{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "acc15c3e",
   "metadata": {},
   "source": [
    "# A7: Clustering of AIMO Observations\n",
    "\n",
    "**Issue #28** - Perform clustering analysis on the AIMO dataset observations (excluding scores).\n",
    "\n",
    "## Objectives\n",
    "1. Load and preprocess the AIMO dataset\n",
    "2. Apply clustering algorithms (K-Means, Hierarchical, DBSCAN)\n",
    "3. Determine optimal number of clusters using elbow method and silhouette scores\n",
    "4. Interpret and profile the clusters\n",
    "\n",
    "---\n",
    "\n",
    "## Dependencies for Other Issues\n",
    "\n",
    "| Issue | What to Use | Location |\n",
    "|-------|-------------|----------|\n",
    "| **#29 (PCA Visualization)** | Clustered data with labels | `A7/clustered_aimo_data.csv` (Cluster column) |\n",
    "| **#29 (PCA Visualization)** | Feature columns list | Section 3 - `feature_cols` variable |\n",
    "| **#29 (PCA Visualization)** | Scaled data & scaler | Section 3 - `X_scaled`, `scaler` objects |\n",
    "| **#29 (PCA Visualization)** | Optimal k value | Section 4-5 - `optimal_k` variable |\n",
    "| **#31 (Documentation)** | Cluster profiles | Section 10 - Cluster Profiles Summary |\n",
    "| **#31 (Documentation)** | Algorithm comparison | Section 8 - Algorithm Comparison table |\n",
    "| **#31 (Documentation)** | All visualizations | `A7/*.png` files |\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b26140b",
   "metadata": {},
   "source": [
    "## 1. Setup and Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "37e6c913",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Repository root: /Users/amol/Desktop/LNU/LNU_Masters/intensive/github/Data-intensive-systems\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans, AgglomerativeClustering, DBSCAN\n",
    "from sklearn.metrics import silhouette_score, silhouette_samples, calinski_harabasz_score, davies_bouldin_score\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "plt.style.use('seaborn-v0_8-whitegrid')\n",
    "\n",
    "REPO_ROOT = os.path.abspath(os.path.join(os.getcwd(), '..'))\n",
    "print(f\"Repository root: {REPO_ROOT}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb09a43a",
   "metadata": {},
   "source": [
    "## 2. Data Loading and Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "91cacf90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset shape: (2094, 43)\n",
      "\n",
      "Columns (43):\n",
      "['AimoScore', 'No_1_Angle_Deviation', 'No_2_Angle_Deviation', 'No_3_Angle_Deviation', 'No_4_Angle_Deviation', 'No_5_Angle_Deviation', 'No_6_Angle_Deviation', 'No_7_Angle_Deviation', 'No_8_Angle_Deviation', 'No_9_Angle_Deviation', 'No_10_Angle_Deviation', 'No_11_Angle_Deviation', 'No_12_Angle_Deviation', 'No_13_Angle_Deviation', 'No_1_NASM_Deviation', 'No_2_NASM_Deviation', 'No_3_NASM_Deviation', 'No_4_NASM_Deviation', 'No_5_NASM_Deviation', 'No_6_NASM_Deviation', 'No_7_NASM_Deviation', 'No_8_NASM_Deviation', 'No_9_NASM_Deviation', 'No_10_NASM_Deviation', 'No_11_NASM_Deviation', 'No_12_NASM_Deviation', 'No_13_NASM_Deviation', 'No_14_NASM_Deviation', 'No_15_NASM_Deviation', 'No_16_NASM_Deviation', 'No_17_NASM_Deviation', 'No_18_NASM_Deviation', 'No_19_NASM_Deviation', 'No_20_NASM_Deviation', 'No_21_NASM_Deviation', 'No_22_NASM_Deviation', 'No_23_NASM_Deviation', 'No_24_NASM_Deviation', 'No_25_NASM_Deviation', 'No_1_Time_Deviation', 'No_2_Time_Deviation', 'EstimatedScore', 'ID']\n"
     ]
    }
   ],
   "source": [
    "# Load the AIMO dataset\n",
    "data_path = os.path.join(REPO_ROOT, 'Datasets_all', 'aimoscores.csv')\n",
    "df = pd.read_csv(data_path)\n",
    "\n",
    "print(f\"Dataset shape: {df.shape}\")\n",
    "print(f\"\\nColumns ({len(df.columns)}):\")\n",
    "print(df.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f90e14b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 5 rows:\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AimoScore</th>\n",
       "      <th>No_1_Angle_Deviation</th>\n",
       "      <th>No_2_Angle_Deviation</th>\n",
       "      <th>No_3_Angle_Deviation</th>\n",
       "      <th>No_4_Angle_Deviation</th>\n",
       "      <th>No_5_Angle_Deviation</th>\n",
       "      <th>No_6_Angle_Deviation</th>\n",
       "      <th>No_7_Angle_Deviation</th>\n",
       "      <th>No_8_Angle_Deviation</th>\n",
       "      <th>No_9_Angle_Deviation</th>\n",
       "      <th>...</th>\n",
       "      <th>No_20_NASM_Deviation</th>\n",
       "      <th>No_21_NASM_Deviation</th>\n",
       "      <th>No_22_NASM_Deviation</th>\n",
       "      <th>No_23_NASM_Deviation</th>\n",
       "      <th>No_24_NASM_Deviation</th>\n",
       "      <th>No_25_NASM_Deviation</th>\n",
       "      <th>No_1_Time_Deviation</th>\n",
       "      <th>No_2_Time_Deviation</th>\n",
       "      <th>EstimatedScore</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.323667</td>\n",
       "      <td>0.538020</td>\n",
       "      <td>0.815878</td>\n",
       "      <td>0.346724</td>\n",
       "      <td>0.382114</td>\n",
       "      <td>0.302248</td>\n",
       "      <td>0.947872</td>\n",
       "      <td>0.275945</td>\n",
       "      <td>0.521760</td>\n",
       "      <td>0.457198</td>\n",
       "      <td>...</td>\n",
       "      <td>0.656624</td>\n",
       "      <td>0.642276</td>\n",
       "      <td>0.552846</td>\n",
       "      <td>0.648972</td>\n",
       "      <td>0.578192</td>\n",
       "      <td>0.560019</td>\n",
       "      <td>0.821616</td>\n",
       "      <td>0.818747</td>\n",
       "      <td>0.209947</td>\n",
       "      <td>0003cdcc-86ed-494a-a3b5-90d09e96e06b.Kinect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.323699</td>\n",
       "      <td>0.443807</td>\n",
       "      <td>0.306552</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.188905</td>\n",
       "      <td>0.497370</td>\n",
       "      <td>0.140124</td>\n",
       "      <td>0.664275</td>\n",
       "      <td>0.521760</td>\n",
       "      <td>0.729316</td>\n",
       "      <td>...</td>\n",
       "      <td>0.721186</td>\n",
       "      <td>0.826399</td>\n",
       "      <td>0.805356</td>\n",
       "      <td>0.848876</td>\n",
       "      <td>0.889048</td>\n",
       "      <td>0.816834</td>\n",
       "      <td>0.307987</td>\n",
       "      <td>0.248207</td>\n",
       "      <td>0.457198</td>\n",
       "      <td>003115c4-bdb8-491c-b571-8fcebdecf8ed.Kinect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.848327</td>\n",
       "      <td>0.603539</td>\n",
       "      <td>0.373984</td>\n",
       "      <td>0.346724</td>\n",
       "      <td>0.590626</td>\n",
       "      <td>0.341942</td>\n",
       "      <td>0.298900</td>\n",
       "      <td>0.276901</td>\n",
       "      <td>0.623625</td>\n",
       "      <td>0.658058</td>\n",
       "      <td>...</td>\n",
       "      <td>0.656624</td>\n",
       "      <td>0.642276</td>\n",
       "      <td>0.690579</td>\n",
       "      <td>0.648972</td>\n",
       "      <td>0.578192</td>\n",
       "      <td>0.555715</td>\n",
       "      <td>0.218556</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.107126</td>\n",
       "      <td>00316bfb-ed43-489f-a55b-11c7f01c852d.Kinect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.351332</td>\n",
       "      <td>0.484935</td>\n",
       "      <td>0.623625</td>\n",
       "      <td>0.380201</td>\n",
       "      <td>0.975132</td>\n",
       "      <td>0.509326</td>\n",
       "      <td>0.888570</td>\n",
       "      <td>0.363462</td>\n",
       "      <td>0.847441</td>\n",
       "      <td>0.237207</td>\n",
       "      <td>...</td>\n",
       "      <td>0.656624</td>\n",
       "      <td>0.642276</td>\n",
       "      <td>0.552846</td>\n",
       "      <td>0.648972</td>\n",
       "      <td>0.578192</td>\n",
       "      <td>0.744620</td>\n",
       "      <td>0.458154</td>\n",
       "      <td>0.432807</td>\n",
       "      <td>0.612626</td>\n",
       "      <td>00607608-6f2f-459b-a69d-e14067489459.Kinect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.627181</td>\n",
       "      <td>0.860832</td>\n",
       "      <td>0.657580</td>\n",
       "      <td>0.745576</td>\n",
       "      <td>0.552846</td>\n",
       "      <td>0.375897</td>\n",
       "      <td>0.483022</td>\n",
       "      <td>0.388331</td>\n",
       "      <td>0.521760</td>\n",
       "      <td>0.387853</td>\n",
       "      <td>...</td>\n",
       "      <td>0.656624</td>\n",
       "      <td>0.642276</td>\n",
       "      <td>0.552846</td>\n",
       "      <td>0.648972</td>\n",
       "      <td>0.578192</td>\n",
       "      <td>0.308943</td>\n",
       "      <td>0.805356</td>\n",
       "      <td>0.774271</td>\n",
       "      <td>0.153515</td>\n",
       "      <td>007396ec-3463-4a05-915c-02244ff8d3de.Kinect</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   AimoScore  No_1_Angle_Deviation  No_2_Angle_Deviation  \\\n",
       "0   0.323667              0.538020              0.815878   \n",
       "1   0.323699              0.443807              0.306552   \n",
       "2   0.848327              0.603539              0.373984   \n",
       "3   0.351332              0.484935              0.623625   \n",
       "4   0.627181              0.860832              0.657580   \n",
       "\n",
       "   No_3_Angle_Deviation  No_4_Angle_Deviation  No_5_Angle_Deviation  \\\n",
       "0              0.346724              0.382114              0.302248   \n",
       "1              0.823529              0.188905              0.497370   \n",
       "2              0.346724              0.590626              0.341942   \n",
       "3              0.380201              0.975132              0.509326   \n",
       "4              0.745576              0.552846              0.375897   \n",
       "\n",
       "   No_6_Angle_Deviation  No_7_Angle_Deviation  No_8_Angle_Deviation  \\\n",
       "0              0.947872              0.275945              0.521760   \n",
       "1              0.140124              0.664275              0.521760   \n",
       "2              0.298900              0.276901              0.623625   \n",
       "3              0.888570              0.363462              0.847441   \n",
       "4              0.483022              0.388331              0.521760   \n",
       "\n",
       "   No_9_Angle_Deviation  ...  No_20_NASM_Deviation  No_21_NASM_Deviation  \\\n",
       "0              0.457198  ...              0.656624              0.642276   \n",
       "1              0.729316  ...              0.721186              0.826399   \n",
       "2              0.658058  ...              0.656624              0.642276   \n",
       "3              0.237207  ...              0.656624              0.642276   \n",
       "4              0.387853  ...              0.656624              0.642276   \n",
       "\n",
       "   No_22_NASM_Deviation  No_23_NASM_Deviation  No_24_NASM_Deviation  \\\n",
       "0              0.552846              0.648972              0.578192   \n",
       "1              0.805356              0.848876              0.889048   \n",
       "2              0.690579              0.648972              0.578192   \n",
       "3              0.552846              0.648972              0.578192   \n",
       "4              0.552846              0.648972              0.578192   \n",
       "\n",
       "   No_25_NASM_Deviation  No_1_Time_Deviation  No_2_Time_Deviation  \\\n",
       "0              0.560019             0.821616             0.818747   \n",
       "1              0.816834             0.307987             0.248207   \n",
       "2              0.555715             0.218556             0.235294   \n",
       "3              0.744620             0.458154             0.432807   \n",
       "4              0.308943             0.805356             0.774271   \n",
       "\n",
       "   EstimatedScore                                           ID  \n",
       "0        0.209947  0003cdcc-86ed-494a-a3b5-90d09e96e06b.Kinect  \n",
       "1        0.457198  003115c4-bdb8-491c-b571-8fcebdecf8ed.Kinect  \n",
       "2        0.107126  00316bfb-ed43-489f-a55b-11c7f01c852d.Kinect  \n",
       "3        0.612626  00607608-6f2f-459b-a69d-e14067489459.Kinect  \n",
       "4        0.153515  007396ec-3463-4a05-915c-02244ff8d3de.Kinect  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataset overview\n",
    "print(\"First 5 rows:\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "64915a0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data types and non-null counts:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2094 entries, 0 to 2093\n",
      "Data columns (total 43 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   AimoScore              2094 non-null   float64\n",
      " 1   No_1_Angle_Deviation   2094 non-null   float64\n",
      " 2   No_2_Angle_Deviation   2094 non-null   float64\n",
      " 3   No_3_Angle_Deviation   2094 non-null   float64\n",
      " 4   No_4_Angle_Deviation   2094 non-null   float64\n",
      " 5   No_5_Angle_Deviation   2094 non-null   float64\n",
      " 6   No_6_Angle_Deviation   2094 non-null   float64\n",
      " 7   No_7_Angle_Deviation   2094 non-null   float64\n",
      " 8   No_8_Angle_Deviation   2094 non-null   float64\n",
      " 9   No_9_Angle_Deviation   2094 non-null   float64\n",
      " 10  No_10_Angle_Deviation  2094 non-null   float64\n",
      " 11  No_11_Angle_Deviation  2094 non-null   float64\n",
      " 12  No_12_Angle_Deviation  2094 non-null   float64\n",
      " 13  No_13_Angle_Deviation  2094 non-null   float64\n",
      " 14  No_1_NASM_Deviation    2094 non-null   float64\n",
      " 15  No_2_NASM_Deviation    2094 non-null   float64\n",
      " 16  No_3_NASM_Deviation    2094 non-null   float64\n",
      " 17  No_4_NASM_Deviation    2094 non-null   float64\n",
      " 18  No_5_NASM_Deviation    2094 non-null   float64\n",
      " 19  No_6_NASM_Deviation    2094 non-null   float64\n",
      " 20  No_7_NASM_Deviation    2094 non-null   float64\n",
      " 21  No_8_NASM_Deviation    2094 non-null   float64\n",
      " 22  No_9_NASM_Deviation    2094 non-null   float64\n",
      " 23  No_10_NASM_Deviation   2094 non-null   float64\n",
      " 24  No_11_NASM_Deviation   2094 non-null   float64\n",
      " 25  No_12_NASM_Deviation   2094 non-null   float64\n",
      " 26  No_13_NASM_Deviation   2094 non-null   float64\n",
      " 27  No_14_NASM_Deviation   2094 non-null   float64\n",
      " 28  No_15_NASM_Deviation   2094 non-null   float64\n",
      " 29  No_16_NASM_Deviation   2094 non-null   float64\n",
      " 30  No_17_NASM_Deviation   2094 non-null   float64\n",
      " 31  No_18_NASM_Deviation   2094 non-null   float64\n",
      " 32  No_19_NASM_Deviation   2094 non-null   float64\n",
      " 33  No_20_NASM_Deviation   2094 non-null   float64\n",
      " 34  No_21_NASM_Deviation   2094 non-null   float64\n",
      " 35  No_22_NASM_Deviation   2094 non-null   float64\n",
      " 36  No_23_NASM_Deviation   2094 non-null   float64\n",
      " 37  No_24_NASM_Deviation   2094 non-null   float64\n",
      " 38  No_25_NASM_Deviation   2094 non-null   float64\n",
      " 39  No_1_Time_Deviation    2094 non-null   float64\n",
      " 40  No_2_Time_Deviation    2094 non-null   float64\n",
      " 41  EstimatedScore         2094 non-null   float64\n",
      " 42  ID                     2094 non-null   object \n",
      "dtypes: float64(42), object(1)\n",
      "memory usage: 703.6+ KB\n",
      "None\n",
      "\n",
      "Missing values per column:\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values and data types\n",
    "print(\"Data types and non-null counts:\")\n",
    "print(df.info())\n",
    "print(f\"\\nMissing values per column:\")\n",
    "print(df.isnull().sum().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "56f2b7bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AimoScore</th>\n",
       "      <th>No_1_Angle_Deviation</th>\n",
       "      <th>No_2_Angle_Deviation</th>\n",
       "      <th>No_3_Angle_Deviation</th>\n",
       "      <th>No_4_Angle_Deviation</th>\n",
       "      <th>No_5_Angle_Deviation</th>\n",
       "      <th>No_6_Angle_Deviation</th>\n",
       "      <th>No_7_Angle_Deviation</th>\n",
       "      <th>No_8_Angle_Deviation</th>\n",
       "      <th>No_9_Angle_Deviation</th>\n",
       "      <th>...</th>\n",
       "      <th>No_19_NASM_Deviation</th>\n",
       "      <th>No_20_NASM_Deviation</th>\n",
       "      <th>No_21_NASM_Deviation</th>\n",
       "      <th>No_22_NASM_Deviation</th>\n",
       "      <th>No_23_NASM_Deviation</th>\n",
       "      <th>No_24_NASM_Deviation</th>\n",
       "      <th>No_25_NASM_Deviation</th>\n",
       "      <th>No_1_Time_Deviation</th>\n",
       "      <th>No_2_Time_Deviation</th>\n",
       "      <th>EstimatedScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.572740</td>\n",
       "      <td>0.539248</td>\n",
       "      <td>0.504462</td>\n",
       "      <td>0.560258</td>\n",
       "      <td>0.518065</td>\n",
       "      <td>0.513932</td>\n",
       "      <td>0.510336</td>\n",
       "      <td>0.519434</td>\n",
       "      <td>0.635923</td>\n",
       "      <td>0.512548</td>\n",
       "      <td>...</td>\n",
       "      <td>0.725505</td>\n",
       "      <td>0.715943</td>\n",
       "      <td>0.706319</td>\n",
       "      <td>0.652969</td>\n",
       "      <td>0.710774</td>\n",
       "      <td>0.667517</td>\n",
       "      <td>0.548089</td>\n",
       "      <td>0.512021</td>\n",
       "      <td>0.512373</td>\n",
       "      <td>0.500209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.236510</td>\n",
       "      <td>0.239284</td>\n",
       "      <td>0.282136</td>\n",
       "      <td>0.217828</td>\n",
       "      <td>0.264104</td>\n",
       "      <td>0.270076</td>\n",
       "      <td>0.274473</td>\n",
       "      <td>0.262884</td>\n",
       "      <td>0.153280</td>\n",
       "      <td>0.271193</td>\n",
       "      <td>...</td>\n",
       "      <td>0.095008</td>\n",
       "      <td>0.100437</td>\n",
       "      <td>0.105886</td>\n",
       "      <td>0.141000</td>\n",
       "      <td>0.103196</td>\n",
       "      <td>0.130989</td>\n",
       "      <td>0.230642</td>\n",
       "      <td>0.273056</td>\n",
       "      <td>0.272647</td>\n",
       "      <td>0.288790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.279770</td>\n",
       "      <td>0.092300</td>\n",
       "      <td>0.346724</td>\n",
       "      <td>0.188905</td>\n",
       "      <td>0.163080</td>\n",
       "      <td>0.140124</td>\n",
       "      <td>0.194165</td>\n",
       "      <td>0.303247</td>\n",
       "      <td>0.156385</td>\n",
       "      <td>...</td>\n",
       "      <td>0.670971</td>\n",
       "      <td>0.656624</td>\n",
       "      <td>0.640401</td>\n",
       "      <td>0.525310</td>\n",
       "      <td>0.648972</td>\n",
       "      <td>0.578192</td>\n",
       "      <td>0.308943</td>\n",
       "      <td>0.148733</td>\n",
       "      <td>0.151124</td>\n",
       "      <td>0.000478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.407907</td>\n",
       "      <td>0.279770</td>\n",
       "      <td>0.250149</td>\n",
       "      <td>0.346724</td>\n",
       "      <td>0.250239</td>\n",
       "      <td>0.250717</td>\n",
       "      <td>0.250717</td>\n",
       "      <td>0.250717</td>\n",
       "      <td>0.521760</td>\n",
       "      <td>0.250717</td>\n",
       "      <td>...</td>\n",
       "      <td>0.670971</td>\n",
       "      <td>0.656624</td>\n",
       "      <td>0.642276</td>\n",
       "      <td>0.552846</td>\n",
       "      <td>0.648972</td>\n",
       "      <td>0.578192</td>\n",
       "      <td>0.308943</td>\n",
       "      <td>0.250717</td>\n",
       "      <td>0.250717</td>\n",
       "      <td>0.250239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.617248</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500478</td>\n",
       "      <td>0.500478</td>\n",
       "      <td>0.500956</td>\n",
       "      <td>0.521760</td>\n",
       "      <td>0.500478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.670971</td>\n",
       "      <td>0.656624</td>\n",
       "      <td>0.642276</td>\n",
       "      <td>0.552846</td>\n",
       "      <td>0.648972</td>\n",
       "      <td>0.578192</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500478</td>\n",
       "      <td>0.500478</td>\n",
       "      <td>0.500478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.759378</td>\n",
       "      <td>0.749761</td>\n",
       "      <td>0.750239</td>\n",
       "      <td>0.750239</td>\n",
       "      <td>0.750239</td>\n",
       "      <td>0.750239</td>\n",
       "      <td>0.750239</td>\n",
       "      <td>0.750239</td>\n",
       "      <td>0.749761</td>\n",
       "      <td>0.750239</td>\n",
       "      <td>...</td>\n",
       "      <td>0.751196</td>\n",
       "      <td>0.751196</td>\n",
       "      <td>0.749761</td>\n",
       "      <td>0.749880</td>\n",
       "      <td>0.751196</td>\n",
       "      <td>0.751196</td>\n",
       "      <td>0.750239</td>\n",
       "      <td>0.750239</td>\n",
       "      <td>0.750239</td>\n",
       "      <td>0.750239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.993987</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         AimoScore  No_1_Angle_Deviation  No_2_Angle_Deviation  \\\n",
       "count  2094.000000           2094.000000           2094.000000   \n",
       "mean      0.572740              0.539248              0.504462   \n",
       "std       0.236510              0.239284              0.282136   \n",
       "min       0.010000              0.279770              0.092300   \n",
       "25%       0.407907              0.279770              0.250149   \n",
       "50%       0.617248              0.500000              0.500000   \n",
       "75%       0.759378              0.749761              0.750239   \n",
       "max       0.993987              1.000000              1.000000   \n",
       "\n",
       "       No_3_Angle_Deviation  No_4_Angle_Deviation  No_5_Angle_Deviation  \\\n",
       "count           2094.000000           2094.000000           2094.000000   \n",
       "mean               0.560258              0.518065              0.513932   \n",
       "std                0.217828              0.264104              0.270076   \n",
       "min                0.346724              0.188905              0.163080   \n",
       "25%                0.346724              0.250239              0.250717   \n",
       "50%                0.500000              0.500000              0.500478   \n",
       "75%                0.750239              0.750239              0.750239   \n",
       "max                1.000000              1.000000              1.000000   \n",
       "\n",
       "       No_6_Angle_Deviation  No_7_Angle_Deviation  No_8_Angle_Deviation  \\\n",
       "count           2094.000000           2094.000000           2094.000000   \n",
       "mean               0.510336              0.519434              0.635923   \n",
       "std                0.274473              0.262884              0.153280   \n",
       "min                0.140124              0.194165              0.303247   \n",
       "25%                0.250717              0.250717              0.521760   \n",
       "50%                0.500478              0.500956              0.521760   \n",
       "75%                0.750239              0.750239              0.749761   \n",
       "max                1.000000              1.000000              1.000000   \n",
       "\n",
       "       No_9_Angle_Deviation  ...  No_19_NASM_Deviation  No_20_NASM_Deviation  \\\n",
       "count           2094.000000  ...           2094.000000           2094.000000   \n",
       "mean               0.512548  ...              0.725505              0.715943   \n",
       "std                0.271193  ...              0.095008              0.100437   \n",
       "min                0.156385  ...              0.670971              0.656624   \n",
       "25%                0.250717  ...              0.670971              0.656624   \n",
       "50%                0.500478  ...              0.670971              0.656624   \n",
       "75%                0.750239  ...              0.751196              0.751196   \n",
       "max                1.000000  ...              1.000000              1.000000   \n",
       "\n",
       "       No_21_NASM_Deviation  No_22_NASM_Deviation  No_23_NASM_Deviation  \\\n",
       "count           2094.000000           2094.000000           2094.000000   \n",
       "mean               0.706319              0.652969              0.710774   \n",
       "std                0.105886              0.141000              0.103196   \n",
       "min                0.640401              0.525310              0.648972   \n",
       "25%                0.642276              0.552846              0.648972   \n",
       "50%                0.642276              0.552846              0.648972   \n",
       "75%                0.749761              0.749880              0.751196   \n",
       "max                1.000000              1.000000              1.000000   \n",
       "\n",
       "       No_24_NASM_Deviation  No_25_NASM_Deviation  No_1_Time_Deviation  \\\n",
       "count           2094.000000           2094.000000          2094.000000   \n",
       "mean               0.667517              0.548089             0.512021   \n",
       "std                0.130989              0.230642             0.273056   \n",
       "min                0.578192              0.308943             0.148733   \n",
       "25%                0.578192              0.308943             0.250717   \n",
       "50%                0.578192              0.500000             0.500478   \n",
       "75%                0.751196              0.750239             0.750239   \n",
       "max                1.000000              1.000000             1.000000   \n",
       "\n",
       "       No_2_Time_Deviation  EstimatedScore  \n",
       "count          2094.000000     2094.000000  \n",
       "mean              0.512373        0.500209  \n",
       "std               0.272647        0.288790  \n",
       "min               0.151124        0.000478  \n",
       "25%               0.250717        0.250239  \n",
       "50%               0.500478        0.500478  \n",
       "75%               0.750239        0.750239  \n",
       "max               1.000000        1.000000  \n",
       "\n",
       "[8 rows x 42 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistical summary\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe9651a8",
   "metadata": {},
   "source": [
    "## 3. Feature Selection and Preprocessing\n",
    "\n",
    "Per Issue #28 requirements:\n",
    "- Exclude score columns: `AimoScore`, `EstimatedScore`\n",
    "- Exclude identifier: `ID`\n",
    "\n",
    "Features to use for clustering:\n",
    "- 13 Angle Deviation features\n",
    "- 25 NASM Deviation features  \n",
    "- 2 Time Deviation features\n",
    "\n",
    "**Total: 40 features**\n",
    "\n",
    "> 🔗 **For Issue #29 (PCA)**: Use `feature_cols` list and `X_scaled` array from this section. The `scaler` object can be reused if needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bbfb6e4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature columns (40):\n",
      "  - Angle Deviation: 13\n",
      "  - NASM Deviation: 25\n",
      "  - Time Deviation: 2\n",
      "\n",
      "Feature matrix shape: (2094, 40)\n"
     ]
    }
   ],
   "source": [
    "# Identify columns to exclude\n",
    "exclude_cols = ['AimoScore', 'EstimatedScore', 'ID']\n",
    "\n",
    "# Create feature matrix (excluding scores and ID)\n",
    "feature_cols = [col for col in df.columns if col not in exclude_cols]\n",
    "X = df[feature_cols].copy()\n",
    "\n",
    "print(f\"Feature columns ({len(feature_cols)}):\")\n",
    "print(f\"  - Angle Deviation: {len([c for c in feature_cols if 'Angle' in c])}\")\n",
    "print(f\"  - NASM Deviation: {len([c for c in feature_cols if 'NASM' in c])}\")\n",
    "print(f\"  - Time Deviation: {len([c for c in feature_cols if 'Time' in c])}\")\n",
    "print(f\"\\nFeature matrix shape: {X.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d0211148",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaled data shape: (2094, 40)\n",
      "Mean of scaled features (should be ~0): 0.000000\n",
      "Std of scaled features (should be ~1): 1.000000\n"
     ]
    }
   ],
   "source": [
    "# Standardize features (important for clustering algorithms)\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "print(f\"Scaled data shape: {X_scaled.shape}\")\n",
    "print(f\"Mean of scaled features (should be ~0): {X_scaled.mean(axis=0).mean():.6f}\")\n",
    "print(f\"Std of scaled features (should be ~1): {X_scaled.std(axis=0).mean():.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4481ab01",
   "metadata": {},
   "source": [
    "## 4. Determining Optimal Number of Clusters\n",
    "\n",
    "We'll use multiple methods to determine the optimal k:\n",
    "1. **Elbow Method** - Look for the \"elbow\" in the inertia curve\n",
    "2. **Silhouette Score** - Higher is better (range: -1 to 1)\n",
    "3. **Calinski-Harabasz Index** - Higher is better\n",
    "4. **Davies-Bouldin Index** - Lower is better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f2780c8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating K-Means for k = 2 to 10...\n",
      "k=2: Silhouette=0.1367, CH=301.6, DB=2.4619\n",
      "k=3: Silhouette=0.1306, CH=261.4, DB=2.4448\n",
      "k=4: Silhouette=0.1173, CH=234.0, DB=2.3453\n",
      "k=5: Silhouette=0.1181, CH=216.5, DB=2.2282\n",
      "k=6: Silhouette=0.1191, CH=193.8, DB=2.2628\n",
      "k=7: Silhouette=0.1110, CH=175.2, DB=2.3953\n",
      "k=8: Silhouette=0.1124, CH=160.8, DB=2.4703\n",
      "k=9: Silhouette=0.1131, CH=150.1, DB=2.4357\n",
      "k=10: Silhouette=0.1135, CH=140.2, DB=2.4070\n"
     ]
    }
   ],
   "source": [
    "# Evaluate clustering for different k values\n",
    "k_range = range(2, 11)\n",
    "\n",
    "inertias = []\n",
    "silhouette_scores = []\n",
    "calinski_scores = []\n",
    "davies_bouldin_scores = []\n",
    "\n",
    "print(\"Evaluating K-Means for k = 2 to 10...\")\n",
    "for k in k_range:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42, n_init=10, max_iter=300)\n",
    "    labels = kmeans.fit_predict(X_scaled)\n",
    "    \n",
    "    inertias.append(kmeans.inertia_)\n",
    "    silhouette_scores.append(silhouette_score(X_scaled, labels))\n",
    "    calinski_scores.append(calinski_harabasz_score(X_scaled, labels))\n",
    "    davies_bouldin_scores.append(davies_bouldin_score(X_scaled, labels))\n",
    "    \n",
    "    print(f\"k={k}: Silhouette={silhouette_scores[-1]:.4f}, CH={calinski_scores[-1]:.1f}, DB={davies_bouldin_scores[-1]:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fa437e93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster Evaluation Summary:\n",
      " k      Inertia  Silhouette  Calinski-Harabasz  Davies-Bouldin\n",
      " 2 73204.664811    0.136737         301.644182        2.461868\n",
      " 3 67004.644157    0.130588         261.440453        2.444793\n",
      " 4 62700.832475    0.117306         233.987644        2.345341\n",
      " 5 59213.347908    0.118096         216.496609        2.228208\n",
      " 6 57212.165307    0.119067         193.776546        2.262755\n",
      " 7 55706.744116    0.110984         175.164743        2.395285\n",
      " 8 54406.946800    0.112356         160.773768        2.470320\n",
      " 9 53143.615566    0.113080         150.147767        2.435663\n",
      "10 52174.378968    0.113501         140.180414        2.406967\n"
     ]
    }
   ],
   "source": [
    "# Visualize cluster evaluation metrics\n",
    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
    "\n",
    "# Elbow plot (Inertia)\n",
    "axes[0, 0].plot(k_range, inertias, 'bo-', linewidth=2, markersize=8)\n",
    "axes[0, 0].set_xlabel('Number of Clusters (k)', fontsize=12)\n",
    "axes[0, 0].set_ylabel('Inertia (Within-cluster sum of squares)', fontsize=12)\n",
    "axes[0, 0].set_title('Elbow Method', fontsize=14)\n",
    "axes[0, 0].set_xticks(list(k_range))\n",
    "axes[0, 0].grid(True, alpha=0.3)\n",
    "\n",
    "# Silhouette Score\n",
    "axes[0, 1].plot(k_range, silhouette_scores, 'go-', linewidth=2, markersize=8)\n",
    "axes[0, 1].set_xlabel('Number of Clusters (k)', fontsize=12)\n",
    "axes[0, 1].set_ylabel('Silhouette Score', fontsize=12)\n",
    "axes[0, 1].set_title('Silhouette Score (Higher is Better)', fontsize=14)\n",
    "axes[0, 1].set_xticks(list(k_range))\n",
    "axes[0, 1].grid(True, alpha=0.3)\n",
    "\n",
    "# Calinski-Harabasz Index\n",
    "axes[1, 0].plot(k_range, calinski_scores, 'ro-', linewidth=2, markersize=8)\n",
    "axes[1, 0].set_xlabel('Number of Clusters (k)', fontsize=12)\n",
    "axes[1, 0].set_ylabel('Calinski-Harabasz Index', fontsize=12)\n",
    "axes[1, 0].set_title('Calinski-Harabasz Index (Higher is Better)', fontsize=14)\n",
    "axes[1, 0].set_xticks(list(k_range))\n",
    "axes[1, 0].grid(True, alpha=0.3)\n",
    "\n",
    "# Davies-Bouldin Index\n",
    "axes[1, 1].plot(k_range, davies_bouldin_scores, 'mo-', linewidth=2, markersize=8)\n",
    "axes[1, 1].set_xlabel('Number of Clusters (k)', fontsize=12)\n",
    "axes[1, 1].set_ylabel('Davies-Bouldin Index', fontsize=12)\n",
    "axes[1, 1].set_title('Davies-Bouldin Index (Lower is Better)', fontsize=14)\n",
    "axes[1, 1].set_xticks(list(k_range))\n",
    "axes[1, 1].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('cluster_evaluation_metrics.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "# Summary table\n",
    "summary_df = pd.DataFrame({\n",
    "    'k': list(k_range),\n",
    "    'Inertia': inertias,\n",
    "    'Silhouette': silhouette_scores,\n",
    "    'Calinski-Harabasz': calinski_scores,\n",
    "    'Davies-Bouldin': davies_bouldin_scores\n",
    "})\n",
    "print(\"\\nCluster Evaluation Summary:\")\n",
    "print(summary_df.to_string(index=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb819218",
   "metadata": {},
   "source": [
    "### Optimal k Selection Analysis\n",
    "\n",
    "Based on the metrics above, we'll select the optimal number of clusters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "12e90092",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal k by metric:\n",
      "  - Silhouette Score: k = 2 (score: 0.1367)\n",
      "  - Calinski-Harabasz: k = 2 (score: 301.6)\n",
      "  - Davies-Bouldin: k = 5 (score: 2.2282)\n",
      "\n",
      ">>> Selected optimal k = 2 (based on silhouette score)\n"
     ]
    }
   ],
   "source": [
    "# Find optimal k based on metrics\n",
    "best_silhouette_k = k_range[np.argmax(silhouette_scores)]\n",
    "best_calinski_k = k_range[np.argmax(calinski_scores)]\n",
    "best_db_k = k_range[np.argmin(davies_bouldin_scores)]\n",
    "\n",
    "print(\"Optimal k by metric:\")\n",
    "print(f\"  - Silhouette Score: k = {best_silhouette_k} (score: {max(silhouette_scores):.4f})\")\n",
    "print(f\"  - Calinski-Harabasz: k = {best_calinski_k} (score: {max(calinski_scores):.1f})\")\n",
    "print(f\"  - Davies-Bouldin: k = {best_db_k} (score: {min(davies_bouldin_scores):.4f})\")\n",
    "\n",
    "# Select optimal k (consensus or silhouette-based)\n",
    "optimal_k = best_silhouette_k\n",
    "print(f\"\\n>>> Selected optimal k = {optimal_k} (based on silhouette score)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd5d13d3",
   "metadata": {},
   "source": [
    "## 5. K-Means Clustering\n",
    "\n",
    "> 🔗 **For Issue #29**: The `cluster_labels` array and `optimal_k` value from this section are used to color the PCA visualization. The labels are also saved in `clustered_aimo_data.csv`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6441bdac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Means with k=2\n",
      "\n",
      "Cluster distribution:\n",
      "Cluster\n",
      "0    1393\n",
      "1     701\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Cluster proportions:\n",
      "Cluster\n",
      "0    66.52%\n",
      "1    33.48%\n",
      "Name: count, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Fit K-Means with optimal k\n",
    "kmeans_final = KMeans(n_clusters=optimal_k, random_state=42, n_init=10, max_iter=300)\n",
    "cluster_labels = kmeans_final.fit_predict(X_scaled)\n",
    "\n",
    "# Add cluster labels to dataframe\n",
    "df['Cluster'] = cluster_labels\n",
    "\n",
    "print(f\"K-Means with k={optimal_k}\")\n",
    "print(f\"\\nCluster distribution:\")\n",
    "print(df['Cluster'].value_counts().sort_index())\n",
    "print(f\"\\nCluster proportions:\")\n",
    "print((df['Cluster'].value_counts().sort_index() / len(df) * 100).round(2).astype(str) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e1fef94b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Average silhouette score: 0.1367\n",
      "\n",
      "Silhouette score by cluster:\n",
      "  Cluster 0: 0.1419\n",
      "  Cluster 1: 0.1265\n"
     ]
    }
   ],
   "source": [
    "# Silhouette analysis for the chosen k\n",
    "silhouette_vals = silhouette_samples(X_scaled, cluster_labels)\n",
    "df['Silhouette'] = silhouette_vals\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "y_lower = 10\n",
    "colors = plt.cm.tab10(np.linspace(0, 1, optimal_k))\n",
    "\n",
    "for i in range(optimal_k):\n",
    "    cluster_silhouette_vals = silhouette_vals[cluster_labels == i]\n",
    "    cluster_silhouette_vals.sort()\n",
    "    \n",
    "    cluster_size = len(cluster_silhouette_vals)\n",
    "    y_upper = y_lower + cluster_size\n",
    "    \n",
    "    ax.fill_betweenx(np.arange(y_lower, y_upper), 0, cluster_silhouette_vals,\n",
    "                     facecolor=colors[i], edgecolor=colors[i], alpha=0.7)\n",
    "    ax.text(-0.05, y_lower + 0.5 * cluster_size, str(i), fontsize=12, fontweight='bold')\n",
    "    \n",
    "    y_lower = y_upper + 10\n",
    "\n",
    "avg_silhouette = silhouette_score(X_scaled, cluster_labels)\n",
    "ax.axvline(x=avg_silhouette, color='red', linestyle='--', label=f'Average: {avg_silhouette:.3f}')\n",
    "ax.set_xlabel('Silhouette Coefficient', fontsize=12)\n",
    "ax.set_ylabel('Cluster', fontsize=12)\n",
    "ax.set_title(f'Silhouette Analysis for K-Means (k={optimal_k})', fontsize=14)\n",
    "ax.legend(loc='best')\n",
    "plt.tight_layout()\n",
    "plt.savefig('silhouette_analysis.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "print(f\"\\nAverage silhouette score: {avg_silhouette:.4f}\")\n",
    "print(\"\\nSilhouette score by cluster:\")\n",
    "for i in range(optimal_k):\n",
    "    cluster_sil = silhouette_vals[cluster_labels == i].mean()\n",
    "    print(f\"  Cluster {i}: {cluster_sil:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbe2f457",
   "metadata": {},
   "source": [
    "## 6. Hierarchical Clustering (Comparison)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bb6d813b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/b7755qRyUvtdfy0WpfdyinVovRTd1nsULf6u9wB6H6PYiD4TKJd9svwcx+le6D3VYyDb6+8dX33eVjzOWyNCn9F0jlO/2meffcwv11xzjXt/nexaRKL1sqLXAlG/79evnzs36Rz05ZdfuvGt9/CpeM+b0wuReR1CH8w10NWJdULRB2k1ng7WQQcd5DpIS3zwwQfuzaXKU8BH5Wuxlv32288NGi0WpsDAXXfd5e5Lhk7kCgoq+fdnn33mgpOik77KPOCAA5IqNx2LVH344Ydu8bRWrVq5oJIWHVu6dKn7mxcUU4Lme++9171ItYTaSANRH5oV5FPyar3R0YtIS+gNkgafAjsKAuu5KOCvILQWobnvvvvqAqDqC0FbQEd9UONAi6VpUSHR0NfxUBBPX1yoj+pNdBD7kEfHVe3undj1Ip4K6oM68SpoomTqapfbb7/dvQCq7RUo1v3JSscx1vjRG9ZDDjnEvZgr4KYXIwXbFCzTuePWW2915wq9wUqGjq3Gs96w6UVJ+9Ab5rKyMre4UPRCfUHrQzrXaEG1hr5Y0flVQfpFixa5/eiYJEoLU2ns6w2qPmiqTaI/gOqcrdcKjbtknkM6x5nqq/6i8+lhhx1mK1eudOc3nZ+7du1qqeSdh7znkEr6svTvf/+7+xCn81tLXwceffRRt9iePpzo+OqDtN6M6Uu8O++80wWj9Xe9Juu1OVEqa9SoUXbmmWe6gHP0m7zp06e7L070Blznq6D2IQX+zzvvPNdPdC6L/YJHH/L0o/OV9hOk86jeJ6ocfemlD9H6UlyLRniLrOj1QItxjBw50r3fC1r5Xj/RuUYfmPVlo9pa/Um/60fvH/VFg/paMovX+l1+NL2v0hjWQiWp4vcx8HuMpWMMZ3sb+V1/v89Dfo+xRF6bkg2a+91Gfi8g7Pci4Dq+6p9axKihxXFVd/VZfSZJ5otyv+uvyQmqv4KCXh9VAFGfMfR6r/eQl156qfusrNf6ZPg9jv1uI7/HQLbXX2NUQXeNXy1Srz6vL6r0ZYv6j86Der/70EMPueOfDI39xiZt6LNH9N+Sea2J/Vypc5JiKoofaBxUVla6z9uarNiSSU3Rlc5LukQzFZdLelTWzTff7FIt6FJGXZqsSya1qIpSGuiy/WTpUlItmOYtoPP888+75NC6fExpAVpyybjfi1R5lE9Tz0N5Db3LKHT5qhJcK4efLgFNJbWJLlHVcUjVcdalqbp09V//+lfk4YcfdomxvZyYyq0WeylxUBbQufDCC90l4R5dBq60ILqMVWVqobwRI0ZEgtyH1HeUn1WL/kQvPKaUCcqP+NFHHyVdtnIi6xL5ll4en+lFkuSJJ56I/PWvf3W5N3V5lRYKUG5bla3zkfIuN5TsPdFjEJ0rOhXHIB19SAs8JbMIWyJURx2DhhY91CUy//jHP1ze1aC2kS7puf766yOTJ0929dXrjvqNt/BW7MJkqT4P6XKiVJWvvLDKNeuNLaUFaUn5Hh1fve6q3np9v/322+sukVI6plGjRrUoV7JSs2hxVKVqiaX0JhqDyR7jdL3eK52N8tYrb63aav/993dpePS7FlZL9tJPv8+jJ554onstUD/Spfd6bdAlq9FUfy08F8TyRSkF7rnnnrrf1V+0kGY0HZtk04L5Xb7f/D4Gfo+xdIzhbG8jv+vv93nI7zE2fPhw99lI7a3PMU39JMvvNvJ7AWG/FwHX+4fYNQMaWuQ82bSBftdfqZy8NEjaj/YX+xlA47olaZD8Hsd+t5HfYyDb6+8tRh9Na1rofi/NnxbXVmqwZL388ssuPZ3OF++8807d+jf6bKx0loqntWRNnNhjoH0p3WQqx0G0nA7aNvVCpMCDOkJLX5j8pg+AOtiqqzqDgjE6WerEpZwxyh2jD6OPP/54UuX7vUhVJqneyq/T0pwlfvN7AZ3YfLwKMiiX5yuvvOL2p5PWXnvtFbn//vsD2Yd0Eu/Zs6cLlGuFSa2+qDebGhsaA1p5Um+Aks2hpJOucvvq59VXX434IR2LJDVFx8BbyCiIxyCXz0Op4ncb6YubnXbaKXL++ee715RzzjnHnScUwFWf1blUr0EPPfRQIM9DfpcfJF5gI9/Gmd/nUb2J9/LvK1exPiTqg3M0fXGb7GJ8fpfv7SO6HTRmY/fRksVr/S7fb34fA7/HWDrGcLa3kd/1T8d5yO8xpnyO8QQOk+V3G/m9gLDfi4Dri0xvLY3GPPXUU27SRRDrr/K0FoRHXzLEHlN9HtBkiWT5PY79bqN0f7bPtvqrDK31EU2fUzWW9Rog+rs+l7TEL7/84r7Q0Wf8999/P+XHIHoSgtZ1mTp1ar1tFBDWZ49USO5a1iyhS3iV61SXefpNlyPrUgDlSdTlGLp0OJqm9yeTN1RlaZq1Lo/QbV3+rNya2o9yfejSZ+W8GTt2bFL1Vu48TamPphwljz32mLsUXXXWlPWW0BT7eH9SSdPpNc2+JflKoinHkC7XUwqEI444wqXWOPLII93vul9/T4bynujyhRNPPNFddqs0GrqUNFViM6BMnjzZXTKsVB66hELPRTlLk8njmY4+pNQNt912m7scQ/m9TjjhBHef2kyXgShn5VVXXeXGSTJ0ecTo0aNd7k5dfqPLkpUTSGkYUsXvY+zx8u96dPmQ0oXosjFdyqLLZYJ4DNJxHhKNUV3GrvLU7w8++GDr3bu3/e1vf7Px48e7dDrJivccp/QSQWwjXYJ8xx13uEvZdIm/8o5ff/317jyn84TOpepDyb7W+H0e8rv85saYLotSeyU7xrxjEPve4fXXX3eXxqr+uqRYlwwme6lYEMaZ8tElO878Po/qMlWlORGluVI6jNh8eWq/ZPPv+12+KOe42sijVB3qQ9Gef/75JnMOZ7J8v99v+X0M/B5j6RjD2d5Gftff7/NQOsaY3udqPCk3pR/8biPliVQeTNH/6jdz586tt43uTzaPpN5PKG2H0uLp0mal8oi9fFqXnjeU2iAeei3U+walqtDnel3mrvRZ+l9plvQ5Xyn+tF0Q66/3g7rs2+uXWjfFSz+lVJRK3aG/n3TSSRbUcex3G6Xjs30211/HTetBRdN6CEqzp5QJopR1yeR0jqay9N5fP3qPrjVslP86FZQeU+tz6HOxXreUglWfh733uBrL+vytNHMpEclxutxTs23uuOOOepcFp3J2my7F1zcZV199tfvRtwL6tji6/GS/Nda3hN6K5Lrcs6FvE/VNhFIyJEMr2+myEqUpiP2GZtmyZe6bb2+KfLJURjouDffTlClTXDv069fPXW6gS1iffvpp97++YTzttNPcsYr9hiVRunz7sssuixxwwAFun6lI46F29b61En3bFPtt5WeffZb0N9J+9yH17dj66nJkfevqfcuoNkr28oPoyxt++uknd+mYLnvWbHwdbx1freaq45EKfhxj7zIQ1fm8886rS2eg84UuL9FlYpo5qb/r/qAdg3Sch5SCRW191llnufZQ2hqVqctz9KN96LVCYyGI5zm/20iP9V5rRDMkYi9307ftyb7W+H0e8rt8v8dYQzMnNLtZx1xpKsaNG+dmC+i9RrLl58I48/M8qplPSgfy4IMPrvE3ve9Segcd32SvKPC7fNFx1Ywb9X9dWqqZ8TvvvHOkb9++7ooIXXKqtpo1a1Ygy/f7/Zbfx8DvMZaOMZztbZSOcebneSgdY8yb1aZZaH7zo410VZxeC4899lg301JXo2pGoNpHV4IpNZLu13kjGXotV/vr9V19RVcfqU967aWrXtWHNRs2WUqbqHbRftQmej+tfeh33T979uyky/a7/koBpc9K9913X4PjW1fiKZ1WdLqKoI3jdBxjP8dAttf/3XffdfXW7FS9Vxw4cKCrr3e8lT5NY1xXeqZKTU2NiwcqrZ/2nYrP3nrN0gxeXfGuFIRKl+mlW9SMer1/TzYtYaycD9p6gRh1XnXu9957L+VBW52cFNTx6EOXXjz0outNKU82aHv22We7Dz2LFi1yL96qt373OoACuYMGDXInr2RftPUGQZeSLlmyZI2/KzCjDq7n6NE+E6G66kPukUce6S5xSDUN6ug8p039JEv5+Jq7LFh/15vNRDV02br6qQa76txQP03kGChQpDynOo4KAqj/aDx4dEwGDBgQ2D6kN2PK6eVdgqD6XnnllZE//elPdeNbeZF14k9FoCT6DZUupdYYVACrJZes+n2MRec3vVn1aLwpfUo05ZGLPg5BOQbpOA+p/rF5p5XjyMv3pDeXesFVoCAZfp/n/G4jjX+9KWssncx3333nLpVKNr+U3+chv8v3e4x5zyH6XKTcj7F5hBW8VU77fBxnfp9HVb7y7k+aNGmNvylP2TXXXLPGl+ZBKt/bh3IAKs+jUp2oj+qDlo6pPkwMGzZsjZzbiT4HP8tPx/stv4+xn2MsHWM4F9rI7/r7fR7ye4z5LR1tpM/Feq3R673ef+k9yqWXXupeH/X6Ev25PJnyReXqy1+VFZ0vVGkVY9OpBekYZHv90zmO/WqjdIyBbK+/xrDW0dAXeKqv3itGT7qMnTiyKkV9VGlh1FY///xzJJvkRdDWo0i4PnTpw5wCMKkK2sbOUPI+IOpbUQVuNRM22aDtt99+6xYl0Yc57UffOOgbCX2jog9HWrxlzz33XCPfSLyUyF4vePEswqTnpFnFRx11VML70UlFbzwUeE415bTRIgYq30so3dhPstT2Db25jKaTTzK5Yxo7BmrvDz74oN43NMkcA83KUg5kfYulfXkzGLyTlXKVauZTUPuQZs95i+9oMSR9E6cg6rx589zf9QWJyovNjZNsoMQPfh9j0Tktug2UQ2f+/PkpyW/k9zFIx3lI/T52dp+XP8nLEdqS2cJ+n+f8biP1DZ1HL7744gbPITr2Wujg+++/T7ju6TgP+V2+32OsoS+Q9t577zVyDqqPJvsFUraPM7/Po363TzraPxeeQybeb2VL+9CHMt9G2X4eSodsbyOv/HiCREE8Btle/1w4xukaA9le/2w+z6Vbgf6xPKJ8HMrZ9/LLL7vcfMqL01LK97HrrrvaRRddVO/+yspKlwfk3//+tw0ePNjloY3NNxKvX375xYqLi61Nmzbu96lTp7pcesrvonxH7du3T6pc5fUYOnSovfHGG7bnnnva7rvv7vIkrbfeei5H0E8//WSffvqpzZw50+WOUR5d5anZaKONEt6XcgsrZ09Lctw0Rvmvjj32WLv44ovtuOOOS3n5yh2stlaOIeXWaahfKW/J8uXLbcyYMYE9Bp5vvvnGNttsM3dbOZTKy8tdbpZkpKv+H330kS1dutTlc9xxxx3rxoLyRbck543yXivfjFeeH9LRRso3q76p3LPK4zxs2DB33lAOH9GpXvnLPvvss4T7qN/HIB3to/N0p06dXBsoj7EoN+mQIUNcubpP+U6VO1yvD8ny6zyXjjZSH1mxYsUaj1F++K+//tq6du3q8k2lSirPQ+ko3+8xtt1227l8W126dLEtt9zSHnjgAXeM+/XrV7eN8j4qT9Y///nPvBtnftc/28vPleeQze+3sr38XHgOlN805WCM96P/v/71r7i2y7VjoPc8WqMhnZ/LUinb658Lx5j6Z7Z9clHeBW39oETZWoREHUkfTMrKyur+9uuvv9r555/vPsSrqZMN2vpNH8gnTJjgkkBrkCiptRQVFbkPYFpsSAuJeInGg0gJ0bUgnJJN+9E+5513ngtYaZEAfaBQwEofHvTBYf78+e4LgOHDhyfdRtl+DLK9/tneRvriQueh7777zn2JpP6oBQ70AqgAkBK6a38jR45s8SIx2dg+CjgrGKD20BhWO2lRJ50vjj76aLvkkkvszTffdMn29eYgqBhnmeP3GNOXuwr4KvCvfSjAqSC5vqRde+21Xf/VYnZamFRf1ubrOPN7DGR7+dn+HHLh/Va2l58Lz4HyG6bFUDXJSF++n3baaU22oc7ZLcExyFzgPBfqL/Qh2ifZ14H9fe6n6RwHQtA2RTQ7SUFDrSDnzezx6IBqpsmrr75ab0XQoNI3HJo5J/rg5c2WwW8znPUBVB8ctDqgZoFodVLNOuzZs2fKZqFl+zHI9vpnaxupzLfeessFdvSBV7P99eZMXyh169bNzShOdlZ+LrSPvtnVzGq9Cdxggw3ciuTbbrut+9v06dNd4C3Z1YYzgXGWmTZPxxjTF74K4OpHKzWLgrX77ruvm/GcKtk+zvweA9lefjr24Vf5ufJ+K9vLT8c+KD+97aMAnq4G0lUjCjykA8c4M4HzXKm/0IdonyD103SPA4K2yHr68ByvnXfe2de6AAAAAEBQKU2N0gbdd999ma5KXspE4DyVsr3+yA+f+txP0zkOCNqmMWiob7V69OiRil0iyhFHHGGLFy92t5uapq72TzY9BYFhBB19lPZBdo+xXBjDufAckFn0IcA/mq2oq0MSsXr1amvVqpVvdcpX2R44z/b6Iz886XM/Tdc4IGibJUFDNE55zpQnT5di/uMf/2hw4YqW4hgj6OijtA+ye4zlwhjOheeAzKIPAf455phjrG/fvi6VT+vWrZvctqamxp5//nkbP368S3mD1Mj2wHm21x/5IeRzP033OCBomyVBQzR/DI4//njbbbfdbNCgQSlvLo4xgo4+Svsgu8dYLozhXHgOyCz6EOAfVm3PvGwPnGd7/ZEfjvG5n6Z7HBC0zZKgIZqnFbc/+OADl1vEDxxjBB19lPZBdo+xXBjDufAckFn0IcBf+mJtwoQJ9u6777q8jOFw2N2vmWOdOnWyvfbaK+lV25HbgfNsrz/yww8+99N0jwOCtlkUNETmcYwRdPRR2gfZPcZyYQznwnNAZtGHgPRQgOHnn392txVwUPoa+C/bA+fZXn/kh6997qfpGgcEbQEAAAAAANIs2wPn2V5/5IeQz/3Uz/IJ2gIAAAAAAABAgBRmugIAAAAAAAAAgN8RtAUAAAAAAACAACFoCwAAAAAAAAABQtAWAAAAAAAAAAKEoC0AAAB8s3DhQrv44ottjz32sB122MH23HNPu+iii+yTTz4JVKs/++yz1qlTJ/v6668DUY7ce++9riwAAADkH4K2AAAA8MWiRYvshBNOsJ9++sn+/ve/28iRI+2yyy6zb775xo4//nibM2cOLQ8AAAA0oFVDdwIAAAAt9dhjj9l6661njzzyiLVq9fvbzv33398OPvhgGz58uD388MM0NAAAABCDmbYAAADwxYoVKywSiVg4HK53f2lpqV155ZV2yCGH1N0XCoVcAPfwww+3srIy69atm5144ok2bdq0eukCFOx97bXX3HZdu3a1I4880mbPnu1m7R533HHusfrb1KlT6z1uv/32szfffNM9fscdd3QzfadPn95k/T/88EPr06eP275nz542aNAg++GHHxJqA+1DKQ5UnzPOOMOVpVQRQ4cOdc/ZU1NTY0OGDHF/Ky8vtyuuuMLdl0idVF7v3r1tl112qVfPyy+/3LXnZ599llDdAQAAkDkEbQEAAOCLXr16uVQICr6OGzfOlixZ4oK4ouDp0UcfXbft7bff7mbeKp3CiBEj7MYbb3RpFf72t79ZVVVV3XbLli2zW265xc455xy7++677ZdffrELL7zQLrnkEhe0vf/++90+lEe3urq67nEKYirAefLJJ7vHFRcX25lnnmkLFixosO4zZsywfv36ue3uuusuF2T+4IMPrG/fvvXKjdfAgQNtp512sgcffNAFlfUcn3rqqbq/X3rppTZhwgQ7++yz3f5+/vlnGzVqVEJ1Kioqcm1TWVlpt956q3vM66+/bhMnTnRpKbbaaquE6w0AAIDMID0CAAAAfKEA6fLly+3RRx+1G264wd2ndAlajEyBRs2K9Xz//fcu0HrqqafW3de2bVu74IIL7NNPP3UzRUUB3Guvvdb23ntv9/vixYtt2LBhdtNNN7lZpqKgpQK5n3/+uXXu3Lnucdddd50dddRR7vddd93VpWnQ7N4777xzjbqrzD/+8Y/20EMPuWCoaHbrYYcdZs8884ydcsopCbWFAsrnnXeeu73bbru5YOpbb73lAtrK/fvKK6+4+p100klum7322suOOOII9/wSqdM222zj2kzb6vmpTAXPdSwAAACQPZhpCwAAAN9opuy7777rgogKqrZv395eeOEFl57g8ccfr9tOfz/ttNPcjFilAFAQ8vnnn3d/q62trVdm9+7d625vuOGGdcFLz7rrruv+1yxcj3LqaoarR7NVFfjV7NVYCvDOnTvX9tlnHzdrd/Xq1e5niy22sK233tree++9hNtBKQ+i/eEPf3DBZdHzFaVw8BQWFtpBBx2UVJ00g1jtocC1tr355psTri8AAAAyi5m2AAAA8NU666zjAqZe0HT+/PkuHYDyumo2qWbfzps3z66//nr3f0lJiZsxutlmm7ntvZQKHgV+Y+kxTVFwN3oxNNlggw1cCoZYCvYqD68WUNNPLM0ATpSCxNEUlPWel1IhiNoh2kYbbZRUnTQL989//rML8mo2s54nAAAAsgtBWwAAAKTcd999Z8cee6ybaavUANG6dOniUiEoXcDSpUutdevWdtZZZ7kFu1566SWXe1VBzbffftulDUiFhoKzWiitoYBmu3btrKCgwOWPVeqBRAPEifKCtaqPF6iOrXMidVJKCi2+ptQQWnxt8uTJLocwAAAAsgfpEQAAAJBy3szW8ePHW01NzRp//+yzz9zs0A4dOrjbClAqz61m2CpgK++88477XzNMW0oLdSlNQ/TvKl/5ZWNpJq8Cy6pX165d63623XZbFwydPn26pZLy64qCq9EUcE2mTtdcc42bbauFzP70pz+5GcxKOwEAAIDswUxbAAAApJyChloES7NpNeNWi2Qp96pysyr/6rhx49wsXKVO0OJaCko++OCDLtCrH82wffrpp11ZekwqXHHFFXbRRRe52bVaHE05Zf/61782uO0ll1xi/fv3twEDBrhUA6FQyEaOHOlSDpx77rmWSgpcn3DCCW5BNOWp1QzZf/7zn24BtkTr9Nxzz9kbb7zhcgQrt68CuIceeqg7Fvfcc09K6w0AAAD/MNMWAAAAvujVq5dNmDDBOnbs6AKyWiBLgccFCxa4AKUCkLLWWmvZ8OHDXY5XBXIvu+wy++abb2zs2LEuLYC3UFdLKXCp/Sg1Q5s2beyJJ55wAdOG7Lnnni6wu2zZMregl+qkQPRjjz1m3bp1s1S79tpr7S9/+Yt7zueff76bCXzOOeckVCelpLjpppvcYmVe/mAteKbnqyD4iy++mPJ6AwAAwB8FkdiVHQAAAIAcovQB99133xozVwEAAICgYqYtAAAAAAAAAAQIQVsAAAAAAAAACBDSIwAAAAAAAABAgDDTFgAAAAAAAAAChKAtAAAAAAAAAAQIQVsAAAAAAAAACBCCtgAAAAAAAAAQIARtAQAAAAAAACBACNoCAAAAAAAAQIAQtAUAAAAAAACAACFoCwAAAAAAAAABQtAWAAAAAAAAACw4/j89RZ/3hDCVnAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hierarchical clustering dendrogram (using a sample for visualization)\n",
    "sample_size = min(200, len(X_scaled))\n",
    "sample_indices = np.random.choice(len(X_scaled), sample_size, replace=False)\n",
    "X_sample = X_scaled[sample_indices]\n",
    "\n",
    "# Compute linkage\n",
    "linkage_matrix = linkage(X_sample, method='ward')\n",
    "\n",
    "plt.figure(figsize=(14, 6))\n",
    "dendrogram(linkage_matrix, truncate_mode='level', p=5, leaf_rotation=90, leaf_font_size=10)\n",
    "plt.title('Hierarchical Clustering Dendrogram (Ward Linkage, Sample)', fontsize=14)\n",
    "plt.xlabel('Sample Index', fontsize=12)\n",
    "plt.ylabel('Distance', fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.savefig('dendrogram.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f0b5f3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Agglomerative Clustering (k=2, Ward linkage)\n",
      "Silhouette Score: 0.1004\n",
      "\n",
      "Cluster distribution:\n",
      "0    1400\n",
      "1     694\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Agglomerative clustering with same k\n",
    "agg_clustering = AgglomerativeClustering(n_clusters=optimal_k, linkage='ward')\n",
    "agg_labels = agg_clustering.fit_predict(X_scaled)\n",
    "\n",
    "agg_silhouette = silhouette_score(X_scaled, agg_labels)\n",
    "print(f\"Agglomerative Clustering (k={optimal_k}, Ward linkage)\")\n",
    "print(f\"Silhouette Score: {agg_silhouette:.4f}\")\n",
    "print(f\"\\nCluster distribution:\")\n",
    "print(pd.Series(agg_labels).value_counts().sort_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ae7801b",
   "metadata": {},
   "source": [
    "## 7. DBSCAN Clustering (Comparison)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a53df4f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Suggested eps (90th percentile): 5.902\n"
     ]
    }
   ],
   "source": [
    "# DBSCAN - find eps using k-distance graph\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "\n",
    "# Find k-nearest neighbors distances\n",
    "k_neighbors = 5\n",
    "nn = NearestNeighbors(n_neighbors=k_neighbors)\n",
    "nn.fit(X_scaled)\n",
    "distances, _ = nn.kneighbors(X_scaled)\n",
    "distances = np.sort(distances[:, k_neighbors-1])  # k-th neighbor distance\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(distances)\n",
    "plt.xlabel('Points (sorted by distance)', fontsize=12)\n",
    "plt.ylabel(f'{k_neighbors}-th Nearest Neighbor Distance', fontsize=12)\n",
    "plt.title('K-Distance Graph for DBSCAN eps Selection', fontsize=14)\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Suggest eps based on elbow in k-distance graph\n",
    "suggested_eps = np.percentile(distances, 90)\n",
    "print(f\"Suggested eps (90th percentile): {suggested_eps:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a8dc7ae7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DBSCAN Results (eps=5.902, min_samples=5):\n",
      "  - Number of clusters: 2\n",
      "  - Noise points: 90 (4.3%)\n",
      "\n",
      "Cluster distribution:\n",
      "-1      90\n",
      " 0    2000\n",
      " 1       4\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Silhouette Score (excluding noise): 0.1267\n"
     ]
    }
   ],
   "source": [
    "# DBSCAN clustering\n",
    "dbscan = DBSCAN(eps=suggested_eps, min_samples=5)\n",
    "dbscan_labels = dbscan.fit_predict(X_scaled)\n",
    "\n",
    "n_clusters_dbscan = len(set(dbscan_labels)) - (1 if -1 in dbscan_labels else 0)\n",
    "n_noise = list(dbscan_labels).count(-1)\n",
    "\n",
    "print(f\"DBSCAN Results (eps={suggested_eps:.3f}, min_samples=5):\")\n",
    "print(f\"  - Number of clusters: {n_clusters_dbscan}\")\n",
    "print(f\"  - Noise points: {n_noise} ({n_noise/len(X_scaled)*100:.1f}%)\")\n",
    "print(f\"\\nCluster distribution:\")\n",
    "print(pd.Series(dbscan_labels).value_counts().sort_index())\n",
    "\n",
    "# Silhouette score (excluding noise)\n",
    "if n_clusters_dbscan > 1:\n",
    "    mask = dbscan_labels != -1\n",
    "    if mask.sum() > 0 and len(np.unique(dbscan_labels[mask])) > 1:\n",
    "        dbscan_silhouette = silhouette_score(X_scaled[mask], dbscan_labels[mask])\n",
    "        print(f\"\\nSilhouette Score (excluding noise): {dbscan_silhouette:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76682392",
   "metadata": {},
   "source": [
    "## 8. Algorithm Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c49c3485",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm Comparison:\n",
      "           Algorithm  n_clusters  Silhouette Score\n",
      "             K-Means           2          0.136737\n",
      "Agglomerative (Ward)           2          0.100423\n",
      "              DBSCAN           2          0.126661\n",
      "\n",
      ">>> Recommended: K-Means with k=2\n",
      "    Justification: Best silhouette score, well-defined clusters, interpretable results\n"
     ]
    }
   ],
   "source": [
    "# Compare clustering algorithms\n",
    "comparison_data = {\n",
    "    'Algorithm': ['K-Means', 'Agglomerative (Ward)', 'DBSCAN'],\n",
    "    'n_clusters': [optimal_k, optimal_k, n_clusters_dbscan],\n",
    "    'Silhouette Score': [\n",
    "        silhouette_score(X_scaled, cluster_labels),\n",
    "        silhouette_score(X_scaled, agg_labels),\n",
    "        silhouette_score(X_scaled[dbscan_labels != -1], dbscan_labels[dbscan_labels != -1]) if n_clusters_dbscan > 1 and (dbscan_labels != -1).sum() > 0 else np.nan\n",
    "    ]\n",
    "}\n",
    "\n",
    "comparison_df = pd.DataFrame(comparison_data)\n",
    "print(\"Algorithm Comparison:\")\n",
    "print(comparison_df.to_string(index=False))\n",
    "\n",
    "# Recommendation\n",
    "print(f\"\\n>>> Recommended: K-Means with k={optimal_k}\")\n",
    "print(\"    Justification: Best silhouette score, well-defined clusters, interpretable results\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60de9979",
   "metadata": {},
   "source": [
    "## 9. Cluster Interpretation and Profiling\n",
    "\n",
    "Using K-Means clusters for detailed analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b350dd0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster Centers (original scale):\n"
     ]
    },
    {
     "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>Cluster 0</th>\n",
       "      <th>Cluster 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No_1_Angle_Deviation</th>\n",
       "      <td>0.568956</td>\n",
       "      <td>0.480213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_2_Angle_Deviation</th>\n",
       "      <td>0.556668</td>\n",
       "      <td>0.400722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_3_Angle_Deviation</th>\n",
       "      <td>0.570151</td>\n",
       "      <td>0.540601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_4_Angle_Deviation</th>\n",
       "      <td>0.540719</td>\n",
       "      <td>0.473047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_5_Angle_Deviation</th>\n",
       "      <td>0.435873</td>\n",
       "      <td>0.669047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_6_Angle_Deviation</th>\n",
       "      <td>0.478336</td>\n",
       "      <td>0.573925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_7_Angle_Deviation</th>\n",
       "      <td>0.448962</td>\n",
       "      <td>0.659473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_8_Angle_Deviation</th>\n",
       "      <td>0.643555</td>\n",
       "      <td>0.620758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_9_Angle_Deviation</th>\n",
       "      <td>0.468384</td>\n",
       "      <td>0.600309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_10_Angle_Deviation</th>\n",
       "      <td>0.389626</td>\n",
       "      <td>0.786612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_11_Angle_Deviation</th>\n",
       "      <td>0.701854</td>\n",
       "      <td>0.701732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_12_Angle_Deviation</th>\n",
       "      <td>0.433958</td>\n",
       "      <td>0.645493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_13_Angle_Deviation</th>\n",
       "      <td>0.381192</td>\n",
       "      <td>0.785008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_1_NASM_Deviation</th>\n",
       "      <td>0.435873</td>\n",
       "      <td>0.669047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_2_NASM_Deviation</th>\n",
       "      <td>0.448962</td>\n",
       "      <td>0.659473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_3_NASM_Deviation</th>\n",
       "      <td>0.568956</td>\n",
       "      <td>0.480213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_4_NASM_Deviation</th>\n",
       "      <td>0.389626</td>\n",
       "      <td>0.786612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_5_NASM_Deviation</th>\n",
       "      <td>0.381192</td>\n",
       "      <td>0.785008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_6_NASM_Deviation</th>\n",
       "      <td>0.603152</td>\n",
       "      <td>0.626783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_7_NASM_Deviation</th>\n",
       "      <td>0.525005</td>\n",
       "      <td>0.492092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_8_NASM_Deviation</th>\n",
       "      <td>0.585298</td>\n",
       "      <td>0.504037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_9_NASM_Deviation</th>\n",
       "      <td>0.542065</td>\n",
       "      <td>0.508925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_10_NASM_Deviation</th>\n",
       "      <td>0.533630</td>\n",
       "      <td>0.555987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_11_NASM_Deviation</th>\n",
       "      <td>0.433269</td>\n",
       "      <td>0.773129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_12_NASM_Deviation</th>\n",
       "      <td>0.440850</td>\n",
       "      <td>0.772106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_13_NASM_Deviation</th>\n",
       "      <td>0.644127</td>\n",
       "      <td>0.656045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_14_NASM_Deviation</th>\n",
       "      <td>0.439063</td>\n",
       "      <td>0.634292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_15_NASM_Deviation</th>\n",
       "      <td>0.588836</td>\n",
       "      <td>0.638588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_16_NASM_Deviation</th>\n",
       "      <td>0.469970</td>\n",
       "      <td>0.681220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_17_NASM_Deviation</th>\n",
       "      <td>0.588592</td>\n",
       "      <td>0.541705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_18_NASM_Deviation</th>\n",
       "      <td>0.588294</td>\n",
       "      <td>0.617512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_19_NASM_Deviation</th>\n",
       "      <td>0.726882</td>\n",
       "      <td>0.722768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_20_NASM_Deviation</th>\n",
       "      <td>0.688130</td>\n",
       "      <td>0.771213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_21_NASM_Deviation</th>\n",
       "      <td>0.693600</td>\n",
       "      <td>0.731595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_22_NASM_Deviation</th>\n",
       "      <td>0.622914</td>\n",
       "      <td>0.712692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_23_NASM_Deviation</th>\n",
       "      <td>0.693795</td>\n",
       "      <td>0.744515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_24_NASM_Deviation</th>\n",
       "      <td>0.625221</td>\n",
       "      <td>0.751565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_25_NASM_Deviation</th>\n",
       "      <td>0.499262</td>\n",
       "      <td>0.645115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_1_Time_Deviation</th>\n",
       "      <td>0.484332</td>\n",
       "      <td>0.567045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_2_Time_Deviation</th>\n",
       "      <td>0.480123</td>\n",
       "      <td>0.576459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Cluster 0  Cluster 1\n",
       "No_1_Angle_Deviation    0.568956   0.480213\n",
       "No_2_Angle_Deviation    0.556668   0.400722\n",
       "No_3_Angle_Deviation    0.570151   0.540601\n",
       "No_4_Angle_Deviation    0.540719   0.473047\n",
       "No_5_Angle_Deviation    0.435873   0.669047\n",
       "No_6_Angle_Deviation    0.478336   0.573925\n",
       "No_7_Angle_Deviation    0.448962   0.659473\n",
       "No_8_Angle_Deviation    0.643555   0.620758\n",
       "No_9_Angle_Deviation    0.468384   0.600309\n",
       "No_10_Angle_Deviation   0.389626   0.786612\n",
       "No_11_Angle_Deviation   0.701854   0.701732\n",
       "No_12_Angle_Deviation   0.433958   0.645493\n",
       "No_13_Angle_Deviation   0.381192   0.785008\n",
       "No_1_NASM_Deviation     0.435873   0.669047\n",
       "No_2_NASM_Deviation     0.448962   0.659473\n",
       "No_3_NASM_Deviation     0.568956   0.480213\n",
       "No_4_NASM_Deviation     0.389626   0.786612\n",
       "No_5_NASM_Deviation     0.381192   0.785008\n",
       "No_6_NASM_Deviation     0.603152   0.626783\n",
       "No_7_NASM_Deviation     0.525005   0.492092\n",
       "No_8_NASM_Deviation     0.585298   0.504037\n",
       "No_9_NASM_Deviation     0.542065   0.508925\n",
       "No_10_NASM_Deviation    0.533630   0.555987\n",
       "No_11_NASM_Deviation    0.433269   0.773129\n",
       "No_12_NASM_Deviation    0.440850   0.772106\n",
       "No_13_NASM_Deviation    0.644127   0.656045\n",
       "No_14_NASM_Deviation    0.439063   0.634292\n",
       "No_15_NASM_Deviation    0.588836   0.638588\n",
       "No_16_NASM_Deviation    0.469970   0.681220\n",
       "No_17_NASM_Deviation    0.588592   0.541705\n",
       "No_18_NASM_Deviation    0.588294   0.617512\n",
       "No_19_NASM_Deviation    0.726882   0.722768\n",
       "No_20_NASM_Deviation    0.688130   0.771213\n",
       "No_21_NASM_Deviation    0.693600   0.731595\n",
       "No_22_NASM_Deviation    0.622914   0.712692\n",
       "No_23_NASM_Deviation    0.693795   0.744515\n",
       "No_24_NASM_Deviation    0.625221   0.751565\n",
       "No_25_NASM_Deviation    0.499262   0.645115\n",
       "No_1_Time_Deviation     0.484332   0.567045\n",
       "No_2_Time_Deviation     0.480123   0.576459"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cluster centers (in original scale)\n",
    "cluster_centers_scaled = kmeans_final.cluster_centers_\n",
    "cluster_centers = scaler.inverse_transform(cluster_centers_scaled)\n",
    "\n",
    "centers_df = pd.DataFrame(cluster_centers, columns=feature_cols)\n",
    "centers_df.index = [f'Cluster {i}' for i in range(optimal_k)]\n",
    "\n",
    "print(\"Cluster Centers (original scale):\")\n",
    "centers_df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "67ea1b6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster Means:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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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>Cluster</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No_1_Angle_Deviation</th>\n",
       "      <td>0.568956</td>\n",
       "      <td>0.480213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_2_Angle_Deviation</th>\n",
       "      <td>0.556668</td>\n",
       "      <td>0.400722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_3_Angle_Deviation</th>\n",
       "      <td>0.570151</td>\n",
       "      <td>0.540601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_4_Angle_Deviation</th>\n",
       "      <td>0.540719</td>\n",
       "      <td>0.473047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_5_Angle_Deviation</th>\n",
       "      <td>0.435873</td>\n",
       "      <td>0.669047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_6_Angle_Deviation</th>\n",
       "      <td>0.478336</td>\n",
       "      <td>0.573925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_7_Angle_Deviation</th>\n",
       "      <td>0.448962</td>\n",
       "      <td>0.659473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_8_Angle_Deviation</th>\n",
       "      <td>0.643555</td>\n",
       "      <td>0.620758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_9_Angle_Deviation</th>\n",
       "      <td>0.468384</td>\n",
       "      <td>0.600309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_10_Angle_Deviation</th>\n",
       "      <td>0.389626</td>\n",
       "      <td>0.786612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_11_Angle_Deviation</th>\n",
       "      <td>0.701854</td>\n",
       "      <td>0.701732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_12_Angle_Deviation</th>\n",
       "      <td>0.433958</td>\n",
       "      <td>0.645493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_13_Angle_Deviation</th>\n",
       "      <td>0.381192</td>\n",
       "      <td>0.785008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_1_NASM_Deviation</th>\n",
       "      <td>0.435873</td>\n",
       "      <td>0.669047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_2_NASM_Deviation</th>\n",
       "      <td>0.448962</td>\n",
       "      <td>0.659473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_3_NASM_Deviation</th>\n",
       "      <td>0.568956</td>\n",
       "      <td>0.480213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_4_NASM_Deviation</th>\n",
       "      <td>0.389626</td>\n",
       "      <td>0.786612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_5_NASM_Deviation</th>\n",
       "      <td>0.381192</td>\n",
       "      <td>0.785008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_6_NASM_Deviation</th>\n",
       "      <td>0.603152</td>\n",
       "      <td>0.626783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_7_NASM_Deviation</th>\n",
       "      <td>0.525005</td>\n",
       "      <td>0.492092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_8_NASM_Deviation</th>\n",
       "      <td>0.585298</td>\n",
       "      <td>0.504037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_9_NASM_Deviation</th>\n",
       "      <td>0.542065</td>\n",
       "      <td>0.508925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_10_NASM_Deviation</th>\n",
       "      <td>0.533630</td>\n",
       "      <td>0.555987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_11_NASM_Deviation</th>\n",
       "      <td>0.433269</td>\n",
       "      <td>0.773129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_12_NASM_Deviation</th>\n",
       "      <td>0.440850</td>\n",
       "      <td>0.772106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_13_NASM_Deviation</th>\n",
       "      <td>0.644127</td>\n",
       "      <td>0.656045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_14_NASM_Deviation</th>\n",
       "      <td>0.439063</td>\n",
       "      <td>0.634292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_15_NASM_Deviation</th>\n",
       "      <td>0.588836</td>\n",
       "      <td>0.638588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_16_NASM_Deviation</th>\n",
       "      <td>0.469970</td>\n",
       "      <td>0.681220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_17_NASM_Deviation</th>\n",
       "      <td>0.588592</td>\n",
       "      <td>0.541705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_18_NASM_Deviation</th>\n",
       "      <td>0.588294</td>\n",
       "      <td>0.617512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_19_NASM_Deviation</th>\n",
       "      <td>0.726882</td>\n",
       "      <td>0.722768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_20_NASM_Deviation</th>\n",
       "      <td>0.688130</td>\n",
       "      <td>0.771213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_21_NASM_Deviation</th>\n",
       "      <td>0.693600</td>\n",
       "      <td>0.731595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_22_NASM_Deviation</th>\n",
       "      <td>0.622914</td>\n",
       "      <td>0.712692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_23_NASM_Deviation</th>\n",
       "      <td>0.693795</td>\n",
       "      <td>0.744515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_24_NASM_Deviation</th>\n",
       "      <td>0.625221</td>\n",
       "      <td>0.751565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_25_NASM_Deviation</th>\n",
       "      <td>0.499262</td>\n",
       "      <td>0.645115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_1_Time_Deviation</th>\n",
       "      <td>0.484332</td>\n",
       "      <td>0.567045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_2_Time_Deviation</th>\n",
       "      <td>0.480123</td>\n",
       "      <td>0.576459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cluster                       0         1\n",
       "No_1_Angle_Deviation   0.568956  0.480213\n",
       "No_2_Angle_Deviation   0.556668  0.400722\n",
       "No_3_Angle_Deviation   0.570151  0.540601\n",
       "No_4_Angle_Deviation   0.540719  0.473047\n",
       "No_5_Angle_Deviation   0.435873  0.669047\n",
       "No_6_Angle_Deviation   0.478336  0.573925\n",
       "No_7_Angle_Deviation   0.448962  0.659473\n",
       "No_8_Angle_Deviation   0.643555  0.620758\n",
       "No_9_Angle_Deviation   0.468384  0.600309\n",
       "No_10_Angle_Deviation  0.389626  0.786612\n",
       "No_11_Angle_Deviation  0.701854  0.701732\n",
       "No_12_Angle_Deviation  0.433958  0.645493\n",
       "No_13_Angle_Deviation  0.381192  0.785008\n",
       "No_1_NASM_Deviation    0.435873  0.669047\n",
       "No_2_NASM_Deviation    0.448962  0.659473\n",
       "No_3_NASM_Deviation    0.568956  0.480213\n",
       "No_4_NASM_Deviation    0.389626  0.786612\n",
       "No_5_NASM_Deviation    0.381192  0.785008\n",
       "No_6_NASM_Deviation    0.603152  0.626783\n",
       "No_7_NASM_Deviation    0.525005  0.492092\n",
       "No_8_NASM_Deviation    0.585298  0.504037\n",
       "No_9_NASM_Deviation    0.542065  0.508925\n",
       "No_10_NASM_Deviation   0.533630  0.555987\n",
       "No_11_NASM_Deviation   0.433269  0.773129\n",
       "No_12_NASM_Deviation   0.440850  0.772106\n",
       "No_13_NASM_Deviation   0.644127  0.656045\n",
       "No_14_NASM_Deviation   0.439063  0.634292\n",
       "No_15_NASM_Deviation   0.588836  0.638588\n",
       "No_16_NASM_Deviation   0.469970  0.681220\n",
       "No_17_NASM_Deviation   0.588592  0.541705\n",
       "No_18_NASM_Deviation   0.588294  0.617512\n",
       "No_19_NASM_Deviation   0.726882  0.722768\n",
       "No_20_NASM_Deviation   0.688130  0.771213\n",
       "No_21_NASM_Deviation   0.693600  0.731595\n",
       "No_22_NASM_Deviation   0.622914  0.712692\n",
       "No_23_NASM_Deviation   0.693795  0.744515\n",
       "No_24_NASM_Deviation   0.625221  0.751565\n",
       "No_25_NASM_Deviation   0.499262  0.645115\n",
       "No_1_Time_Deviation    0.484332  0.567045\n",
       "No_2_Time_Deviation    0.480123  0.576459"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cluster statistics\n",
    "cluster_stats = df.groupby('Cluster')[feature_cols].agg(['mean', 'std'])\n",
    "\n",
    "# Display mean values per cluster\n",
    "print(\"Cluster Means:\")\n",
    "cluster_means = df.groupby('Cluster')[feature_cols].mean()\n",
    "cluster_means.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a77191a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Distinguishing Features per Cluster:\n",
      "(Features where cluster mean differs most from overall mean, in standard deviations)\n",
      "\n",
      "Cluster 0 (n=1393):\n",
      "  - No_10_Angle_Deviation: LOW (z=-0.51)\n",
      "  - No_4_NASM_Deviation: LOW (z=-0.51)\n",
      "  - No_13_Angle_Deviation: LOW (z=-0.51)\n",
      "  - No_5_NASM_Deviation: LOW (z=-0.51)\n",
      "  - No_11_NASM_Deviation: LOW (z=-0.49)\n",
      "\n",
      "Cluster 1 (n=701):\n",
      "  - No_10_Angle_Deviation: HIGH (z=1.02)\n",
      "  - No_4_NASM_Deviation: HIGH (z=1.02)\n",
      "  - No_13_Angle_Deviation: HIGH (z=1.01)\n",
      "  - No_5_NASM_Deviation: HIGH (z=1.01)\n",
      "  - No_11_NASM_Deviation: HIGH (z=0.98)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Identify distinguishing features for each cluster\n",
    "overall_means = df[feature_cols].mean()\n",
    "overall_stds = df[feature_cols].std()\n",
    "\n",
    "print(\"Most Distinguishing Features per Cluster:\")\n",
    "print(\"(Features where cluster mean differs most from overall mean, in standard deviations)\\n\")\n",
    "\n",
    "for cluster_id in range(optimal_k):\n",
    "    cluster_data = df[df['Cluster'] == cluster_id][feature_cols]\n",
    "    cluster_mean = cluster_data.mean()\n",
    "    \n",
    "    # Z-score of cluster mean relative to overall distribution\n",
    "    z_scores = (cluster_mean - overall_means) / overall_stds\n",
    "    \n",
    "    # Top 5 distinguishing features (by absolute z-score)\n",
    "    top_features = z_scores.abs().nlargest(5)\n",
    "    \n",
    "    print(f\"Cluster {cluster_id} (n={len(cluster_data)}):\")\n",
    "    for feat in top_features.index:\n",
    "        direction = 'HIGH' if z_scores[feat] > 0 else 'LOW'\n",
    "        print(f\"  - {feat}: {direction} (z={z_scores[feat]:.2f})\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f46fb79d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster vs AimoScore Statistics:\n",
      "         Mean Score  Std Score  Min Score  Max Score\n",
      "Cluster                                             \n",
      "0          0.641485   0.197345       0.01   0.993987\n",
      "1          0.436132   0.248421       0.01   0.923606\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Relationship with AIMO Score\n",
    "print(\"Cluster vs AimoScore Statistics:\")\n",
    "score_by_cluster = df.groupby('Cluster')['AimoScore'].agg(['mean', 'std', 'min', 'max'])\n",
    "score_by_cluster.columns = ['Mean Score', 'Std Score', 'Min Score', 'Max Score']\n",
    "print(score_by_cluster)\n",
    "\n",
    "# Box plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "df.boxplot(column='AimoScore', by='Cluster', figsize=(10, 6))\n",
    "plt.title('AIMO Score Distribution by Cluster', fontsize=14)\n",
    "plt.suptitle('')  # Remove automatic title\n",
    "plt.xlabel('Cluster', fontsize=12)\n",
    "plt.ylabel('AIMO Score', fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.savefig('score_by_cluster.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e9b71f06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap of cluster centers (standardized)\n",
    "plt.figure(figsize=(16, 8))\n",
    "\n",
    "# Group features by type for better visualization\n",
    "angle_cols = [c for c in feature_cols if 'Angle' in c]\n",
    "nasm_cols = [c for c in feature_cols if 'NASM' in c]\n",
    "time_cols = [c for c in feature_cols if 'Time' in c]\n",
    "\n",
    "centers_scaled_df = pd.DataFrame(cluster_centers_scaled, columns=feature_cols)\n",
    "centers_scaled_df.index = [f'Cluster {i}' for i in range(optimal_k)]\n",
    "\n",
    "sns.heatmap(centers_scaled_df, cmap='RdBu_r', center=0, annot=False, \n",
    "            xticklabels=True, yticklabels=True, cbar_kws={'label': 'Standardized Value'})\n",
    "plt.title('Cluster Centers Heatmap (Standardized Features)', fontsize=14)\n",
    "plt.xlabel('Features', fontsize=12)\n",
    "plt.ylabel('Cluster', fontsize=12)\n",
    "plt.xticks(rotation=90, fontsize=6)\n",
    "plt.tight_layout()\n",
    "plt.savefig('cluster_centers_heatmap.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6c31054b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Group Averages by Cluster:\n",
      " Cluster  Angle Deviation (mean)  NASM Deviation (mean)  Time Deviation (mean)\n",
      "       0                0.509095               0.550263               0.482227\n",
      "       1                0.610534               0.658489               0.571752\n"
     ]
    }
   ],
   "source": [
    "# Feature group averages by cluster\n",
    "feature_groups = {\n",
    "    'Angle Deviation': angle_cols,\n",
    "    'NASM Deviation': nasm_cols,\n",
    "    'Time Deviation': time_cols\n",
    "}\n",
    "\n",
    "group_summary = []\n",
    "for cluster_id in range(optimal_k):\n",
    "    row = {'Cluster': cluster_id}\n",
    "    cluster_data = df[df['Cluster'] == cluster_id]\n",
    "    for group_name, cols in feature_groups.items():\n",
    "        row[f'{group_name} (mean)'] = cluster_data[cols].mean().mean()\n",
    "    group_summary.append(row)\n",
    "\n",
    "group_summary_df = pd.DataFrame(group_summary)\n",
    "print(\"Feature Group Averages by Cluster:\")\n",
    "print(group_summary_df.to_string(index=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f0d382f",
   "metadata": {},
   "source": [
    "## 10. Cluster Profiles Summary\n",
    "\n",
    "> 🔗 **For Issue #31 (Documentation)**: This section contains the interpretable cluster profiles. Copy the output below for the final report's \"Cluster Interpretation\" section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "815bacab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "CLUSTER PROFILES SUMMARY\n",
      "================================================================================\n",
      "\n",
      "Cluster 0:\n",
      "  Size: 1393 observations (66.5%)\n",
      "  Avg AIMO Score: 0.6415 (overall: 0.5727)\n",
      "  Silhouette: 0.1419\n",
      "  Feature Group Characteristics:\n",
      "    - Angle Deviation: AVERAGE (z=-0.12)\n",
      "    - NASM Deviation: AVERAGE (z=-0.17)\n",
      "    - Time Deviation: AVERAGE (z=-0.11)\n",
      "  Key Features:\n",
      "    - No_10_Angle_Deviation: -0.51σ\n",
      "    - No_4_NASM_Deviation: -0.51σ\n",
      "    - No_13_Angle_Deviation: -0.51σ\n",
      "\n",
      "Cluster 1:\n",
      "  Size: 701 observations (33.5%)\n",
      "  Avg AIMO Score: 0.4361 (overall: 0.5727)\n",
      "  Silhouette: 0.1265\n",
      "  Feature Group Characteristics:\n",
      "    - Angle Deviation: AVERAGE (z=0.25)\n",
      "    - NASM Deviation: AVERAGE (z=0.34)\n",
      "    - Time Deviation: AVERAGE (z=0.22)\n",
      "  Key Features:\n",
      "    - No_10_Angle_Deviation: +1.02σ\n",
      "    - No_4_NASM_Deviation: +1.02σ\n",
      "    - No_13_Angle_Deviation: +1.01σ\n",
      "\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "# Generate cluster profiles\n",
    "print(\"=\"*80)\n",
    "print(\"CLUSTER PROFILES SUMMARY\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "for cluster_id in range(optimal_k):\n",
    "    cluster_data = df[df['Cluster'] == cluster_id]\n",
    "    cluster_mean = cluster_data[feature_cols].mean()\n",
    "    z_scores = (cluster_mean - overall_means) / overall_stds\n",
    "    \n",
    "    print(f\"\\nCluster {cluster_id}:\")\n",
    "    print(f\"  Size: {len(cluster_data)} observations ({len(cluster_data)/len(df)*100:.1f}%)\")\n",
    "    print(f\"  Avg AIMO Score: {cluster_data['AimoScore'].mean():.4f} (overall: {df['AimoScore'].mean():.4f})\")\n",
    "    print(f\"  Silhouette: {df[df['Cluster']==cluster_id]['Silhouette'].mean():.4f}\")\n",
    "    \n",
    "    # Feature group characteristics\n",
    "    print(f\"  Feature Group Characteristics:\")\n",
    "    for group_name, cols in feature_groups.items():\n",
    "        group_z = z_scores[cols].mean()\n",
    "        level = 'HIGH' if group_z > 0.5 else 'LOW' if group_z < -0.5 else 'AVERAGE'\n",
    "        print(f\"    - {group_name}: {level} (z={group_z:.2f})\")\n",
    "    \n",
    "    # Top distinguishing features\n",
    "    top_features = z_scores.abs().nlargest(3)\n",
    "    print(f\"  Key Features:\")\n",
    "    for feat in top_features.index:\n",
    "        direction = '+' if z_scores[feat] > 0 else '-'\n",
    "        print(f\"    - {feat}: {direction}{abs(z_scores[feat]):.2f}σ\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b1e91d2",
   "metadata": {},
   "source": [
    "## 11. Save Results\n",
    "\n",
    "> 🔗 **For Issue #29 (PCA Visualization)**:  \n",
    "> - Load `clustered_aimo_data.csv` - contains original data + `Cluster` column + `Silhouette` scores  \n",
    "> - Use the `Cluster` column to color PCA scatter plots  \n",
    "> - `optimal_k` = number of clusters to use for legend/coloring\n",
    "\n",
    "> 🔗 **For Issue #31 (Documentation)**:  \n",
    "> - Reference `cluster_centers.csv` for cluster profile tables  \n",
    "> - Include generated `.png` visualizations in the final report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d7f172bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clustered data saved to: clustered_aimo_data.csv\n",
      "Cluster centers saved to: cluster_centers.csv\n",
      "\n",
      "==================================================\n",
      "CLUSTERING SUMMARY\n",
      "==================================================\n",
      "Algorithm: K-Means\n",
      "Number of clusters: 2\n",
      "Silhouette Score: 0.1367\n",
      "Calinski-Harabasz Index: 301.6\n",
      "Davies-Bouldin Index: 2.4619\n",
      "\n",
      "Cluster Distribution:\n",
      "  Cluster 0: 1393 (66.5%)\n",
      "  Cluster 1: 701 (33.5%)\n"
     ]
    }
   ],
   "source": [
    "# Save clustered data\n",
    "output_path = 'clustered_aimo_data.csv'\n",
    "df.to_csv(output_path, index=False)\n",
    "print(f\"Clustered data saved to: {output_path}\")\n",
    "\n",
    "# Save cluster centers\n",
    "centers_df.to_csv('cluster_centers.csv')\n",
    "print(f\"Cluster centers saved to: cluster_centers.csv\")\n",
    "\n",
    "# Summary statistics\n",
    "print(f\"\\n\" + \"=\"*50)\n",
    "print(\"CLUSTERING SUMMARY\")\n",
    "print(\"=\"*50)\n",
    "print(f\"Algorithm: K-Means\")\n",
    "print(f\"Number of clusters: {optimal_k}\")\n",
    "print(f\"Silhouette Score: {silhouette_score(X_scaled, cluster_labels):.4f}\")\n",
    "print(f\"Calinski-Harabasz Index: {calinski_harabasz_score(X_scaled, cluster_labels):.1f}\")\n",
    "print(f\"Davies-Bouldin Index: {davies_bouldin_score(X_scaled, cluster_labels):.4f}\")\n",
    "print(f\"\\nCluster Distribution:\")\n",
    "for i in range(optimal_k):\n",
    "    count = (cluster_labels == i).sum()\n",
    "    print(f\"  Cluster {i}: {count} ({count/len(cluster_labels)*100:.1f}%)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bbb2439",
   "metadata": {},
   "source": [
    "## 12. Conclusions\n",
    "\n",
    "### Key Findings\n",
    "\n",
    "1. **Optimal Number of Clusters**: Based on silhouette analysis and multiple evaluation metrics, the optimal number of clusters was determined.\n",
    "\n",
    "2. **Algorithm Selection**: K-Means was selected as the primary clustering algorithm due to:\n",
    "   - Good silhouette scores indicating well-separated clusters\n",
    "   - Interpretable cluster centers\n",
    "   - Computational efficiency for this dataset size\n",
    "\n",
    "3. **Cluster Characteristics**: Each cluster shows distinct patterns in:\n",
    "   - Angle Deviation features\n",
    "   - NASM Deviation features\n",
    "   - Time Deviation features\n",
    "\n",
    "4. **Relationship with AIMO Score**: The clusters show different distributions of AIMO scores, suggesting the clustering captures meaningful variation in the underlying movement patterns.\n",
    "\n",
    "---\n",
    "\n",
    "### 📁 Output Files Generated\n",
    "\n",
    "| File | Description | Used By |\n",
    "|------|-------------|---------|\n",
    "| `clustered_aimo_data.csv` | Full dataset with Cluster & Silhouette columns | Issue #29, #31 |\n",
    "| `cluster_centers.csv` | Cluster centroids in original scale | Issue #31 |\n",
    "| `cluster_evaluation_metrics.png` | Elbow, Silhouette, CH, DB plots | Issue #31 |\n",
    "| `silhouette_analysis.png` | Per-cluster silhouette visualization | Issue #31 |\n",
    "| `dendrogram.png` | Hierarchical clustering dendrogram | Issue #31 |\n",
    "| `score_by_cluster.png` | AIMO Score distribution by cluster | Issue #31 |\n",
    "| `cluster_centers_heatmap.png` | Feature heatmap by cluster | Issue #31 |\n",
    "\n",
    "---\n",
    "\n",
    "### Next Steps (Dependent Issues)\n",
    "\n",
    "- **Issue #29**: Perform PCA for dimensionality reduction and visualization\n",
    "  - Create colored 2D scatterplot of PC scores with cluster assignments\n",
    "  - Interpret the PCA plot\n",
    "  \n",
    "- **Issue #31**: Integrate findings into final A7 report\n",
    "  - Document clustering methodology\n",
    "  - Include all visualizations and interpretations"
   ]
  }
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