diff --git "a/A7/A7_Report.ipynb" "b/A7/A7_Report.ipynb"
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@@ -0,0 +1,1469 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bdaf8935",
+   "metadata": {},
+   "source": [
+    "# A7: PCA Analysis & Cluster Visualization\n",
+    "\n",
+    "This notebook completes **Issue #29: PCA Analysis & Cluster Visualization** for the AIMO observations.\n",
+    "\n",
+    "## Objective\n",
+    "Perform Principal Component Analysis (PCA) on the AIMO observations and visualize the results together with the cluster assignments produced in the clustering task.\n",
+    "\n",
+    "## What this notebook does\n",
+    "1. Loads the AIMO dataset and the clustering results\n",
+    "2. Excludes score columns and identifier columns from PCA\n",
+    "3. Standardizes the remaining observation features\n",
+    "4. Performs PCA\n",
+    "5. Analyzes explained variance\n",
+    "6. Visualizes the observations in the first two principal components\n",
+    "7. Colors each observation by cluster assignment\n",
+    "8. Interprets cluster separation and the strongest feature contributions to PC1 and PC2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e5bee279",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "plt.style.use(\"ggplot\")\n",
+    "pd.set_option(\"display.max_rows\", 100)\n",
+    "pd.set_option(\"display.max_columns\", 100)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77d7d723",
+   "metadata": {},
+   "source": [
+    "## 1. Load the dataset and clustering results\n",
+    "\n",
+    "The PCA task depends on the clustering output from Issue #28.  \n",
+    "This notebook supports two common file layouts:\n",
+    "- files located in the same folder as the notebook\n",
+    "- the original project paths such as `Datasets_all/aimoscores.csv`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ae63d651",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset path: ../Datasets_all/aimoscores.csv\n",
+      "Cluster path: clustered_aimo_data.csv\n",
+      "Original dataset shape: (2094, 43)\n",
+      "Clustered dataset shape: (2094, 45)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from pathlib import Path\n",
+    "\n",
+    "candidate_dataset_paths = [\n",
+    "    Path(\"Datasets_all/aimoscores.csv\"),\n",
+    "    Path(\"aimoscores.csv\"),\n",
+    "    Path(\"../Datasets_all/aimoscores.csv\"),\n",
+    "    Path(\"/mnt/data/aimoscores.csv\"),\n",
+    "]\n",
+    "\n",
+    "candidate_cluster_paths = [\n",
+    "    Path(\"clustered_aimo_data.csv\"),\n",
+    "    Path(\"A7/clustered_aimo_data.csv\"),\n",
+    "    Path(\"/mnt/data/clustered_aimo_data.csv\"),\n",
+    "]\n",
+    "\n",
+    "dataset_path = next((p for p in candidate_dataset_paths if p.exists()), None)\n",
+    "cluster_path = next((p for p in candidate_cluster_paths if p.exists()), None)\n",
+    "\n",
+    "if dataset_path is None:\n",
+    "    raise FileNotFoundError(\"Could not find aimoscores.csv\")\n",
+    "if cluster_path is None:\n",
+    "    raise FileNotFoundError(\"Could not find clustered_aimo_data.csv\")\n",
+    "\n",
+    "df = pd.read_csv(dataset_path)\n",
+    "clustered_df = pd.read_csv(cluster_path)\n",
+    "\n",
+    "print(\"Dataset path:\", dataset_path)\n",
+    "print(\"Cluster path:\", cluster_path)\n",
+    "print(\"Original dataset shape:\", df.shape)\n",
+    "print(\"Clustered dataset shape:\", clustered_df.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "78b79180",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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>No_10_Angle_Deviation</th>\n",
+       "      <th>No_11_Angle_Deviation</th>\n",
+       "      <th>No_12_Angle_Deviation</th>\n",
+       "      <th>No_13_Angle_Deviation</th>\n",
+       "      <th>No_1_NASM_Deviation</th>\n",
+       "      <th>No_2_NASM_Deviation</th>\n",
+       "      <th>No_3_NASM_Deviation</th>\n",
+       "      <th>No_4_NASM_Deviation</th>\n",
+       "      <th>No_5_NASM_Deviation</th>\n",
+       "      <th>No_6_NASM_Deviation</th>\n",
+       "      <th>No_7_NASM_Deviation</th>\n",
+       "      <th>No_8_NASM_Deviation</th>\n",
+       "      <th>No_9_NASM_Deviation</th>\n",
+       "      <th>No_10_NASM_Deviation</th>\n",
+       "      <th>No_11_NASM_Deviation</th>\n",
+       "      <th>No_12_NASM_Deviation</th>\n",
+       "      <th>No_13_NASM_Deviation</th>\n",
+       "      <th>No_14_NASM_Deviation</th>\n",
+       "      <th>No_15_NASM_Deviation</th>\n",
+       "      <th>No_16_NASM_Deviation</th>\n",
+       "      <th>No_17_NASM_Deviation</th>\n",
+       "      <th>No_18_NASM_Deviation</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",
+       "      <th>ID</th>\n",
+       "      <th>Cluster</th>\n",
+       "      <th>Silhouette</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>0.210426</td>\n",
+       "      <td>0.835007</td>\n",
+       "      <td>0.451937</td>\n",
+       "      <td>0.178862</td>\n",
+       "      <td>0.302248</td>\n",
+       "      <td>0.275945</td>\n",
+       "      <td>0.538020</td>\n",
+       "      <td>0.210426</td>\n",
+       "      <td>0.178862</td>\n",
+       "      <td>0.778575</td>\n",
+       "      <td>0.214730</td>\n",
+       "      <td>0.428503</td>\n",
+       "      <td>0.300813</td>\n",
+       "      <td>0.432329</td>\n",
+       "      <td>0.730751</td>\n",
+       "      <td>0.606408</td>\n",
+       "      <td>0.829747</td>\n",
+       "      <td>0.838833</td>\n",
+       "      <td>0.459110</td>\n",
+       "      <td>0.652798</td>\n",
+       "      <td>0.711143</td>\n",
+       "      <td>0.587757</td>\n",
+       "      <td>0.833094</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",
+       "      <td>0</td>\n",
+       "      <td>0.165605</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>0.912482</td>\n",
+       "      <td>0.635581</td>\n",
+       "      <td>0.222382</td>\n",
+       "      <td>0.765184</td>\n",
+       "      <td>0.497370</td>\n",
+       "      <td>0.664275</td>\n",
+       "      <td>0.443807</td>\n",
+       "      <td>0.912482</td>\n",
+       "      <td>0.765184</td>\n",
+       "      <td>0.493544</td>\n",
+       "      <td>0.164993</td>\n",
+       "      <td>0.370636</td>\n",
+       "      <td>0.361071</td>\n",
+       "      <td>0.835007</td>\n",
+       "      <td>0.983262</td>\n",
+       "      <td>0.733620</td>\n",
+       "      <td>0.625060</td>\n",
+       "      <td>0.727881</td>\n",
+       "      <td>0.841703</td>\n",
+       "      <td>0.619321</td>\n",
+       "      <td>0.378288</td>\n",
+       "      <td>0.441416</td>\n",
+       "      <td>0.670971</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",
+       "      <td>1</td>\n",
+       "      <td>0.168119</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>0.244381</td>\n",
+       "      <td>0.635581</td>\n",
+       "      <td>0.957915</td>\n",
+       "      <td>0.243424</td>\n",
+       "      <td>0.341942</td>\n",
+       "      <td>0.276901</td>\n",
+       "      <td>0.603539</td>\n",
+       "      <td>0.244381</td>\n",
+       "      <td>0.243424</td>\n",
+       "      <td>0.709230</td>\n",
+       "      <td>0.355332</td>\n",
+       "      <td>0.339072</td>\n",
+       "      <td>0.245815</td>\n",
+       "      <td>0.286466</td>\n",
+       "      <td>0.306552</td>\n",
+       "      <td>0.321377</td>\n",
+       "      <td>0.544715</td>\n",
+       "      <td>0.687231</td>\n",
+       "      <td>0.698231</td>\n",
+       "      <td>0.947394</td>\n",
+       "      <td>0.378288</td>\n",
+       "      <td>0.733142</td>\n",
+       "      <td>0.670971</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",
+       "      <td>0</td>\n",
+       "      <td>0.186393</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>0.723577</td>\n",
+       "      <td>0.846485</td>\n",
+       "      <td>0.431851</td>\n",
+       "      <td>0.795313</td>\n",
+       "      <td>0.509326</td>\n",
+       "      <td>0.363462</td>\n",
+       "      <td>0.484935</td>\n",
+       "      <td>0.723577</td>\n",
+       "      <td>0.795313</td>\n",
+       "      <td>0.471066</td>\n",
+       "      <td>0.609756</td>\n",
+       "      <td>0.789096</td>\n",
+       "      <td>0.899091</td>\n",
+       "      <td>0.970827</td>\n",
+       "      <td>0.676231</td>\n",
+       "      <td>0.701100</td>\n",
+       "      <td>0.725012</td>\n",
+       "      <td>0.453372</td>\n",
+       "      <td>0.459110</td>\n",
+       "      <td>0.407939</td>\n",
+       "      <td>0.536585</td>\n",
+       "      <td>0.934003</td>\n",
+       "      <td>0.953611</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",
+       "      <td>0</td>\n",
+       "      <td>0.028242</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>0.210426</td>\n",
+       "      <td>0.635581</td>\n",
+       "      <td>0.095170</td>\n",
+       "      <td>0.376375</td>\n",
+       "      <td>0.375897</td>\n",
+       "      <td>0.388331</td>\n",
+       "      <td>0.860832</td>\n",
+       "      <td>0.210426</td>\n",
+       "      <td>0.376375</td>\n",
+       "      <td>0.471066</td>\n",
+       "      <td>0.880440</td>\n",
+       "      <td>0.479197</td>\n",
+       "      <td>0.534672</td>\n",
+       "      <td>0.650885</td>\n",
+       "      <td>0.457198</td>\n",
+       "      <td>0.417982</td>\n",
+       "      <td>0.544715</td>\n",
+       "      <td>0.121473</td>\n",
+       "      <td>0.459110</td>\n",
+       "      <td>0.483501</td>\n",
+       "      <td>0.439981</td>\n",
+       "      <td>0.441416</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>0.805356</td>\n",
+       "      <td>0.774271</td>\n",
+       "      <td>0.153515</td>\n",
+       "      <td>007396ec-3463-4a05-915c-02244ff8d3de.Kinect</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.246162</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\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_10_Angle_Deviation  No_11_Angle_Deviation  \\\n",
+       "0              0.457198               0.210426               0.835007   \n",
+       "1              0.729316               0.912482               0.635581   \n",
+       "2              0.658058               0.244381               0.635581   \n",
+       "3              0.237207               0.723577               0.846485   \n",
+       "4              0.387853               0.210426               0.635581   \n",
+       "\n",
+       "   No_12_Angle_Deviation  No_13_Angle_Deviation  No_1_NASM_Deviation  \\\n",
+       "0               0.451937               0.178862             0.302248   \n",
+       "1               0.222382               0.765184             0.497370   \n",
+       "2               0.957915               0.243424             0.341942   \n",
+       "3               0.431851               0.795313             0.509326   \n",
+       "4               0.095170               0.376375             0.375897   \n",
+       "\n",
+       "   No_2_NASM_Deviation  No_3_NASM_Deviation  No_4_NASM_Deviation  \\\n",
+       "0             0.275945             0.538020             0.210426   \n",
+       "1             0.664275             0.443807             0.912482   \n",
+       "2             0.276901             0.603539             0.244381   \n",
+       "3             0.363462             0.484935             0.723577   \n",
+       "4             0.388331             0.860832             0.210426   \n",
+       "\n",
+       "   No_5_NASM_Deviation  No_6_NASM_Deviation  No_7_NASM_Deviation  \\\n",
+       "0             0.178862             0.778575             0.214730   \n",
+       "1             0.765184             0.493544             0.164993   \n",
+       "2             0.243424             0.709230             0.355332   \n",
+       "3             0.795313             0.471066             0.609756   \n",
+       "4             0.376375             0.471066             0.880440   \n",
+       "\n",
+       "   No_8_NASM_Deviation  No_9_NASM_Deviation  No_10_NASM_Deviation  \\\n",
+       "0             0.428503             0.300813              0.432329   \n",
+       "1             0.370636             0.361071              0.835007   \n",
+       "2             0.339072             0.245815              0.286466   \n",
+       "3             0.789096             0.899091              0.970827   \n",
+       "4             0.479197             0.534672              0.650885   \n",
+       "\n",
+       "   No_11_NASM_Deviation  No_12_NASM_Deviation  No_13_NASM_Deviation  \\\n",
+       "0              0.730751              0.606408              0.829747   \n",
+       "1              0.983262              0.733620              0.625060   \n",
+       "2              0.306552              0.321377              0.544715   \n",
+       "3              0.676231              0.701100              0.725012   \n",
+       "4              0.457198              0.417982              0.544715   \n",
+       "\n",
+       "   No_14_NASM_Deviation  No_15_NASM_Deviation  No_16_NASM_Deviation  \\\n",
+       "0              0.838833              0.459110              0.652798   \n",
+       "1              0.727881              0.841703              0.619321   \n",
+       "2              0.687231              0.698231              0.947394   \n",
+       "3              0.453372              0.459110              0.407939   \n",
+       "4              0.121473              0.459110              0.483501   \n",
+       "\n",
+       "   No_17_NASM_Deviation  No_18_NASM_Deviation  No_19_NASM_Deviation  \\\n",
+       "0              0.711143              0.587757              0.833094   \n",
+       "1              0.378288              0.441416              0.670971   \n",
+       "2              0.378288              0.733142              0.670971   \n",
+       "3              0.536585              0.934003              0.953611   \n",
+       "4              0.439981              0.441416              0.670971   \n",
+       "\n",
+       "   No_20_NASM_Deviation  No_21_NASM_Deviation  No_22_NASM_Deviation  \\\n",
+       "0              0.656624              0.642276              0.552846   \n",
+       "1              0.721186              0.826399              0.805356   \n",
+       "2              0.656624              0.642276              0.690579   \n",
+       "3              0.656624              0.642276              0.552846   \n",
+       "4              0.656624              0.642276              0.552846   \n",
+       "\n",
+       "   No_23_NASM_Deviation  No_24_NASM_Deviation  No_25_NASM_Deviation  \\\n",
+       "0              0.648972              0.578192              0.560019   \n",
+       "1              0.848876              0.889048              0.816834   \n",
+       "2              0.648972              0.578192              0.555715   \n",
+       "3              0.648972              0.578192              0.744620   \n",
+       "4              0.648972              0.578192              0.308943   \n",
+       "\n",
+       "   No_1_Time_Deviation  No_2_Time_Deviation  EstimatedScore  \\\n",
+       "0             0.821616             0.818747        0.209947   \n",
+       "1             0.307987             0.248207        0.457198   \n",
+       "2             0.218556             0.235294        0.107126   \n",
+       "3             0.458154             0.432807        0.612626   \n",
+       "4             0.805356             0.774271        0.153515   \n",
+       "\n",
+       "                                            ID  Cluster  Silhouette  \n",
+       "0  0003cdcc-86ed-494a-a3b5-90d09e96e06b.Kinect        0    0.165605  \n",
+       "1  003115c4-bdb8-491c-b571-8fcebdecf8ed.Kinect        1    0.168119  \n",
+       "2  00316bfb-ed43-489f-a55b-11c7f01c852d.Kinect        0    0.186393  \n",
+       "3  00607608-6f2f-459b-a69d-e14067489459.Kinect        0    0.028242  \n",
+       "4  007396ec-3463-4a05-915c-02244ff8d3de.Kinect        0    0.246162  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "clustered_df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8d9afb8",
+   "metadata": {},
+   "source": [
+    "## 2. Select the PCA input features\n",
+    "\n",
+    "According to the issue description, PCA should be performed on the **AIMO observations**, excluding score columns.\n",
+    "\n",
+    "The following columns are excluded:\n",
+    "- `AimoScore`\n",
+    "- `EstimatedScore`\n",
+    "- `ID`\n",
+    "- `Cluster`\n",
+    "- `Silhouette`\n",
+    "\n",
+    "All remaining columns are treated as observation features.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "90f0d4ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of PCA input features: 40\n",
+      "['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']\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "exclude_cols = {\"AimoScore\", \"EstimatedScore\", \"ID\", \"Cluster\", \"Silhouette\"}\n",
+    "feature_cols = [col for col in clustered_df.columns if col not in exclude_cols]\n",
+    "\n",
+    "X = clustered_df[feature_cols].copy()\n",
+    "y = clustered_df[\"Cluster\"].copy()\n",
+    "\n",
+    "print(\"Number of PCA input features:\", len(feature_cols))\n",
+    "print(feature_cols)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c695640f",
+   "metadata": {},
+   "source": [
+    "## 3. Standardize the features\n",
+    "\n",
+    "Standardization is required because PCA is variance-based.  \n",
+    "Without scaling, features with larger numeric ranges would dominate the principal components.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9369e356",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.00513192,  1.1040404 , -0.98052474, -0.51488474, -0.78398237,\n",
+       "         1.59447707, -0.92644342, -0.74498145, -0.20414782, -1.20669255,\n",
+       "         1.22574805, -0.18752275, -1.26782539, -0.78398237, -0.92644342,\n",
+       "        -0.00513192, -1.20669255, -1.26782539,  0.97120456, -1.11072811,\n",
+       "        -0.58760146, -0.92257339, -0.45836943,  0.79473721,  0.2415011 ,\n",
+       "         1.25939909,  1.18502637, -0.82672181,  0.47129322,  0.65964464,\n",
+       "        -0.05602852,  1.13269881, -0.59075656, -0.6049716 , -0.71026318,\n",
+       "        -0.59902433, -0.68208525,  0.05173871,  1.13408479,  1.12396828],\n",
+       "       [-0.3989552 , -0.70163975,  1.20890883, -1.24662313, -0.06133811,\n",
+       "        -1.34913153,  0.5511039 , -0.74498145,  0.79950496,  1.5077349 ,\n",
+       "        -0.6095174 , -1.00224701,  0.93461609, -0.06133811,  0.5511039 ,\n",
+       "        -0.3989552 ,  1.5077349 ,  0.93461609, -0.68135104, -1.2953327 ,\n",
+       "        -0.84998578, -0.68103156,  1.23832504,  1.88711894,  0.80350283,\n",
+       "        -0.15987635,  0.79185891,  1.33406328,  0.33055967, -0.92857182,\n",
+       "        -0.85065882, -0.57413414,  0.05221015,  1.13431975,  1.0810216 ,\n",
+       "         1.33856354,  1.69162154,  1.16548459, -0.74740426, -0.96912528],\n",
+       "       [ 0.26874519, -0.46257787, -0.98052474,  0.27481314, -0.63697385,\n",
+       "        -0.77051811, -0.92280414, -0.08025399,  0.53668551, -1.07540894,\n",
+       "        -0.6095174 ,  1.6082653 , -1.02530533, -0.63697385, -0.92280414,\n",
+       "         0.26874519, -1.07540894, -1.02530533,  0.56915663, -0.58886512,\n",
+       "        -0.99310449, -1.14302823, -1.07296778, -1.04038135, -1.01772083,\n",
+       "        -0.71697512,  0.64781049,  0.52376887,  1.70974852, -0.92857182,\n",
+       "         0.73340811, -0.57413414, -0.59075656, -0.6049716 ,  0.26680124,\n",
+       "        -0.59902433, -0.68208525,  0.03307258, -1.07500058, -1.01649621],\n",
+       "       [-0.22703234,  0.42245972, -0.82680322,  1.73104498, -0.01705844,\n",
+       "         1.37836844, -0.59344938,  1.38027395, -1.0155367 ,  0.77735422,\n",
+       "         1.33137483, -0.25881113,  1.04779212, -0.01705844, -0.59344938,\n",
+       "        -0.22703234,  0.77735422,  1.04779212, -0.81167002,  0.35545838,\n",
+       "         1.04742143,  1.47559185,  1.81060678,  0.55888206,  0.65983321,\n",
+       "         0.53318105, -0.18089159, -0.82672181, -0.55807221, -0.17325623,\n",
+       "         1.82407715,  2.40149498, -0.59075656, -0.6049716 , -0.71026318,\n",
+       "        -0.59902433, -0.68208525,  0.85230837, -0.19732273, -0.29189635],\n",
+       "       [ 1.34426259,  0.5428384 ,  0.8509573 ,  0.13172567, -0.51121959,\n",
+       "        -0.09953569, -0.49882812, -0.74498145, -0.45991171, -1.20669255,\n",
+       "        -0.6095174 , -1.45374004, -0.52589364, -0.51121959, -0.49882812,\n",
+       "         1.34426259, -1.20669255, -0.52589364, -0.81167002,  1.36013338,\n",
+       "        -0.35774413,  0.01483893,  0.46252056, -0.38867634, -0.59093756,\n",
+       "        -0.71697512, -1.35700459, -0.82672181, -0.24041647, -0.63420412,\n",
+       "        -0.85065882, -0.57413414, -0.59075656, -0.6049716 , -0.71026318,\n",
+       "        -0.59902433, -0.68208525, -1.03711899,  1.07452183,  0.96080173]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "scaler = StandardScaler()\n",
+    "X_scaled = scaler.fit_transform(X)\n",
+    "\n",
+    "X_scaled[:5]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d6701098-cd0a-4db6-97aa-63ceacfdfffc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bc0fbae",
+   "metadata": {},
+   "source": [
+    "## 4. Perform PCA\n",
+    "We first fit PCA on all components so we can inspect the explained variance profile before focusing on the first two principal components for visualization.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3b83c08f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Principal Component</th>\n",
+       "      <th>Explained Variance Ratio</th>\n",
+       "      <th>Cumulative Explained Variance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PC1</td>\n",
+       "      <td>0.185730</td>\n",
+       "      <td>0.185730</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PC2</td>\n",
+       "      <td>0.125019</td>\n",
+       "      <td>0.310749</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PC3</td>\n",
+       "      <td>0.094939</td>\n",
+       "      <td>0.405688</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>PC4</td>\n",
+       "      <td>0.060190</td>\n",
+       "      <td>0.465878</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>PC5</td>\n",
+       "      <td>0.051808</td>\n",
+       "      <td>0.517686</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>PC6</td>\n",
+       "      <td>0.042601</td>\n",
+       "      <td>0.560287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>PC7</td>\n",
+       "      <td>0.039913</td>\n",
+       "      <td>0.600200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>PC8</td>\n",
+       "      <td>0.035092</td>\n",
+       "      <td>0.635292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>PC9</td>\n",
+       "      <td>0.031951</td>\n",
+       "      <td>0.667243</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>PC10</td>\n",
+       "      <td>0.028445</td>\n",
+       "      <td>0.695688</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Principal Component  Explained Variance Ratio  Cumulative Explained Variance\n",
+       "0                 PC1                  0.185730                       0.185730\n",
+       "1                 PC2                  0.125019                       0.310749\n",
+       "2                 PC3                  0.094939                       0.405688\n",
+       "3                 PC4                  0.060190                       0.465878\n",
+       "4                 PC5                  0.051808                       0.517686\n",
+       "5                 PC6                  0.042601                       0.560287\n",
+       "6                 PC7                  0.039913                       0.600200\n",
+       "7                 PC8                  0.035092                       0.635292\n",
+       "8                 PC9                  0.031951                       0.667243\n",
+       "9                PC10                  0.028445                       0.695688"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "pca = PCA()\n",
+    "X_pca = pca.fit_transform(X_scaled)\n",
+    "\n",
+    "explained_variance_ratio = pca.explained_variance_ratio_\n",
+    "cumulative_explained_variance = np.cumsum(explained_variance_ratio)\n",
+    "\n",
+    "explained_variance_df = pd.DataFrame({\n",
+    "    \"Principal Component\": [f\"PC{i+1}\" for i in range(len(explained_variance_ratio))],\n",
+    "    \"Explained Variance Ratio\": explained_variance_ratio,\n",
+    "    \"Cumulative Explained Variance\": cumulative_explained_variance\n",
+    "})\n",
+    "\n",
+    "explained_variance_df.head(10)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11735c9d",
+   "metadata": {},
+   "source": [
+    "## 5. Explained variance analysis\n",
+    "\n",
+    "### Summary\n",
+    "- **PC1 explains:** **18.57%** of the variance\n",
+    "- **PC2 explains:** **12.50%** of the variance\n",
+    "- **PC1 + PC2 explain:** **31.07%** of the variance\n",
+    "- **Components needed for 80% variance:** **15**\n",
+    "- **Components needed for 90% variance:** **22**\n",
+    "\n",
+    "This means the first two components provide a useful low-dimensional view, but they do **not** capture all of the structure in the data. That is normal for a dataset with many movement-related features.\n",
+    "- The relatively low variance explained by the first two components (31.07%) indicates that the dataset is high-dimensional and complex, requiring many components (15–22) to capture most of the variance. This suggests that movement behavior cannot be fully represented in only two dimensions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "749a7723",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.plot(\n",
+    "    range(1, len(explained_variance_ratio) + 1),\n",
+    "    explained_variance_ratio,\n",
+    "    marker=\"o\"\n",
+    ")\n",
+    "plt.xlabel(\"Principal Component\")\n",
+    "plt.ylabel(\"Explained Variance Ratio\")\n",
+    "plt.title(\"Explained Variance by Principal Component\")\n",
+    "plt.xticks(range(1, len(explained_variance_ratio) + 1, 2))\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4022c2d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.plot(\n",
+    "    range(1, len(cumulative_explained_variance) + 1),\n",
+    "    cumulative_explained_variance,\n",
+    "    marker=\"o\"\n",
+    ")\n",
+    "plt.axhline(0.80, linestyle=\"--\", label=\"80% variance\")\n",
+    "plt.axhline(0.90, linestyle=\"--\", label=\"90% variance\")\n",
+    "plt.xlabel(\"Principal Component\")\n",
+    "plt.ylabel(\"Cumulative Explained Variance\")\n",
+    "plt.title(\"Cumulative Explained Variance\")\n",
+    "plt.xticks(range(1, len(cumulative_explained_variance) + 1, 2))\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d28053a6",
+   "metadata": {},
+   "source": [
+    "## 6. Project the observations onto the first two principal components\n",
+    "\n",
+    "The following scatterplot is the main visualization required by the issue.  \n",
+    "Each point is an AIMO observation, and the color shows its cluster assignment from the clustering task.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6ab13a10",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Cluster\n",
+       "0    1393\n",
+       "1     701\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "pca_df = pd.DataFrame({\n",
+    "    \"PC1\": X_pca[:, 0],\n",
+    "    \"PC2\": X_pca[:, 1],\n",
+    "    \"Cluster\": y.astype(str)\n",
+    "})\n",
+    "\n",
+    "cluster_counts = pca_df[\"Cluster\"].value_counts().sort_index()\n",
+    "cluster_counts\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "84fddf6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.figure(figsize=(10, 7))\n",
+    "\n",
+    "for cluster_label in sorted(pca_df[\"Cluster\"].unique()):\n",
+    "    mask = pca_df[\"Cluster\"] == cluster_label\n",
+    "    plt.scatter(\n",
+    "        pca_df.loc[mask, \"PC1\"],\n",
+    "        pca_df.loc[mask, \"PC2\"],\n",
+    "        alpha=0.6,\n",
+    "        label=f\"Cluster {cluster_label}\"\n",
+    "    )\n",
+    "\n",
+    "plt.xlabel(f\"PC1 ({explained_variance_ratio[0]:.2%} variance)\")\n",
+    "plt.ylabel(f\"PC2 ({explained_variance_ratio[1]:.2%} variance)\")\n",
+    "plt.title(\"PCA of AIMO Observations Colored by Cluster Assignment\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aded154c",
+   "metadata": {},
+   "source": [
+    "## 7. Cluster separation in PC space\n",
+    "\n",
+    "To make the separation easier to interpret, we can compare the cluster centers in the PCA projection.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ff038e59",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"5\" halign=\"left\">PC1</th>\n",
+       "      <th colspan=\"5\" halign=\"left\">PC2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>max</th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>max</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cluster</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.570246</td>\n",
+       "      <td>1.339862</td>\n",
+       "      <td>-5.030407</td>\n",
+       "      <td>1.091974</td>\n",
+       "      <td>1393</td>\n",
+       "      <td>-0.165564</td>\n",
+       "      <td>2.133781</td>\n",
+       "      <td>-4.954586</td>\n",
+       "      <td>6.769967</td>\n",
+       "      <td>1393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3.120332</td>\n",
+       "      <td>1.999224</td>\n",
+       "      <td>0.041481</td>\n",
+       "      <td>6.940333</td>\n",
+       "      <td>701</td>\n",
+       "      <td>0.329002</td>\n",
+       "      <td>2.396339</td>\n",
+       "      <td>-3.607924</td>\n",
+       "      <td>8.773168</td>\n",
+       "      <td>701</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              PC1                                           PC2            \\\n",
+       "             mean       std       min       max count      mean       std   \n",
+       "Cluster                                                                     \n",
+       "0       -1.570246  1.339862 -5.030407  1.091974  1393 -0.165564  2.133781   \n",
+       "1        3.120332  1.999224  0.041481  6.940333   701  0.329002  2.396339   \n",
+       "\n",
+       "                                   \n",
+       "              min       max count  \n",
+       "Cluster                            \n",
+       "0       -4.954586  6.769967  1393  \n",
+       "1       -3.607924  8.773168   701  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "pca_cluster_summary = pca_df.groupby(\"Cluster\")[[\"PC1\", \"PC2\"]].agg([\"mean\", \"std\", \"min\", \"max\", \"count\"])\n",
+    "pca_cluster_summary\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11eea49c",
+   "metadata": {},
+   "source": [
+    "### Interpretation of the PCA scatterplot\n",
+    "\n",
+    "- The clusters show their strongest separation along **PC1**.\n",
+    "- The average PC1 values are approximately:\n",
+    "  - **Cluster 0:** **-1.570**\n",
+    "  - **Cluster 1:** **3.120**\n",
+    "- The distributions still overlap, especially along **PC2**.\n",
+    "\n",
+    "### What this indicates\n",
+    "### Interpretation of PCA Visualization\n",
+    "\n",
+    "- The PCA projection reveals that the clusters are primarily separated along PC1, with cluster 0 centered around negative PC1 values and cluster 1 around positive values. However, there is still noticeable overlap between the clusters, particularly along PC2.\n",
+    "\n",
+    "- This indicates that while the clustering algorithm successfully captured an underlying structure in the data, the separation between groups is not sharply defined. Instead, the observations appear to lie on a continuous spectrum of movement patterns rather than forming completely distinct categories.\n",
+    "\n",
+    "- This observation is consistent with the relatively low silhouette score obtained in the clustering analysis, confirming that cluster separation is moderate rather than strong.\n",
+    "\n",
+    "So the PCA visualization supports the clustering result, while also showing that separation is **moderate rather than strong**.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f77fe1c6",
+   "metadata": {},
+   "source": [
+    "## 8. Feature contributions (loadings) for PC1 and PC2\n",
+    "\n",
+    "PCA loadings show which original features contribute most to each principal component.  \n",
+    "Large positive or negative absolute values indicate stronger influence.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b2ee1859",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top 10 contributors to PC1:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Loading</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>No_4_NASM_Deviation</th>\n",
+       "      <td>0.300729</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>No_10_Angle_Deviation</th>\n",
+       "      <td>0.300729</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>No_5_NASM_Deviation</th>\n",
+       "      <td>0.299762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>No_13_Angle_Deviation</th>\n",
+       "      <td>0.299762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>No_12_NASM_Deviation</th>\n",
+       "      <td>0.284173</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>No_11_NASM_Deviation</th>\n",
+       "      <td>0.281051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>No_5_Angle_Deviation</th>\n",
+       "      <td>0.217492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>No_1_NASM_Deviation</th>\n",
+       "      <td>0.217492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>No_7_Angle_Deviation</th>\n",
+       "      <td>0.213496</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>No_2_NASM_Deviation</th>\n",
+       "      <td>0.213496</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        Loading\n",
+       "No_4_NASM_Deviation    0.300729\n",
+       "No_10_Angle_Deviation  0.300729\n",
+       "No_5_NASM_Deviation    0.299762\n",
+       "No_13_Angle_Deviation  0.299762\n",
+       "No_12_NASM_Deviation   0.284173\n",
+       "No_11_NASM_Deviation   0.281051\n",
+       "No_5_Angle_Deviation   0.217492\n",
+       "No_1_NASM_Deviation    0.217492\n",
+       "No_7_Angle_Deviation   0.213496\n",
+       "No_2_NASM_Deviation    0.213496"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Top 10 contributors to PC2:\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>Loading</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>No_8_Angle_Deviation</th>\n",
+       "      <td>0.272415</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>No_8_NASM_Deviation</th>\n",
+       "      <td>0.270850</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>No_10_NASM_Deviation</th>\n",
+       "      <td>0.261473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>No_1_Angle_Deviation</th>\n",
+       "      <td>0.250991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>No_3_NASM_Deviation</th>\n",
+       "      <td>0.250991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>No_9_NASM_Deviation</th>\n",
+       "      <td>0.246433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>No_11_Angle_Deviation</th>\n",
+       "      <td>0.245050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>No_17_NASM_Deviation</th>\n",
+       "      <td>0.242802</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>No_4_Angle_Deviation</th>\n",
+       "      <td>0.233129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>No_2_Angle_Deviation</th>\n",
+       "      <td>0.229244</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        Loading\n",
+       "No_8_Angle_Deviation   0.272415\n",
+       "No_8_NASM_Deviation    0.270850\n",
+       "No_10_NASM_Deviation   0.261473\n",
+       "No_1_Angle_Deviation   0.250991\n",
+       "No_3_NASM_Deviation    0.250991\n",
+       "No_9_NASM_Deviation    0.246433\n",
+       "No_11_Angle_Deviation  0.245050\n",
+       "No_17_NASM_Deviation   0.242802\n",
+       "No_4_Angle_Deviation   0.233129\n",
+       "No_2_Angle_Deviation   0.229244"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "loadings = pd.DataFrame(\n",
+    "    pca.components_.T,\n",
+    "    index=feature_cols,\n",
+    "    columns=[f\"PC{i+1}\" for i in range(len(feature_cols))]\n",
+    ")\n",
+    "\n",
+    "top_pc1 = loadings[\"PC1\"].sort_values(key=lambda s: s.abs(), ascending=False).head(10)\n",
+    "top_pc2 = loadings[\"PC2\"].sort_values(key=lambda s: s.abs(), ascending=False).head(10)\n",
+    "\n",
+    "print(\"Top 10 contributors to PC1:\")\n",
+    "display(top_pc1.to_frame(\"Loading\"))\n",
+    "\n",
+    "print(\"\\nTop 10 contributors to PC2:\")\n",
+    "display(top_pc2.to_frame(\"Loading\"))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0bec9eb8",
+   "metadata": {},
+   "source": [
+    "### Strongest contributors to PC1\n",
+    "- **No_10_Angle_Deviation**: 0.301\n",
+    "- **No_4_NASM_Deviation**: 0.301\n",
+    "- **No_13_Angle_Deviation**: 0.300\n",
+    "- **No_5_NASM_Deviation**: 0.300\n",
+    "- **No_12_NASM_Deviation**: 0.284\n",
+    "- **No_11_NASM_Deviation**: 0.281\n",
+    "- **No_5_Angle_Deviation**: 0.217\n",
+    "- **No_1_NASM_Deviation**: 0.217\n",
+    "\n",
+    "### Strongest contributors to PC2\n",
+    "- **No_8_Angle_Deviation**: 0.272\n",
+    "- **No_8_NASM_Deviation**: 0.271\n",
+    "- **No_10_NASM_Deviation**: 0.261\n",
+    "- **No_1_Angle_Deviation**: 0.251\n",
+    "- **No_3_NASM_Deviation**: 0.251\n",
+    "- **No_9_NASM_Deviation**: 0.246\n",
+    "- **No_11_Angle_Deviation**: 0.245\n",
+    "- **No_17_NASM_Deviation**: 0.243\n",
+    "\n",
+    "### Interpretation of the loadings\n",
+    "- **PC1** is driven mainly by a subset of angle deviation and NASM deviation features, especially around the features listed above.\n",
+    "- **PC2** captures a different pattern of variation, again combining angle and NASM deviations but with a different weighting structure.\n",
+    "\n",
+    "This means the first two principal components represent **combined movement-deviation patterns**, not single raw features in isolation.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5efd7614",
+   "metadata": {},
+   "source": [
+    "### Final Conclusion\n",
+    "\n",
+    "The PCA analysis supports the clustering results from Issue #28:\n",
+    "\n",
+    "- There is clear underlying structure in the AIMO observations.\n",
+    "- The clusters are visible in PCA space, especially along PC1.\n",
+    "- However, the overlap between clusters indicates that the separation is not strong.\n",
+    "\n",
+    "Overall, the data appears to represent a continuous range of movement patterns rather than sharply defined groups. This suggests that while clustering is useful for identifying general trends, the underlying behavior is better understood as a spectrum rather than discrete categories."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47b0f77b",
+   "metadata": {},
+   "source": [
+    "## 10. Optional: Save PCA output for reuse\n",
+    "This cell saves the PCA coordinates together with cluster labels, which can be useful for the report or later analysis.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "cfbfefc5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved: A7_PCA_with_clusters.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "output_df = clustered_df.copy()\n",
+    "output_df[\"PC1\"] = X_pca[:, 0]\n",
+    "output_df[\"PC2\"] = X_pca[:, 1]\n",
+    "\n",
+    "output_df.to_csv(\"A7_PCA_with_clusters.csv\", index=False)\n",
+    "print(\"Saved: A7_PCA_with_clusters.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "611d6922-68c6-4d66-b083-153fa1b0d393",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}