clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "02cb505c",
   "metadata": {},
   "source": [
    "Perform clustering on multiple clusters. Check using the silhouette coefficient \n",
    "http://archive.ics.uci.edu/ml/datasets/Grammatical+Facial+Expressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c951fcae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0.0</th>\n",
       "      <th>0x</th>\n",
       "      <th>0y</th>\n",
       "      <th>0z</th>\n",
       "      <th>1x</th>\n",
       "      <th>1y</th>\n",
       "      <th>1z</th>\n",
       "      <th>2x</th>\n",
       "      <th>2y</th>\n",
       "      <th>2z</th>\n",
       "      <th>...</th>\n",
       "      <th>96z</th>\n",
       "      <th>97x</th>\n",
       "      <th>97y</th>\n",
       "      <th>97z</th>\n",
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       "      <th>98y</th>\n",
       "      <th>98z</th>\n",
       "      <th>99x</th>\n",
       "      <th>99y</th>\n",
       "      <th>99z</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.390385e+09</td>\n",
       "      <td>244.672</td>\n",
       "      <td>208.182</td>\n",
       "      <td>895</td>\n",
       "      <td>240.949</td>\n",
       "      <td>205.708</td>\n",
       "      <td>890</td>\n",
       "      <td>235.994</td>\n",
       "      <td>205.018</td>\n",
       "      <td>883</td>\n",
       "      <td>...</td>\n",
       "      <td>923</td>\n",
       "      <td>283.559</td>\n",
       "      <td>186.549</td>\n",
       "      <td>944</td>\n",
       "      <td>291.722</td>\n",
       "      <td>189.204</td>\n",
       "      <td>0</td>\n",
       "      <td>296.547</td>\n",
       "      <td>192.722</td>\n",
       "      <td>1019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.390385e+09</td>\n",
       "      <td>265.953</td>\n",
       "      <td>195.731</td>\n",
       "      <td>1016</td>\n",
       "      <td>262.556</td>\n",
       "      <td>193.301</td>\n",
       "      <td>1007</td>\n",
       "      <td>258.243</td>\n",
       "      <td>192.547</td>\n",
       "      <td>1001</td>\n",
       "      <td>...</td>\n",
       "      <td>1050</td>\n",
       "      <td>301.606</td>\n",
       "      <td>176.421</td>\n",
       "      <td>1069</td>\n",
       "      <td>308.776</td>\n",
       "      <td>180.199</td>\n",
       "      <td>0</td>\n",
       "      <td>312.501</td>\n",
       "      <td>184.498</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.390385e+09</td>\n",
       "      <td>272.755</td>\n",
       "      <td>192.786</td>\n",
       "      <td>1060</td>\n",
       "      <td>269.481</td>\n",
       "      <td>190.460</td>\n",
       "      <td>1056</td>\n",
       "      <td>265.371</td>\n",
       "      <td>189.756</td>\n",
       "      <td>1053</td>\n",
       "      <td>...</td>\n",
       "      <td>1097</td>\n",
       "      <td>307.299</td>\n",
       "      <td>174.126</td>\n",
       "      <td>1114</td>\n",
       "      <td>314.125</td>\n",
       "      <td>177.992</td>\n",
       "      <td>1140</td>\n",
       "      <td>317.529</td>\n",
       "      <td>182.324</td>\n",
       "      <td>1179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.390385e+09</td>\n",
       "      <td>281.890</td>\n",
       "      <td>190.533</td>\n",
       "      <td>1129</td>\n",
       "      <td>278.861</td>\n",
       "      <td>188.023</td>\n",
       "      <td>1129</td>\n",
       "      <td>274.899</td>\n",
       "      <td>187.213</td>\n",
       "      <td>1136</td>\n",
       "      <td>...</td>\n",
       "      <td>1167</td>\n",
       "      <td>315.525</td>\n",
       "      <td>176.009</td>\n",
       "      <td>1187</td>\n",
       "      <td>322.016</td>\n",
       "      <td>180.029</td>\n",
       "      <td>0</td>\n",
       "      <td>325.041</td>\n",
       "      <td>184.442</td>\n",
       "      <td>1252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.390385e+09</td>\n",
       "      <td>283.933</td>\n",
       "      <td>193.201</td>\n",
       "      <td>1159</td>\n",
       "      <td>280.955</td>\n",
       "      <td>190.877</td>\n",
       "      <td>1159</td>\n",
       "      <td>277.180</td>\n",
       "      <td>190.196</td>\n",
       "      <td>1167</td>\n",
       "      <td>...</td>\n",
       "      <td>1195</td>\n",
       "      <td>315.102</td>\n",
       "      <td>177.654</td>\n",
       "      <td>1216</td>\n",
       "      <td>321.384</td>\n",
       "      <td>181.259</td>\n",
       "      <td>1225</td>\n",
       "      <td>324.488</td>\n",
       "      <td>185.400</td>\n",
       "      <td>1284</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 301 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0.0       0x       0y    0z       1x       1y    1z       2x  \\\n",
       "0  1.390385e+09  244.672  208.182   895  240.949  205.708   890  235.994   \n",
       "1  1.390385e+09  265.953  195.731  1016  262.556  193.301  1007  258.243   \n",
       "2  1.390385e+09  272.755  192.786  1060  269.481  190.460  1056  265.371   \n",
       "3  1.390385e+09  281.890  190.533  1129  278.861  188.023  1129  274.899   \n",
       "4  1.390385e+09  283.933  193.201  1159  280.955  190.877  1159  277.180   \n",
       "\n",
       "        2y    2z  ...   96z      97x      97y   97z      98x      98y   98z  \\\n",
       "0  205.018   883  ...   923  283.559  186.549   944  291.722  189.204     0   \n",
       "1  192.547  1001  ...  1050  301.606  176.421  1069  308.776  180.199     0   \n",
       "2  189.756  1053  ...  1097  307.299  174.126  1114  314.125  177.992  1140   \n",
       "3  187.213  1136  ...  1167  315.525  176.009  1187  322.016  180.029     0   \n",
       "4  190.196  1167  ...  1195  315.102  177.654  1216  321.384  181.259  1225   \n",
       "\n",
       "       99x      99y   99z  \n",
       "0  296.547  192.722  1019  \n",
       "1  312.501  184.498     0  \n",
       "2  317.529  182.324  1179  \n",
       "3  325.041  184.442  1252  \n",
       "4  324.488  185.400  1284  \n",
       "\n",
       "[5 rows x 301 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "from sklearn.decomposition import PCA\n",
    "import warnings\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Data uploading \n",
    "df = pd.read_csv('a_affirmative_datapoints.txt', sep=' ', header=0)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f980d969",
   "metadata": {},
   "outputs": [],
   "source": [
    " # Check for missing values\n",
    "if df.isnull().sum().any():\n",
    "    print(\"Missing values found. Replacing with column mean.\")\n",
    "    df.fillna(df.mean(), inplace=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "09f8a2f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting numeric columns\n",
    "numeric_columns = df.select_dtypes(include=[np.number]).columns\n",
    "filtered_df = df[numeric_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e66821aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Scaling\n",
    "scaler = StandardScaler() \n",
    "scaled_data = scaler.fit_transform(filtered_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "20c2d8a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of clusters: 2\n"
     ]
    }
   ],
   "source": [
    "# Clustering on multiple clusters\n",
    "silhouette_scores = []\n",
    "k_range = range(2, 11)\n",
    "\n",
    "\n",
    "for k in k_range:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    labels = kmeans.fit_predict(scaled_data) \n",
    "    silhouette_avg = silhouette_score(scaled_data, labels)\n",
    "    silhouette_scores.append(silhouette_avg)\n",
    "\n",
    "# Silhouette coefficient graph\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(k_range, silhouette_scores, marker='o')\n",
    "plt.title('Silhouette Score for Different Number of Clusters')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Silhouette Score')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Choosing the optimal number of clusters\n",
    "optimal_k = k_range[np.argmax(silhouette_scores)]\n",
    "print(f'Optimal number of clusters: {optimal_k}')\n",
    "\n",
    "# Clustering with the optimal number of clusters\n",
    "kmeans = KMeans(n_clusters=optimal_k, random_state=42)\n",
    "labels = kmeans.fit_predict(scaled_data) \n",
    "filtered_df['Cluster'] = labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "97e867d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of clusters using PCA\n",
    "pca = PCA(n_components=2)\n",
    "reduced_data = pca.fit_transform(scaled_data)\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "for cluster in range(optimal_k):\n",
    "    cluster_data = reduced_data[filtered_df['Cluster'] == cluster]\n",
    "    plt.scatter(cluster_data[:, 0], cluster_data[:, 1], label=f'Cluster {cluster}')\n",
    "\n",
    "plt.title('Cluster Visualization (PCA Reduced)')\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80f7d959",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}