000000  126.000000\n",
       "25%     47.500000  211.000000\n",
       "50%     55.000000  240.000000\n",
       "75%     61.000000  274.500000\n",
       "max     77.000000  564.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Visualize the features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'age')"
      ]
     },
     "execution_count": 6,
     "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(X.chol,X.age)\n",
    "plt.xlabel(\"Chol\")\n",
    "plt.ylabel(\"age\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 Scale the feature values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9.52196602e-01, -2.56333707e-01],\n",
       "       [-1.91531289e+00,  7.21994916e-02],\n",
       "       [-1.47415758e+00, -8.16772694e-01],\n",
       "       [ 1.80174816e-01, -1.98357261e-01],\n",
       "       [ 2.90463642e-01,  2.08204965e+00],\n",
       "       [ 2.90463642e-01, -1.04867848e+00],\n",
       "       [ 1.80174816e-01,  9.22520713e-01],\n",
       "       [-1.14329110e+00,  3.23430762e-01],\n",
       "       [-2.60980491e-01, -9.13400106e-01],\n",
       "       [ 2.90463642e-01, -1.51249006e+00],\n",
       "       [-4.04028375e-02, -1.40380814e-01],\n",
       "       [-7.02135797e-01,  5.55336549e-01],\n",
       "       [-5.91846970e-01,  3.81407208e-01],\n",
       "       [ 1.06248543e+00, -6.81494318e-01],\n",
       "       [ 4.00752469e-01,  7.09940408e-01],\n",
       "       [-4.81558144e-01, -5.26890460e-01],\n",
       "       [ 4.00752469e-01,  1.81149290e+00],\n",
       "       [ 1.28306308e+00, -3.91612084e-01],\n",
       "       [-1.25357993e+00,  1.42230447e-02],\n",
       "       [ 1.61392956e+00, -1.40380814e-01],\n",
       "       [ 5.11041295e-01, -2.37008225e-01],\n",
       "       [-1.14329110e+00, -2.56333707e-01],\n",
       "       [-1.36386876e+00, -3.91612084e-01],\n",
       "       [ 7.31618948e-01, -6.30788845e-02],\n",
       "       [-1.58444641e+00, -9.13400106e-01],\n",
       "       [ 1.83450721e+00,  1.07712457e+00],\n",
       "       [ 5.11041295e-01, -6.62168836e-01],\n",
       "       [-3.71269317e-01, -1.37721168e+00],\n",
       "       [ 1.17277425e+00,  3.29955504e+00],\n",
       "       [-1.50691664e-01, -9.52051070e-01],\n",
       "       [-1.47415758e+00, -9.32725588e-01],\n",
       "       [ 1.17277425e+00, -1.33856072e+00],\n",
       "       [-1.14329110e+00, -5.26890460e-01],\n",
       "       [-4.04028375e-02,  5.16685585e-01],\n",
       "       [-3.71269317e-01, -6.42843353e-01],\n",
       "       [-9.22713450e-01, -1.33856072e+00],\n",
       "       [-4.04028375e-02,  1.11577554e+00],\n",
       "       [-4.04028375e-02, -2.75659190e-01],\n",
       "       [ 1.17277425e+00,  4.39383655e-01],\n",
       "       [ 1.17277425e+00,  2.19800254e+00],\n",
       "       [-3.71269317e-01,  1.19307747e+00],\n",
       "       [-7.02135797e-01, -2.44279199e-02],\n",
       "       [-1.03300228e+00, -7.39470765e-01],\n",
       "       [-1.50691664e-01,  3.42756244e-01],\n",
       "       [-1.69473524e+00,  1.44430873e+00],\n",
       "       [-2.60980491e-01,  1.52161066e+00],\n",
       "       [-1.14329110e+00, -2.17682743e-01],\n",
       "       [-8.12424623e-01,  2.07477868e-01],\n",
       "       [-1.50691664e-01, -5.84866907e-01],\n",
       "       [-1.50691664e-01, -2.37008225e-01],\n",
       "       [-3.71269317e-01,  1.88152385e-01],\n",
       "       [ 1.28306308e+00,  1.07712457e+00],\n",
       "       [ 8.41907775e-01, -2.94984672e-01],\n",
       "       [-1.14329110e+00, -2.03427808e+00],\n",
       "       [ 9.52196602e-01,  1.10850456e-01],\n",
       "       [-2.60980491e-01, -8.74749141e-01],\n",
       "       [-7.02135797e-01, -4.68914013e-01],\n",
       "       [-1.03300228e+00,  2.65454315e-01],\n",
       "       [-2.24617937e+00, -1.24193330e+00],\n",
       "       [ 2.90463642e-01,  1.09645005e+00],\n",
       "       [ 1.83450721e+00,  3.62081726e-01],\n",
       "       [-4.04028375e-02,  1.21240295e+00],\n",
       "       [-2.60980491e-01, -1.16463138e+00],\n",
       "       [-1.47415758e+00, -8.36098177e-01],\n",
       "       [ 4.00752469e-01, -6.81494318e-01],\n",
       "       [-2.13589054e+00, -1.22260782e+00],\n",
       "       [-3.71269317e-01, -4.68914013e-01],\n",
       "       [-1.03300228e+00, -2.37008225e-01],\n",
       "       [-1.14329110e+00, -5.07564977e-01],\n",
       "       [ 8.41907775e-01, -7.20145283e-01],\n",
       "       [-4.04028375e-02,  2.26803350e-01],\n",
       "       [-3.71269317e-01, -3.72286601e-01],\n",
       "       [-2.79762350e+00, -8.16772694e-01],\n",
       "       [-3.71269317e-01,  2.84779797e-01],\n",
       "       [-1.25357993e+00, -6.42843353e-01],\n",
       "       [ 6.98859891e-02,  7.21994916e-02],\n",
       "       [-3.71269317e-01, -2.44279199e-02],\n",
       "       [ 5.11041295e-01, -4.88239495e-01],\n",
       "       [-2.60980491e-01, -7.97447212e-01],\n",
       "       [ 4.00752469e-01, -1.21055331e-01],\n",
       "       [-1.47415758e+00,  7.21994916e-02],\n",
       "       [-1.03300228e+00,  1.19307747e+00],\n",
       "       [ 6.21330122e-01,  1.38633229e+00],\n",
       "       [-2.60980491e-01,  9.99822642e-01],\n",
       "       [-1.36386876e+00,  3.62081726e-01],\n",
       "       [ 1.39335191e+00,  6.14040093e+00],\n",
       "       [ 1.50364073e+00,  5.93987514e-01],\n",
       "       [-9.22713450e-01, -9.52051070e-01],\n",
       "       [-4.04028375e-02, -6.23517871e-01],\n",
       "       [ 4.00752469e-01,  3.35485270e-02],\n",
       "       [-7.02135797e-01,  1.68826903e-01],\n",
       "       [ 2.90463642e-01, -7.58796247e-01],\n",
       "       [-2.60980491e-01, -4.49588530e-01],\n",
       "       [-4.04028375e-02,  8.06567819e-01],\n",
       "       [-1.03300228e+00, -1.66709392e+00],\n",
       "       [-1.50691664e-01, -3.91612084e-01],\n",
       "       [ 8.41907775e-01,  2.85506894e+00],\n",
       "       [-2.60980491e-01, -2.56333707e-01],\n",
       "       [-1.25357993e+00,  1.32835584e+00],\n",
       "       [-1.50691664e-01, -5.10243757e-03],\n",
       "       [-1.36386876e+00, -4.37534022e-02],\n",
       "       [ 5.11041295e-01,  4.58709138e-01],\n",
       "       [ 9.52196602e-01, -9.90702035e-01],\n",
       "       [-1.36386876e+00, -1.21055331e-01],\n",
       "       [-4.81558144e-01, -9.71376553e-01],\n",
       "       [ 1.50364073e+00, -6.81494318e-01],\n",
       "       [ 1.61392956e+00, -2.37008225e-01],\n",
       "       [-1.03300228e+00, -1.98357261e-01],\n",
       "       [-4.81558144e-01, -4.37534022e-02],\n",
       "       [-4.81558144e-01,  1.49501421e-01],\n",
       "       [ 1.06248543e+00,  1.52161066e+00],\n",
       "       [ 2.90463642e-01, -2.32416031e+00],\n",
       "       [ 1.06248543e+00,  1.28970488e+00],\n",
       "       [-1.25357993e+00, -6.81494318e-01],\n",
       "       [ 6.98859891e-02,  3.04105279e-01],\n",
       "       [-1.91531289e+00, -6.04192389e-01],\n",
       "       [-1.47415758e+00, -6.23517871e-01],\n",
       "       [ 1.80174816e-01, -1.02935300e+00],\n",
       "       [-9.22713450e-01, -8.16772694e-01],\n",
       "       [-9.22713450e-01, -6.30788845e-02],\n",
       "       [ 1.06248543e+00,  1.09645005e+00],\n",
       "       [ 5.11041295e-01,  4.78034620e-01],\n",
       "       [-1.47415758e+00,  4.20058173e-01],\n",
       "       [-4.04028375e-02,  4.00732691e-01],\n",
       "       [-1.69473524e+00, -9.13400106e-01],\n",
       "       [-2.24617937e+00, -7.00819800e-01],\n",
       "       [-8.12424623e-01, -8.16772694e-01],\n",
       "       [ 1.39335191e+00,  5.93987514e-01],\n",
       "       [-2.60980491e-01, -9.71376553e-01],\n",
       "       [ 2.16537369e+00,  4.39383655e-01],\n",
       "       [-4.04028375e-02, -8.74749141e-01],\n",
       "       [-5.91846970e-01,  4.78034620e-01],\n",
       "       [-1.36386876e+00,  9.41846195e-01],\n",
       "       [-1.47415758e+00, -2.17682743e-01],\n",
       "       [-1.47415758e+00,  1.15442650e+00],\n",
       "       [-5.91846970e-01,  4.39383655e-01],\n",
       "       [ 6.21330122e-01, -1.31923523e+00],\n",
       "       [ 8.41907775e-01, -7.39470765e-01],\n",
       "       [ 2.90463642e-01, -8.74749141e-01],\n",
       "       [ 1.06248543e+00,  3.23430762e-01],\n",
       "       [-3.71269317e-01,  9.41846195e-01],\n",
       "       [-1.25357993e+00,  1.09645005e+00],\n",
       "       [-1.36386876e+00, -7.20145283e-01],\n",
       "       [ 1.39335191e+00, -4.49588530e-01],\n",
       "       [ 2.38595135e+00, -9.52051070e-01],\n",
       "       [ 1.72421839e+00, -2.44279199e-02],\n",
       "       [-1.14329110e+00, -8.24043668e-02],\n",
       "       [ 6.21330122e-01, -1.21055331e-01],\n",
       "       [-1.14329110e+00, -3.91612084e-01],\n",
       "       [-1.36386876e+00, -1.28058427e+00],\n",
       "       [ 1.28306308e+00, -3.52961119e-01],\n",
       "       [ 1.83450721e+00, -1.87967422e+00],\n",
       "       [ 1.06248543e+00, -3.72286601e-01],\n",
       "       [ 1.28306308e+00,  6.13312996e-01],\n",
       "       [-1.69473524e+00, -5.07564977e-01],\n",
       "       [ 4.00752469e-01, -9.52051070e-01],\n",
       "       [-8.12424623e-01,  1.30175939e-01],\n",
       "       [-2.13589054e+00, -1.04867848e+00],\n",
       "       [ 4.00752469e-01, -5.07564977e-01],\n",
       "       [ 1.80174816e-01, -4.88239495e-01],\n",
       "       [ 1.80174816e-01, -1.21055331e-01],\n",
       "       [ 6.98859891e-02,  1.85014386e+00],\n",
       "       [-1.47415758e+00, -1.72507036e+00],\n",
       "       [-1.80502406e+00, -1.37721168e+00],\n",
       "       [-1.80502406e+00, -1.37721168e+00],\n",
       "       [ 1.39335191e+00,  7.67916854e-01],\n",
       "       [ 1.39335191e+00, -3.33635637e-01],\n",
       "       [ 8.41907775e-01,  4.20058173e-01],\n",
       "       [ 9.52196602e-01,  1.49501421e-01],\n",
       "       [-1.50691664e-01, -8.36098177e-01],\n",
       "       [ 1.80174816e-01,  1.88152385e-01],\n",
       "       [-7.02135797e-01, -3.33635637e-01],\n",
       "       [ 4.00752469e-01,  7.29265890e-01],\n",
       "       [ 4.00752469e-01, -4.30263048e-01],\n",
       "       [ 6.21330122e-01, -7.78121730e-01],\n",
       "       [-1.58444641e+00, -1.53181554e+00],\n",
       "       [ 6.21330122e-01, -3.14310154e-01],\n",
       "       [ 1.06248543e+00,  1.71486549e+00],\n",
       "       [-1.25357993e+00, -1.33856072e+00],\n",
       "       [ 2.90463642e-01,  5.74662031e-01],\n",
       "       [ 6.98859891e-02,  2.06272417e+00],\n",
       "       [ 1.17277425e+00, -4.10937566e-01],\n",
       "       [ 7.31618948e-01,  1.61823808e+00],\n",
       "       [ 4.00752469e-01, -3.14310154e-01],\n",
       "       [-4.81558144e-01, -6.30788845e-02],\n",
       "       [-1.14329110e+00,  8.45218784e-01],\n",
       "       [ 6.21330122e-01,  1.30175939e-01],\n",
       "       [-4.04028375e-02,  3.81407208e-01],\n",
       "       [-4.81558144e-01, -2.56333707e-01],\n",
       "       [-1.47415758e+00, -1.43518813e+00],\n",
       "       [-3.71269317e-01,  1.13510102e+00],\n",
       "       [ 4.00752469e-01, -5.84866907e-01],\n",
       "       [-4.04028375e-02, -1.12598041e+00],\n",
       "       [ 6.21330122e-01,  6.90614925e-01],\n",
       "       [ 6.21330122e-01, -1.18395686e+00],\n",
       "       [ 5.11041295e-01,  1.54093615e+00],\n",
       "       [-9.22713450e-01, -2.94984672e-01],\n",
       "       [ 1.39335191e+00,  1.49501421e-01],\n",
       "       [ 8.41907775e-01,  4.00732691e-01],\n",
       "       [ 1.17277425e+00,  3.35485270e-02],\n",
       "       [-1.14329110e+00, -9.52051070e-01],\n",
       "       [ 6.21330122e-01,  2.26803350e-01],\n",
       "       [ 4.00752469e-01,  4.58709138e-01],\n",
       "       [ 1.50364073e+00,  5.36011067e-01],\n",
       "       [ 8.41907775e-01, -1.58979199e+00],\n",
       "       [-2.60980491e-01,  1.68826903e-01],\n",
       "       [ 5.11041295e-01, -1.40380814e-01],\n",
       "       [ 6.21330122e-01,  2.26803350e-01],\n",
       "       [-5.91846970e-01, -1.12598041e+00],\n",
       "       [ 5.11041295e-01, -1.33856072e+00],\n",
       "       [ 2.90463642e-01, -3.33635637e-01],\n",
       "       [ 7.31618948e-01,  2.65454315e-01],\n",
       "       [-1.69473524e+00, -5.26890460e-01],\n",
       "       [ 7.31618948e-01,  1.17375198e+00],\n",
       "       [ 1.80174816e-01,  5.28740093e-02],\n",
       "       [-1.25357993e+00,  1.83081838e+00],\n",
       "       [ 8.41907775e-01,  3.23430762e-01],\n",
       "       [ 9.52196602e-01,  1.61823808e+00],\n",
       "       [ 1.17277425e+00,  1.49501421e-01],\n",
       "       [-7.02135797e-01,  1.88152385e-01],\n",
       "       [ 9.52196602e-01,  3.10630021e+00],\n",
       "       [ 6.98859891e-02, -5.65541424e-01],\n",
       "       [ 1.17277425e+00,  6.90614925e-01],\n",
       "       [ 1.80174816e-01,  8.06567819e-01],\n",
       "       [-4.04028375e-02, -1.40380814e-01],\n",
       "       [ 1.72421839e+00, -1.39653716e+00],\n",
       "       [ 8.41907775e-01,  6.71289443e-01],\n",
       "       [-2.13589054e+00, -9.32725588e-01],\n",
       "       [ 5.11041295e-01,  8.06567819e-01],\n",
       "       [ 1.06248543e+00,  1.21240295e+00],\n",
       "       [-8.12424623e-01, -6.30788845e-02],\n",
       "       [ 2.90463642e-01,  8.25893301e-01],\n",
       "       [ 6.98859891e-02,  8.25893301e-01],\n",
       "       [ 1.06248543e+00, -5.10243757e-03],\n",
       "       [ 1.72421839e+00,  1.46363422e+00],\n",
       "       [-3.71269317e-01,  1.01914812e+00],\n",
       "       [ 4.00752469e-01,  1.03847361e+00],\n",
       "       [ 6.21330122e-01,  9.03195231e-01],\n",
       "       [ 2.49624017e+00,  1.11577554e+00],\n",
       "       [-2.13589054e+00,  6.90614925e-01],\n",
       "       [ 1.72421839e+00,  4.39383655e-01],\n",
       "       [ 5.11041295e-01,  5.28740093e-02],\n",
       "       [ 1.06248543e+00, -6.62168836e-01],\n",
       "       [ 2.90463642e-01,  5.36011067e-01],\n",
       "       [ 1.80174816e-01, -1.20328234e+00],\n",
       "       [-7.02135797e-01,  5.36011067e-01],\n",
       "       [ 1.80174816e-01,  3.14495118e+00],\n",
       "       [ 1.28306308e+00, -5.10243757e-03],\n",
       "       [-4.04028375e-02,  7.09940408e-01],\n",
       "       [ 1.61392956e+00,  1.49501421e-01],\n",
       "       [-3.71269317e-01,  9.99822642e-01],\n",
       "       [-1.25357993e+00,  1.42230447e-02],\n",
       "       [ 8.41907775e-01,  9.22520713e-01],\n",
       "       [ 1.39335191e+00,  1.01914812e+00],\n",
       "       [ 5.11041295e-01,  5.16685585e-01],\n",
       "       [-1.03300228e+00,  1.21240295e+00],\n",
       "       [ 4.00752469e-01,  2.46128832e-01],\n",
       "       [-4.81558144e-01, -8.94074623e-01],\n",
       "       [ 8.41907775e-01, -4.37534022e-02],\n",
       "       [-1.80502406e+00, -2.94984672e-01],\n",
       "       [ 1.28306308e+00, -3.52961119e-01],\n",
       "       [-2.60980491e-01, -3.14310154e-01],\n",
       "       [-1.50691664e-01,  6.90614925e-01],\n",
       "       [ 9.52196602e-01,  4.39383655e-01],\n",
       "       [-4.04028375e-02, -7.78121730e-01],\n",
       "       [ 1.28306308e+00, -6.62168836e-01],\n",
       "       [ 6.98859891e-02,  1.56026163e+00],\n",
       "       [-5.91846970e-01, -1.87967422e+00],\n",
       "       [-4.04028375e-02,  7.67916854e-01],\n",
       "       [ 1.80174816e-01,  7.09940408e-01],\n",
       "       [-9.22713450e-01,  5.28740093e-02],\n",
       "       [ 7.31618948e-01, -2.37008225e-01],\n",
       "       [ 1.39335191e+00, -1.79031778e-01],\n",
       "       [ 4.00752469e-01, -2.37008225e-01],\n",
       "       [-8.12424623e-01,  5.55336549e-01],\n",
       "       [-2.60980491e-01, -6.62168836e-01],\n",
       "       [ 4.00752469e-01, -5.46215942e-01],\n",
       "       [ 2.90463642e-01,  2.84779797e-01],\n",
       "       [ 4.00752469e-01,  1.40565777e+00],\n",
       "       [ 7.31618948e-01, -1.55114102e+00],\n",
       "       [-1.36386876e+00,  1.32835584e+00],\n",
       "       [-2.60980491e-01, -8.16772694e-01],\n",
       "       [ 5.11041295e-01, -5.46215942e-01],\n",
       "       [-1.58444641e+00, -4.49588530e-01],\n",
       "       [ 7.31618948e-01, -7.58796247e-01],\n",
       "       [-9.22713450e-01,  1.25105391e+00],\n",
       "       [ 5.11041295e-01, -8.16772694e-01],\n",
       "       [ 2.90463642e-01, -2.75659190e-01],\n",
       "       [ 2.90463642e-01,  1.71486549e+00],\n",
       "       [ 6.98859891e-02, -7.97447212e-01],\n",
       "       [ 7.31618948e-01, -8.36098177e-01],\n",
       "       [ 4.00752469e-01,  1.38633229e+00],\n",
       "       [ 4.00752469e-01, -4.10937566e-01],\n",
       "       [ 1.39335191e+00, -6.62168836e-01],\n",
       "       [-1.14329110e+00, -1.49316457e+00],\n",
       "       [ 9.52196602e-01, -1.14530589e+00],\n",
       "       [ 9.52196602e-01, -9.52051070e-01],\n",
       "       [ 5.11041295e-01, -1.35788620e+00],\n",
       "       [ 2.90463642e-01, -1.01729849e-01],\n",
       "       [-1.03300228e+00,  3.42756244e-01],\n",
       "       [ 1.50364073e+00, -1.02935300e+00],\n",
       "       [ 2.90463642e-01, -2.22753290e+00],\n",
       "       [ 2.90463642e-01, -1.98357261e-01]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler=StandardScaler()\n",
    "X=scaler.fit_transform(X)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 fit Model for Different K "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"200\"\n",
       "            src=\"https://www.youtube.com/embed/8luMuQJyxdc\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x189d1bb6908>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "\n",
    "YouTubeVideo('8luMuQJyxdc', width=400, height=200)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"200\"\n",
       "            src=\"https://www.youtube.com/embed/Kmrxc3NpNtQ\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x189d1b494c8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo('Kmrxc3NpNtQ', width=400, height=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(2,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- inertia 375.14224818605464 silhoutte 0.35642725100730843\n",
      "---- inertia 256.9598573689617 silhoutte 0.3603326502768412\n",
      "---- inertia 207.3588011627035 silhoutte 0.34256800544161103\n",
      "---- inertia 165.2567106271851 silhoutte 0.351113055666146\n",
      "---- inertia 138.3244614208023 silhoutte 0.3556814561758239\n",
      "---- inertia 118.25985139788774 silhoutte 0.3525461911915897\n",
      "---- inertia 105.34245769394377 silhoutte 0.3378717416607325\n",
      "---- inertia 92.3009997241576 silhoutte 0.3592517442383332\n",
      "---- inertia 81.78131322337609 silhoutte 0.3526927917769282\n",
      "---- inertia 75.32536663369663 silhoutte 0.34678236946104796\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model_labels={}\n",
    "centroids={}\n",
    "i_score=[]\n",
    "s_score=[]\n",
    "for k in range(2,12):\n",
    "    #model building\n",
    "    model=KMeans(n_clusters=k)\n",
    "    #model fit \n",
    "    model=model.fit(X)\n",
    "    \n",
    "    # get the output labels\n",
    "    labels=model.labels_\n",
    "    model_labels[k]=labels\n",
    "    \n",
    "    #inertia\n",
    "    i_score.append(model.inertia_)\n",
    "    \n",
    "    #silhouette\n",
    "    s_score.append(silhouette_score(X,labels))\n",
    "    \n",
    "    #centroids\n",
    "    centroids[k]=model.cluster_centers_\n",
    "    \n",
    "    print(f\"---- inertia {model.inertia_} silhoutte {silhouette_score(X,labels)}\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6 Plot the inertia and Silhouette score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'K')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(2,1,sharex=True)\n",
    "plt.subplot(211) #row,solumn,plot\n",
    "plt.plot(range(2,12),i_score,\"o--\")\n",
    "plt.ylabel(\"inertia\")\n",
    "\n",
    "plt.subplot(212) #row,solumn,plot\n",
    "plt.plot(range(2,12),s_score,\"o--\")\n",
    "plt.ylabel(\"silhouette\")\n",
    "plt.xlabel(\"K\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7 Get the cluster label of the best model and plot the clusters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "K_labels=model_labels[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_centroid=centroids[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x189d21ddc08>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(5,5))\n",
    "# 1st cluster\n",
    "plt.scatter(X[K_labels == 0,0],X[K_labels == 0,1],c=\"green\",label=\"cluster1\")\n",
    "#2nd cluster\n",
    "plt.scatter(X[K_labels == 1,0],X[K_labels == 1,1],c=\"yellow\",label=\"cluster2\")\n",
    "#3rd cluster\n",
    "plt.scatter(X[K_labels == 2,0],X[K_labels == 2,1],c=\"blue\",label=\"cluster3\")\n",
    "#3 cluster centroids\n",
    "plt.scatter(k_centroid[:,0],k_centroid[:,1],c=\"r\",label=\"centroids\")\n",
    "plt.xlabel(\"chol\")\n",
    "plt.ylabel(\"age\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>age</th>\n",
       "      <th>chol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  chol\n",
       "0     63   233\n",
       "1     37   250\n",
       "2     41   204\n",
       "3     56   236\n",
       "4     57   354\n",
       "..   ...   ...\n",
       "298   57   241\n",
       "299   45   264\n",
       "300   68   193\n",
       "301   57   131\n",
       "302   57   236\n",
       "\n",
       "[303 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis=medical_df[[\"age\",\"chol\"]].copy()\n",
    "analysis"
   ]
  },
  {
   "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>age</th>\n",
       "      <th>chol</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>233</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>250</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>204</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>354</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>264</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>193</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  chol  label\n",
       "0     63   233      0\n",
       "1     37   250      1\n",
       "2     41   204      1\n",
       "3     56   236      0\n",
       "4     57   354      2\n",
       "..   ...   ...    ...\n",
       "298   57   241      0\n",
       "299   45   264      1\n",
       "300   68   193      0\n",
       "301   57   131      0\n",
       "302   57   236      0\n",
       "\n",
       "[303 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis[\"label\"]=K_labels\n",
    "analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>age</th>\n",
       "      <th>chol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59.470588</td>\n",
       "      <td>218.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43.920000</td>\n",
       "      <td>230.530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>59.571429</td>\n",
       "      <td>304.630952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             age        chol\n",
       "label                       \n",
       "0      59.470588  218.285714\n",
       "1      43.920000  230.530000\n",
       "2      59.571429  304.630952"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis.groupby(\"label\")[\"age\",\"chol\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 30, 150]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "person_01=np.array([[30,150]])\n",
    "person_01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.68733467, -1.86034874]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person_01=scaler.transform(person_01)\n",
    "person_01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=KMeans(n_clusters=3)\n",
    "#model fit \n",
    "model=model.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(person_01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "0- 1st cluster ,1-2nd cluster ,2 - 3rd cluster \n"
   ]
  }
 ],
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