UTS_DataSchience_IV.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "## Load Data Set\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"./UTS_DataSchience_IV_Dataset.csv\",delimiter=\";\")\n",
    "df.head(5)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item  X  Y Cluster\n",
       "0    A  1  3      C1\n",
       "1    B  3  3      C2\n",
       "2    C  4  3      C2\n",
       "3    D  5  3      C2\n",
       "4    E  1  2      C1"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "## Preprocessing Dataset\n",
    "X = df[['X','Y']]\n",
    "X.head(10)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X  Y\n",
       "0  1  3\n",
       "1  3  3\n",
       "2  4  3\n",
       "3  5  3\n",
       "4  1  2\n",
       "5  4  2\n",
       "6  1  1\n",
       "7  2  1"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "## Count Shilluete Score data A to G\n",
    "from sklearn.metrics import silhouette_score\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "K_cluster = [2,3,4,5]\n",
    "sse_score = []\n",
    "for k in K_cluster:\n",
    "    cluster = KMeans(n_clusters=k)\n",
    "    train_data = cluster.fit(X)\n",
    "\n",
    "    sse_score.append(train_data.inertia_)\n",
    "\n",
    "print(sse_score)\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[6.25, 3.75, 2.6666666666666665, 1.5]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "## Display an Elbow\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(K_cluster,sse_score,'bx-')\n",
    "plt.xlabel(\"Number of Cluster\")\n",
    "plt.ylabel(\"SSE\")\n",
    "plt.title(\"Elbow Method to showing the optimal number of cluster K\")\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Elbow Method to showing the optimal number of cluster K')"
      ]
     },
     "metadata": {},
     "execution_count": 12
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python",
   "version": "3.7.0",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.7.0 64-bit ('venv': venv)"
  },
  "interpreter": {
   "hash": "da768e2d208a493623333cafe6f92a3449634d992c4b6a0156652a6ac42758f7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}