diff --git "a/AI for Sleep Detection EDA.ipynb" "b/AI for Sleep Detection EDA.ipynb"
new file mode 100644--- /dev/null
+++ "b/AI for Sleep Detection EDA.ipynb"
@@ -0,0 +1,2391 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3ffc354a-ff10-4d6d-88d9-cf55f4cbcf13",
+   "metadata": {},
+   "source": [
+    "# 1. Business Statement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f5b6476-e52d-4493-b4e2-471b3d743e1f",
+   "metadata": {},
+   "source": [
+    "## 1a. Business Problem"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8649b44e-1c17-4166-9feb-b730a8511256",
+   "metadata": {},
+   "source": [
+    " #####  Sleep is essential for maintaining good health, influencing mental performance, physical capabilities, and overall growth.\n",
+    " #####  This project focuses on solving the problem of automatically identifying sleep states using data from wrist-worn accelerometers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84f21aaf-1000-4c40-ac38-e1ffa35c4c81",
+   "metadata": {},
+   "source": [
+    "## 1b. Business Objective"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "184b357b-3477-434b-8803-7aeecc960c57",
+   "metadata": {},
+   "source": [
+    "##### The objective is to build a machine learning model capable of interpreting data from wrist-worn accelerometers to precisely identify when an individual falls asleep (sleep onset) and when they wake up (wake-up time).\n",
+    "##### This approach would support researchers in conducting sleep studies by enabling the collection of large-scale, real-time data, ultimately making sleep research more efficient and informative."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13b59876-e02e-4823-b769-547a76089c03",
+   "metadata": {},
+   "source": [
+    "## 1c. Business Constraints"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20aaab00-1372-4fb2-b49f-a19f48f84301",
+   "metadata": {},
+   "source": [
+    "#### 1. Data Reliability: \n",
+    "##### The dataset relies on accelerometer readings to infer sleep based on movement levels. However, the data may be affected by noise, gaps, or inconsistencies, which can make it difficult to accurately identify sleep-related events.\n",
+    "\n",
+    "#### 2. Definition of Sleep Period:\n",
+    "##### Sleep is defined as the longest stretch of low activity lasting at least 30 minutes. Minor movements during this time are not considered interruptions and are treated as part of the continuous sleep phase.\n",
+    "\n",
+    "#### 3. Real-Time Application:\n",
+    "##### The model must provide timely predictions of sleep states, which is crucial for applications requiring real-time or near-instant feedback.\n",
+    "\n",
+    "#### 4. Scalability: \n",
+    "##### Since the data is collected from a diverse set of individuals, the model should be robust enough to handle variations in movement patterns across different people.\n",
+    "\n",
+    "#### 5. False Detections: \n",
+    "##### The model should aim to reduce both false positives (wrongly detecting sleep events) and false negatives (failing to detect actual sleep), as these errors can compromise the accuracy of the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5caf41ee-52f6-4278-9c36-f15770410f84",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import polars as pl\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b613efdd-f4a6-4ec4-a4b8-8f9a8641b65a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (127_946_340, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>series_id</th><th>step</th><th>timestamp</th><th>anglez</th><th>enmo</th></tr><tr><td>str</td><td>u32</td><td>str</td><td>f32</td><td>f32</td></tr></thead><tbody><tr><td>&quot;038441c925bb&quot;</td><td>0</td><td>&quot;2018-08-14T15:30:00-0400&quot;</td><td>2.6367</td><td>0.0217</td></tr><tr><td>&quot;038441c925bb&quot;</td><td>1</td><td>&quot;2018-08-14T15:30:05-0400&quot;</td><td>2.6368</td><td>0.0215</td></tr><tr><td>&quot;038441c925bb&quot;</td><td>2</td><td>&quot;2018-08-14T15:30:10-0400&quot;</td><td>2.637</td><td>0.0216</td></tr><tr><td>&quot;038441c925bb&quot;</td><td>3</td><td>&quot;2018-08-14T15:30:15-0400&quot;</td><td>2.6368</td><td>0.0213</td></tr><tr><td>&quot;038441c925bb&quot;</td><td>4</td><td>&quot;2018-08-14T15:30:20-0400&quot;</td><td>2.6368</td><td>0.0215</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;fe90110788d2&quot;</td><td>592375</td><td>&quot;2017-09-08T00:14:35-0400&quot;</td><td>-27.2775</td><td>0.0204</td></tr><tr><td>&quot;fe90110788d2&quot;</td><td>592376</td><td>&quot;2017-09-08T00:14:40-0400&quot;</td><td>-27.032499</td><td>0.0233</td></tr><tr><td>&quot;fe90110788d2&quot;</td><td>592377</td><td>&quot;2017-09-08T00:14:45-0400&quot;</td><td>-26.8412</td><td>0.0202</td></tr><tr><td>&quot;fe90110788d2&quot;</td><td>592378</td><td>&quot;2017-09-08T00:14:50-0400&quot;</td><td>-26.7239</td><td>0.0199</td></tr><tr><td>&quot;fe90110788d2&quot;</td><td>592379</td><td>&quot;2017-09-08T00:14:55-0400&quot;</td><td>-31.521601</td><td>0.0205</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (127_946_340, 5)\n",
+       "┌──────────────┬────────┬──────────────────────────┬────────────┬────────┐\n",
+       "│ series_id    ┆ step   ┆ timestamp                ┆ anglez     ┆ enmo   │\n",
+       "│ ---          ┆ ---    ┆ ---                      ┆ ---        ┆ ---    │\n",
+       "│ str          ┆ u32    ┆ str                      ┆ f32        ┆ f32    │\n",
+       "╞══════════════╪════════╪══════════════════════════╪════════════╪════════╡\n",
+       "│ 038441c925bb ┆ 0      ┆ 2018-08-14T15:30:00-0400 ┆ 2.6367     ┆ 0.0217 │\n",
+       "│ 038441c925bb ┆ 1      ┆ 2018-08-14T15:30:05-0400 ┆ 2.6368     ┆ 0.0215 │\n",
+       "│ 038441c925bb ┆ 2      ┆ 2018-08-14T15:30:10-0400 ┆ 2.637      ┆ 0.0216 │\n",
+       "│ 038441c925bb ┆ 3      ┆ 2018-08-14T15:30:15-0400 ┆ 2.6368     ┆ 0.0213 │\n",
+       "│ 038441c925bb ┆ 4      ┆ 2018-08-14T15:30:20-0400 ┆ 2.6368     ┆ 0.0215 │\n",
+       "│ …            ┆ …      ┆ …                        ┆ …          ┆ …      │\n",
+       "│ fe90110788d2 ┆ 592375 ┆ 2017-09-08T00:14:35-0400 ┆ -27.2775   ┆ 0.0204 │\n",
+       "│ fe90110788d2 ┆ 592376 ┆ 2017-09-08T00:14:40-0400 ┆ -27.032499 ┆ 0.0233 │\n",
+       "│ fe90110788d2 ┆ 592377 ┆ 2017-09-08T00:14:45-0400 ┆ -26.8412   ┆ 0.0202 │\n",
+       "│ fe90110788d2 ┆ 592378 ┆ 2017-09-08T00:14:50-0400 ┆ -26.7239   ┆ 0.0199 │\n",
+       "│ fe90110788d2 ┆ 592379 ┆ 2017-09-08T00:14:55-0400 ┆ -31.521601 ┆ 0.0205 │\n",
+       "└──────────────┴────────┴──────────────────────────┴────────────┴────────┘"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pl.read_parquet(r\"C:\\Users\\ragha\\Downloads\\child-mind-institute-detect-sleep-states\\train_series.parquet\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "99cf7010-0216-4c2d-b03f-dda77f27c8fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (592_380, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>series_id</th><th>step</th><th>timestamp</th><th>anglez</th><th>enmo</th></tr><tr><td>str</td><td>u32</td><td>str</td><td>f32</td><td>f32</td></tr></thead><tbody><tr><td>&quot;fe90110788d2&quot;</td><td>0</td><td>&quot;2017-08-04T17:30:00-0400&quot;</td><td>-27.707001</td><td>0.0298</td></tr><tr><td>&quot;fe90110788d2&quot;</td><td>1</td><td>&quot;2017-08-04T17:30:05-0400&quot;</td><td>-33.8675</td><td>0.0488</td></tr><tr><td>&quot;fe90110788d2&quot;</td><td>2</td><td>&quot;2017-08-04T17:30:10-0400&quot;</td><td>-15.475</td><td>0.1077</td></tr><tr><td>&quot;fe90110788d2&quot;</td><td>3</td><td>&quot;2017-08-04T17:30:15-0400&quot;</td><td>-73.656197</td><td>0.053</td></tr><tr><td>&quot;fe90110788d2&quot;</td><td>4</td><td>&quot;2017-08-04T17:30:20-0400&quot;</td><td>-53.152901</td><td>0.0601</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;fe90110788d2&quot;</td><td>592375</td><td>&quot;2017-09-08T00:14:35-0400&quot;</td><td>-27.2775</td><td>0.0204</td></tr><tr><td>&quot;fe90110788d2&quot;</td><td>592376</td><td>&quot;2017-09-08T00:14:40-0400&quot;</td><td>-27.032499</td><td>0.0233</td></tr><tr><td>&quot;fe90110788d2&quot;</td><td>592377</td><td>&quot;2017-09-08T00:14:45-0400&quot;</td><td>-26.8412</td><td>0.0202</td></tr><tr><td>&quot;fe90110788d2&quot;</td><td>592378</td><td>&quot;2017-09-08T00:14:50-0400&quot;</td><td>-26.7239</td><td>0.0199</td></tr><tr><td>&quot;fe90110788d2&quot;</td><td>592379</td><td>&quot;2017-09-08T00:14:55-0400&quot;</td><td>-31.521601</td><td>0.0205</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (592_380, 5)\n",
+       "┌──────────────┬────────┬──────────────────────────┬────────────┬────────┐\n",
+       "│ series_id    ┆ step   ┆ timestamp                ┆ anglez     ┆ enmo   │\n",
+       "│ ---          ┆ ---    ┆ ---                      ┆ ---        ┆ ---    │\n",
+       "│ str          ┆ u32    ┆ str                      ┆ f32        ┆ f32    │\n",
+       "╞══════════════╪════════╪══════════════════════════╪════════════╪════════╡\n",
+       "│ fe90110788d2 ┆ 0      ┆ 2017-08-04T17:30:00-0400 ┆ -27.707001 ┆ 0.0298 │\n",
+       "│ fe90110788d2 ┆ 1      ┆ 2017-08-04T17:30:05-0400 ┆ -33.8675   ┆ 0.0488 │\n",
+       "│ fe90110788d2 ┆ 2      ┆ 2017-08-04T17:30:10-0400 ┆ -15.475    ┆ 0.1077 │\n",
+       "│ fe90110788d2 ┆ 3      ┆ 2017-08-04T17:30:15-0400 ┆ -73.656197 ┆ 0.053  │\n",
+       "│ fe90110788d2 ┆ 4      ┆ 2017-08-04T17:30:20-0400 ┆ -53.152901 ┆ 0.0601 │\n",
+       "│ …            ┆ …      ┆ …                        ┆ …          ┆ …      │\n",
+       "│ fe90110788d2 ┆ 592375 ┆ 2017-09-08T00:14:35-0400 ┆ -27.2775   ┆ 0.0204 │\n",
+       "│ fe90110788d2 ┆ 592376 ┆ 2017-09-08T00:14:40-0400 ┆ -27.032499 ┆ 0.0233 │\n",
+       "│ fe90110788d2 ┆ 592377 ┆ 2017-09-08T00:14:45-0400 ┆ -26.8412   ┆ 0.0202 │\n",
+       "│ fe90110788d2 ┆ 592378 ┆ 2017-09-08T00:14:50-0400 ┆ -26.7239   ┆ 0.0199 │\n",
+       "│ fe90110788d2 ┆ 592379 ┆ 2017-09-08T00:14:55-0400 ┆ -31.521601 ┆ 0.0205 │\n",
+       "└──────────────┴────────┴──────────────────────────┴────────────┴────────┘"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.filter(pl.col(\"series_id\") == \"fe90110788d2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "df7fbfba-9fd4-4d22-9e2d-7db9473f1972",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (14_508, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>series_id</th><th>night</th><th>event</th><th>step</th><th>timestamp</th></tr><tr><td>str</td><td>i64</td><td>str</td><td>i64</td><td>str</td></tr></thead><tbody><tr><td>&quot;038441c925bb&quot;</td><td>1</td><td>&quot;onset&quot;</td><td>4992</td><td>&quot;2018-08-14T22:26:00-0400&quot;</td></tr><tr><td>&quot;038441c925bb&quot;</td><td>1</td><td>&quot;wakeup&quot;</td><td>10932</td><td>&quot;2018-08-15T06:41:00-0400&quot;</td></tr><tr><td>&quot;038441c925bb&quot;</td><td>2</td><td>&quot;onset&quot;</td><td>20244</td><td>&quot;2018-08-15T19:37:00-0400&quot;</td></tr><tr><td>&quot;038441c925bb&quot;</td><td>2</td><td>&quot;wakeup&quot;</td><td>27492</td><td>&quot;2018-08-16T05:41:00-0400&quot;</td></tr><tr><td>&quot;038441c925bb&quot;</td><td>3</td><td>&quot;onset&quot;</td><td>39996</td><td>&quot;2018-08-16T23:03:00-0400&quot;</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;fe90110788d2&quot;</td><td>33</td><td>&quot;wakeup&quot;</td><td>560604</td><td>&quot;2017-09-06T04:07:00-0400&quot;</td></tr><tr><td>&quot;fe90110788d2&quot;</td><td>34</td><td>&quot;onset&quot;</td><td>574620</td><td>&quot;2017-09-06T23:35:00-0400&quot;</td></tr><tr><td>&quot;fe90110788d2&quot;</td><td>34</td><td>&quot;wakeup&quot;</td><td>581604</td><td>&quot;2017-09-07T09:17:00-0400&quot;</td></tr><tr><td>&quot;fe90110788d2&quot;</td><td>35</td><td>&quot;onset&quot;</td><td>null</td><td>null</td></tr><tr><td>&quot;fe90110788d2&quot;</td><td>35</td><td>&quot;wakeup&quot;</td><td>null</td><td>null</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (14_508, 5)\n",
+       "┌──────────────┬───────┬────────┬────────┬──────────────────────────┐\n",
+       "│ series_id    ┆ night ┆ event  ┆ step   ┆ timestamp                │\n",
+       "│ ---          ┆ ---   ┆ ---    ┆ ---    ┆ ---                      │\n",
+       "│ str          ┆ i64   ┆ str    ┆ i64    ┆ str                      │\n",
+       "╞══════════════╪═══════╪════════╪════════╪══════════════���═══════════╡\n",
+       "│ 038441c925bb ┆ 1     ┆ onset  ┆ 4992   ┆ 2018-08-14T22:26:00-0400 │\n",
+       "│ 038441c925bb ┆ 1     ┆ wakeup ┆ 10932  ┆ 2018-08-15T06:41:00-0400 │\n",
+       "│ 038441c925bb ┆ 2     ┆ onset  ┆ 20244  ┆ 2018-08-15T19:37:00-0400 │\n",
+       "│ 038441c925bb ┆ 2     ┆ wakeup ┆ 27492  ┆ 2018-08-16T05:41:00-0400 │\n",
+       "│ 038441c925bb ┆ 3     ┆ onset  ┆ 39996  ┆ 2018-08-16T23:03:00-0400 │\n",
+       "│ …            ┆ …     ┆ …      ┆ …      ┆ …                        │\n",
+       "│ fe90110788d2 ┆ 33    ┆ wakeup ┆ 560604 ┆ 2017-09-06T04:07:00-0400 │\n",
+       "│ fe90110788d2 ┆ 34    ┆ onset  ┆ 574620 ┆ 2017-09-06T23:35:00-0400 │\n",
+       "│ fe90110788d2 ┆ 34    ┆ wakeup ┆ 581604 ┆ 2017-09-07T09:17:00-0400 │\n",
+       "│ fe90110788d2 ┆ 35    ┆ onset  ┆ null   ┆ null                     │\n",
+       "│ fe90110788d2 ┆ 35    ┆ wakeup ┆ null   ┆ null                     │\n",
+       "└──────────────┴───────┴────────┴────────┴──────────────────────────┘"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "events = pl.read_csv(r\"C:\\Users\\ragha\\Downloads\\child-mind-institute-detect-sleep-states\\train_events.csv\")\n",
+    "events"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cde36964-eccd-44df-9e1f-afceab1223ba",
+   "metadata": {},
+   "source": [
+    "# 2. Data Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12810611-371d-419c-a86c-bc474ef9aafc",
+   "metadata": {},
+   "source": [
+    "### Data Cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "331fc277-2e6e-4a08-a0bc-7f043efe48a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Schema({'series_id': String, 'step': UInt32, 'timestamp': String, 'anglez': Float32, 'enmo': Float32})\n",
+      "Schema({'series_id': String, 'night': Int64, 'event': String, 'step': Int64, 'timestamp': String})\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.schema)\n",
+    "print(events.schema)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f64a24d8-798a-479d-8ec4-9c47af850607",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "shape: (5, 1)\n",
+      "┌──────────────────────────┐\n",
+      "│ timestamp                │\n",
+      "│ ---                      │\n",
+      "│ str                      │\n",
+      "╞══════════════════════════╡\n",
+      "│ 2018-08-14T15:30:00-0400 │\n",
+      "│ 2018-08-14T15:30:05-0400 │\n",
+      "│ 2018-08-14T15:30:10-0400 │\n",
+      "│ 2018-08-14T15:30:15-0400 │\n",
+      "│ 2018-08-14T15:30:20-0400 │\n",
+      "└──────────────────────────┘\n",
+      "shape: (5, 1)\n",
+      "┌──────────────────────────┐\n",
+      "│ timestamp                │\n",
+      "│ ---                      │\n",
+      "│ str                      │\n",
+      "╞══════════════════════════╡\n",
+      "│ 2018-08-14T22:26:00-0400 │\n",
+      "│ 2018-08-15T06:41:00-0400 │\n",
+      "│ 2018-08-15T19:37:00-0400 │\n",
+      "│ 2018-08-16T05:41:00-0400 │\n",
+      "│ 2018-08-16T23:03:00-0400 │\n",
+      "└──────────────────────────┘\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.select('timestamp').head(5))\n",
+    "print(events.select('timestamp').head(5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "24e30fb0-5c46-4a44-bbc3-08e256bdde75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert 'timestamp' to datetime (with timezone)\n",
+    "df = df.with_columns(\n",
+    "    pl.col(\"timestamp\").cast(pl.Utf8).str.to_datetime().alias(\"timestamp\")\n",
+    ")\n",
+    "\n",
+    "events = events.with_columns(\n",
+    "    pl.col(\"timestamp\").cast(pl.Utf8).str.to_datetime().alias(\"timestamp\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "458fc1d2-9bdc-4c04-93d5-efb2e3b91c21",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "shape: (1, 5)\n",
+      "┌───────────┬──────┬───────────┬────────┬──────┐\n",
+      "│ series_id ┆ step ┆ timestamp ┆ anglez ┆ enmo │\n",
+      "│ ---       ┆ ---  ┆ ---       ┆ ---    ┆ ---  │\n",
+      "│ u32       ┆ u32  ┆ u32       ┆ u32    ┆ u32  │\n",
+      "╞═══════════╪══════╪═══════════╪════════╪══════╡\n",
+      "│ 0         ┆ 0    ┆ 0         ┆ 0      ┆ 0    │\n",
+      "└───────────┴──────┴───────────┴────────┴──────┘\n",
+      "shape: (1, 5)\n",
+      "┌───────────┬───────┬───────┬──────┬───────────┐\n",
+      "│ series_id ┆ night ┆ event ┆ step ┆ timestamp │\n",
+      "│ ---       ┆ ---   ┆ ---   ┆ ---  ┆ ---       │\n",
+      "│ u32       ┆ u32   ┆ u32   ┆ u32  ┆ u32       │\n",
+      "╞═══════════╪═══════╪═══════╪══════╪═══════════╡\n",
+      "│ 0         ┆ 0     ┆ 0     ┆ 4923 ┆ 4923      │\n",
+      "└───────────┴───────┴───────┴──────┴───────────┘\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.null_count())\n",
+    "print(events.null_count())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "b4941e79-9e8d-4196-a4ac-785ee5b41a74",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[String, UInt32, Datetime(time_unit='us', time_zone='UTC'), Float32, Float32]\n",
+      "[String, Int64, String, Int64, Datetime(time_unit='us', time_zone='UTC')]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check data types to ensure correct conversion\n",
+    "print(df.dtypes)\n",
+    "print(events.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "40966b6d-33c2-43c4-a0e3-8df94ea76a07",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_sample = df.sample(n=5000)  # Sample 5000 rows\n",
+    "df_sample_pandas = df_sample.to_pandas()\n",
+    "\n",
+    "# Step 2: Create boxplots with dark colors\n",
+    "plt.figure(figsize=(12, 5))\n",
+    "\n",
+    "# Boxplot for enmo\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.boxplot(y=df_sample_pandas['enmo'], color='#2C3E50')  # Dark slate blue\n",
+    "plt.title('Boxplot of enmo', color='#2C3E50')\n",
+    "plt.ylabel('enmo', color='#2C3E50')\n",
+    "\n",
+    "# Boxplot for anglez\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.boxplot(y=df_sample_pandas['anglez'], color='#1A5276')  # Dark blue\n",
+    "plt.title('Boxplot of anglez', color='#1A5276')\n",
+    "plt.ylabel('anglez', color='#1A5276')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "a52b7028-8d2b-408f-99f3-796198d9eaaa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outliers in enmo: (10638653, 5)\n",
+      "Outliers in anglez: (1785262, 5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "def detect_outliers_iqr(df, col):\n",
+    "    q1 = df.select(pl.col(col).quantile(0.25)).item()\n",
+    "    q3 = df.select(pl.col(col).quantile(0.75)).item()\n",
+    "    iqr = q3 - q1\n",
+    "    lower = q1 - 1.5 * iqr\n",
+    "    upper = q3 + 1.5 * iqr\n",
+    "    outliers = df.filter((pl.col(col) < lower) | (pl.col(col) > upper))\n",
+    "    return outliers\n",
+    "\n",
+    "outliers_enmo = detect_outliers_iqr(df, \"enmo\")\n",
+    "outliers_anglez = detect_outliers_iqr(df, \"anglez\")\n",
+    "\n",
+    "print(\"Outliers in enmo:\", outliers_enmo.shape)\n",
+    "print(\"Outliers in anglez:\", outliers_anglez.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "b076aa2c-49a8-451b-a35d-03b5f5b379b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def winsorize_column(df, col_name, lower=0.01, upper=0.99):\n",
+    "    # Calculate the lower and upper quantiles\n",
+    "    quantiles = df.select([\n",
+    "        pl.col(col_name).quantile(lower).alias(\"lower\"),\n",
+    "        pl.col(col_name).quantile(upper).alias(\"upper\")\n",
+    "    ]).to_dict(as_series=False)\n",
+    "\n",
+    "    lower_bound = quantiles['lower'][0]\n",
+    "    upper_bound = quantiles['upper'][0]\n",
+    "\n",
+    "    # Apply winsorization\n",
+    "    df = df.with_columns([\n",
+    "        pl.when(pl.col(col_name) < lower_bound).then(lower_bound)\n",
+    "        .when(pl.col(col_name) > upper_bound).then(upper_bound)\n",
+    "        .otherwise(pl.col(col_name))\n",
+    "        .alias(col_name)\n",
+    "    ])\n",
+    "    return df\n",
+    "\n",
+    "# Apply to both 'enmo' and 'anglez'\n",
+    "df = winsorize_column(df, 'enmo')\n",
+    "df = winsorize_column(df, 'anglez')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b4c0e9b-3052-46c4-84a6-d5eb4978d651",
+   "metadata": {},
+   "source": [
+    "* Target Variable: sleep (i.e., Sleep State)\n",
+    "\n",
+    "* Input Features: From the df DataFrame , \n",
+    "enmo, anglez, timestamp, step, series_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d544d4db-b24a-4c36-bde8-3abdf33fb715",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Join the data\n",
+    "full_df = df.join(events.select(['series_id', 'step', 'event']), on=['series_id', 'step'], how='left')\n",
+    "\n",
+    "# Rename 'event' to 'sleep' as target variable\n",
+    "full_df = full_df.rename({\"event\": \"sleep\"})\n",
+    "\n",
+    "# Drop rows with missing sleep labels (if any)\n",
+    "full_df = full_df.drop_nulls(subset=[\"sleep\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8aa7e29a-ecfa-43e3-ab91-97aad866c05d",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'full_df' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m full_df\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'full_df' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "full_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b25dd633-a863-49f5-80a0-9b0ee8f3f598",
+   "metadata": {},
+   "source": [
+    "### 1d. Data Understanding"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a98c53a0-d64e-4062-9032-68b53452b48b",
+   "metadata": {},
+   "source": [
+    "* Identify Target Variable\n",
+    "✅ Target Variable = \"sleep\" (You already renamed event to sleep).\n",
+    "\n",
+    "* But sleep has labels like \"onset\", \"wakeup\", etc.\n",
+    "➡️ So, this is a Classification Task, not Regression.\n",
+    "\n",
+    "* series_id, step, timestamp, anglez, enmo  are the Numerical Features.\n",
+    "\n",
+    "* sleep is the categorical Feature and it is the Target Variable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e7ccfc8-fc2f-45fc-902c-45903750faa7",
+   "metadata": {},
+   "source": [
+    "####  * series_id: Unique ID for each participant.  \n",
+    "####  * step: Time step index.                     \n",
+    "####  * timestamp: Date and time of recording.     \n",
+    "####  * anglez: Angle of the arm relative to the body \n",
+    "####  * enmo: Processed movement data                \n",
+    "####  * sleep : Type of event (onset or wakeup).   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f898d361-ae10-49bb-9dd3-31c09f4ccfb2",
+   "metadata": {},
+   "source": [
+    "### EDA - Exploratory Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "be963ad7-1110-422a-b668-0f6fe33a6e63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_df_pandas = full_df.to_pandas()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e9fc88be-1d17-4e55-a664-a1674e5dfa94",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1. Distribution Plot for 'anglez'\n",
+    "plt.style.use('dark_background')\n",
+    "plt.figure(figsize=(8,4))\n",
+    "sns.histplot(full_df_pandas['anglez'], kde=True, bins=30, color='skyblue')\n",
+    "plt.title('Distribution of anglez')\n",
+    "plt.xlabel('anglez')\n",
+    "plt.ylabel('Count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6688a093-00ed-476c-b28f-b91d1d6a98df",
+   "metadata": {},
+   "source": [
+    "#### Insights :\n",
+    "\n",
+    "* The data is skewed left with more frequent values in the negative range (especially between -70 and -30).\n",
+    "\n",
+    "* Most body angles are negative → sensors were tilted backward often.\n",
+    "\n",
+    "* There’s a clear peak around -50 to -40, so that angle was common.\n",
+    "\n",
+    "* Less data at high positive angles → forward tilt was less common.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "edb7ef4c-1e72-4503-b7c7-8085b99d554b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 2. Distribution Plot for 'enmo'\n",
+    "plt.style.use('dark_background')\n",
+    "plt.figure(figsize=(8,4))\n",
+    "sns.histplot(full_df_pandas['enmo'], kde=True, bins=30, color='lightgreen')\n",
+    "plt.title('Distribution of enmo')\n",
+    "plt.xlabel('enmo')\n",
+    "plt.ylabel('Count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b4a5604-a29c-4eec-8416-3df91db596d7",
+   "metadata": {},
+   "source": [
+    "#### insights :\n",
+    "\n",
+    "* Most of the time, the subject was not moving much — possibly resting or sleeping.\n",
+    "\n",
+    "* The sharp drop-off means that high movement was rare.\n",
+    "\n",
+    "* This kind of shape is called right-skewed, meaning most values are small, but a few large values stretch the tail."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "6b8b3c41-fee6-4293-90c9-90532ad8dd99",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 3. Scatter Plot of 'anglez' vs 'enmo' colored by 'sleep' \n",
+    "plt.style.use('dark_background')  \n",
+    "plt.figure(figsize=(8,6))\n",
+    "sns.scatterplot(data=full_df_pandas, x='anglez', y='enmo', hue='sleep', palette='dark')\n",
+    "plt.title('Scatter Plot of anglez vs enmo by Sleep State')\n",
+    "plt.xlabel('anglez')\n",
+    "plt.ylabel('enmo')\n",
+    "plt.legend(title='Sleep State')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "444280e9-233b-4827-962e-9e453ee42337",
+   "metadata": {},
+   "source": [
+    "#### insights:\n",
+    "\n",
+    "* Most sleep onset (blue) points are clustered at low enmo values.\n",
+    "\n",
+    "* Wakeup (orange) points tend to have higher enmo, showing more movement.\n",
+    "\n",
+    "* anglez is spread for both states, less useful alone, but might add value when combined with enmo."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "496ddacf-648d-4ac8-8a2c-1c3c0a5f0629",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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qLCxU165dtWbNmgYbqQEAAOD89Oijj2rw4MHq3Lmz4uLi5Ovr63T9nXfeOa37NGVj3N+aM2eOjh49qmHDhjnGioqKzviebLgLwJvk5OScdl23bt3OcTcA4D6nHdzeeuut57AN15owYYImTJjg7jYAAABgQg8++KA+/fRTXXvttWrTps1pT044mTPZGPdEb775ptLS0rR69eoG32A703tOmTJFqampjvPy8nJFRkaeTvsAYDqn2uTxt3UEtwC82WkHt08//fS57AMAAABoFsuXL9eqVas0ePDgs7pPUzbGrbdy5UqNHTtW//jHP3TDDTc4XYuIiDjje7LhLgBvcuzYMcfrHj16qEePHvLz81N1dbWys7OVnZ3doA4AvNFZr3ELAAAAeJLWrVurc+fOZ32fpm6M++abb2rMmDFasWJFo+Fx7969G9xz/fr1bLYL4LxhGIYkydfXV6mpqUpOTlb//v2VnJys1NRU+fj4ONUBgLc67Rm3J2rVqlWjX9WyWCzy9/fXJZdcojFjxujuu+8+6wYBAAAAV0pLS9PTTz+tpUuX6oILLjire6WmpmrUqFFKSEhQ7969tXjxYqeNcadMmaJ9+/Zp+fLlko6HtnfddZfmzp2rq6++2jGzNiAgQMHBwZKklJQUXXPNNZo1a5aGDh2q1atX6+OPP9bmzZvPqlcA8BT1eUNNTY3mzJmjHj16yGazqaqqStnZ2aqtrXWqAwBv1aTg9m9/+5umT5+uQYMGKTExUYZh6Msvv9TatWs1ceJE7d27Vw888IBqa2t17733urpnAAAAoMnmzZun//73vwoPD1dUVFSDzcnOZKPd39sYt7CwUPn5+Y76l19+WbW1tZo4caImTpzoGB89erSWLVsmSerTp4/eeustPfnkk3rqqafUuXNnrVy5Ur169TqLTw0AniMgIMDxevfu3dq9e/fv1gGAN2pScLt582Y9++yzjpkE9V5++WWtX79eq1atUrdu3TRv3jyCWwAAAJiKqzfdPdXGuPVhbL3PPvvstO55++236/bbbz/LzgDAM/Xt21f/+te/TqsOALxZk4LbdevWadasWQ3Gr7/+ej3yyCOSpJtuuklPPPHE2XUHAAAAuBib7gKAucXFxTleWywWp7VsTzw/sQ4AvFGTNidr3bq1/vnPfzYY/+c//6nWrVtLko4ePaqWLVueXXcAAADAOVJdXa2ff/5Z+fn5TgcAwL3y8vIcr3+7AdmJ5yfWAYA3atKM26eeekoPPPCAPv30UyUmJspisWj79u1as2aNXnrpJUnHd9Pt16+fS5sFAAAAzlZeXp7Gjh2rLVu2OI0bhiGLxaK6ujo3dQYAkKRDhw65tA4APFWTgtt7771XcXFxevHFF/XOO+/IMAzFxsbq888/V58+fSTJsWQCAAAAYCZ33323fHx89MEHH6hdu3bsSg4AJnP48GFJkr+/vxYuXKiNGzequLhYbdu21XXXXacJEyaosrLSUQcA3qpJwa0kJSUlKSkpyZW9AAAAAOdcdna2du7cqdjYWHe3AgBoRP2yNYGBgXr88cd14MABx7W1a9fqggsuUGVlJcvbAPB6TQ5u7Xa7vv/+exUXF8tutztdu+aaa866MQAAAOBciIuLcwoBAADmUlVVJUk6ePBgg29FHDx40LHObX0dAHirJgW3//73vzVixAj99NNPDRYKZ10wAAAAmNmsWbP0+OOPa8aMGbriiivk6+vrdD0oKMhNnQEAJCkmJkY7duyQdOrNyWJiYpq1LwBobk0KbsePH6+EhAR9+OGHrAsGAAAAj3LDDTdIkq677jqn51g2JwMAc+jQoYPjdVBQkP7whz+obdu2Ki4u1ubNm1VeXt6gDgC8UZOC2++++07/+7//q0suucTV/QAAAADn1KeffuruFgAAp5Cbm+t4XVFRoTVr1jjOT/yFW25urrp3796svQFAc7I25U29evXS999/7+peAAAAgHOuX79+slqteuWVV/TEE0/okksuUb9+/ZSfn68WLVq4uz0AOO/Vr0MeGRnZ6FIJF198sVMdAHirJs24ffDBB/XII4+oqKio0XXBunXr5pLmAAAAAFdbtWqVRo0apZEjRyorK8uxuU1FRYVmzJjhNLMLAND8QkNDJUkFBQXy8fFRbW2t45qPj49+/vlnpzoA8FZNCm7/9Kc/SZLuueeeBtdYFwwAAABm9uyzz+qll17SXXfdpbfeessx3qdPH02bNs2NnQEAJCkuLk6rV6+WJKfQ9rfncXFxzdoXADS3JgW3e/fudXUfAAAAQLP49ttvdc011zQYDwoKUmlpafM3BABw8tvlEc62DgA8VZOC244dO0qScnJylJ+fr+rqasc1i8XiuA4AAACYTbt27fT9998rKirKaXzz5s3q1KmTe5oCADjk5OQ4Xvv6+qqmpqbR85ycHJZqBODVmhTc/vDDD/rjH/+o//znP7JYLI7fctXv7shSCQAAADCr+++/XykpKVqyZIksFov279+vrVu36tFHH9Xf/vY3d7cHAOe9H374QZJ05ZVXKj8/32kTspCQEF100UXKzs521AGAt2pScJuSkqLo6Gh9/PHH6tSpk7Zt26ZDhw7pkUce0ezZs13dIwAAAOAyjz/+uMrKynTttdeqsrJS11xzjWw2mx599FFNmjTJ3e0BwHnPZrNJOh7g/nYJm5KSEse3fuvrAMBbNSm43bp1qzZu3KiwsDBZrVa1aNFCf/jDHzRz5kw99NBDysrKcnWfAAAAgMtMnz5dU6dOVU5Ojux2u+Li4nThhRe6uy0AgKTY2Fjt3LnzpOuOl5WVOeoAwJtZm/Kmuro6x4NtaGio9u/fL+n42rfffvut67oDAAAAzpELLrhACQkJSkxMJLQFABO5/vrrXVoHAJ6qScFt165d9dVXX0mSevXqpeeee07/+te/NG3aNDZ0AAAAAAAATbZu3TqX1gGAp2pScPvkk0/KbrdLkp599ln99NNP6tu3r9asWaN58+a5tEEAAAAAAHD++Ne//iVJatGiRaPX68fr6wDAWzVpjduBAwc6Xnfq1Ek5OTk6dOiQWrVqJYvF4rLmAAAAAADA+eXXX3+VdHyZxsbUj9fXAYC3atKM28a0bt2a0BYAAAAAAJyV8PBwx2s/Pz+nayeen1gHAN7IZcEtAAAAAADA2ToxkK2urna6duI5wS0Ab0dwCwAAAAAATKO8vNyldQDgqQhuAQAAAACAafx2lu3Z1gGApyK4BQAAAAAApnHhhRe6tA4APJWPuxsAAAAAAACod+LG54GBgeratatsNpuqqqr09ddf6+jRow3qAMAbEdwCAAAAAADTOHHt2qNHj2rbtm2/WwcA3oilEgAAAAAAgGn4+/u7tA4APBXBLQAAAAAAMI0uXbq4tA4APBXBLQAAAAAAMI2oqCiX1gGApyK4BQAAAAAApnHo0CGX1gGApyK4BQAAAAAAprF9+3aX1gGApyK4BQAAAAAAprF//36X1gGApyK4BQAAAAAApnHs2DGn827duiktLU3dunU7ZR0AeBsfdzcAAAAAAABQ74ILLlB5ebkkqVWrVvrqq6/01VdfOc4PHz7sqAMAb0ZwC4fasiPubgEAAJwG/jcbAODNKisrHa/LysrUu3dvderUST/88IO2bdvWaB0AeCOCWziU/us/7m4BAAAAAHCe8/H5f1GF3W7X1q1btXXr1lPWAYA34l85OIQkXSGf4Avd3QYAAPgdtWVH+IUrAMBrdejQQQcOHDitOgDwZgS3cPAJvlC+bYLd3QYAAAAA4Dw2adIk3XPPPadVBwDezOruBgAAAAAAAOr5+fnJaj11XGG1WuXn59dMHQGAexDcAgAAAAAA08jJyZHdbj9ljd1uV05OTjN1BADuQXALAAAAAABMY8+ePS6tAwBPRXALAAAAAABMo6SkxPG6RYsWTtdOPD+xDgC8kUcEtz/++KPGjh2r6OhoBQQEqHPnznr66adVXV3tVJefn6+bb75ZgYGBCg0N1UMPPdSgBgAAAAAAmFdZWZnj9amC2xPrAMAb+bi7gdORm5sru92ul19+WZdccom+/vpr3XvvvTp69Khmz54tSaqrq9PgwYMVFhamzZs36+DBgxo9erQMw9D8+fPd/AkAAAAAAMDpOHECls1m06hRo3TllVdq165devvttx3XmagFwNt5RHB744036sYbb3Scd+rUSd9++60WLVrkCG7Xr1+vnJwcFRQUqH379pKkOXPmaMyYMZo+fbqCgoLc0jsAAAAAADh9vr6+jteVlZV67bXX9NprrzW4duJrAPBGHrFUQmPKysrUunVrx/nWrVvVtWtXR2grSQMHDlRVVZV27tzpjhYBAAAAAMAZCgkJcbyuqalxunbi+Yl1AOCNPGLG7W/997//1fz58zVnzhzHWFFRkcLDw53qWrVqJT8/PxUVFZ30XlVVVaqqqnKcl5eXu75hAAAAAABwWsLCwlxaBwCeyq0zbtPS0mSxWE557Nixw+k9+/fv14033qg///nPGjdunNM1i8XS4GcYhtHoeL2ZM2cqODjYcURGRrrmwwEAAAAAgDN26aWXurQOADyVW2fcTpo0SXfccccpa6Kiohyv9+/fr2uvvVa9e/fW4sWLneoiIiK0bds2p7HDhw+rpqamwUzcE02ZMkWpqamO8/LycsJbAAAAAADc5LcTuE5V16NHj3PbDAC4kVtn3IaGhio2NvaUh7+/vyRp37596t+/v6688kotXbpUVqtz671799bXX3+twsJCx9j69etls9nUs2fPk/Zgs9kUFBTkdAAAAACna+HChYqOjpa/v7969uypTZs2nbS2sLBQI0aM0KWXXiqr1arJkyc3qFm2bFmj30SrrKw8h58CAMxjz549Lq0DAE/lEZuT7d+/X/3791dkZKRmz56tkpISFRUVOa1dm5ycrLi4OI0aNUpZWVn65JNP9Oijj+ree+8ljAUAAMA5sXLlSk2ePFlTp05VVlaW+vbtq0GDBik/P7/R+qqqKoW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+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create side-by-side boxplots for 'anglez' and 'enmo'\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "\n",
+    "# Boxplot for 'anglez'\n",
+    "sns.boxplot(data=full_df_pandas, y=\"anglez\", palette='Set2', ax=ax[0])\n",
+    "ax[0].set_title(\"Boxplot of anglez\")\n",
+    "ax[0].set_ylabel(\"anglez\")\n",
+    "\n",
+    "# Boxplot for 'enmo'\n",
+    "sns.boxplot(data=full_df_pandas, y=\"enmo\", palette='Set2', ax=ax[1])\n",
+    "ax[1].set_title(\"Boxplot of enmo\")\n",
+    "ax[1].set_ylabel(\"enmo\")\n",
+    "\n",
+    "# Display the plots\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "496dabc2-2d6b-4f6f-8785-459f92325d7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 603.75x500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 5. Pairplot with dark background and custom dark color palette\n",
+    "plt.style.use('default')  # Dark background\n",
+    "\n",
+    "# Pairplot with hue based on 'sleep', using a custom dark color palette\n",
+    "sns.pairplot(\n",
+    "    full_df_pandas,\n",
+    "    hue='sleep',\n",
+    "    vars=['anglez', 'enmo'],\n",
+    "    palette='dark'  # Dark color palette\n",
+    ")\n",
+    "\n",
+    "# Title and adjustments for dark background\n",
+    "plt.suptitle('Pairplot of Features by Sleep State', y=1.02, color='white')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fecc3ef1-e01a-4910-97c2-41673e1336eb",
+   "metadata": {},
+   "source": [
+    "#### insights:\n",
+    "\n",
+    "* The pairplot compares anglez and enmo across sleep states (onset vs. wakeup).\n",
+    "\n",
+    "* Onset: Lower anglez and minimal enmo, indicating still body posture and little movement.\n",
+    "\n",
+    "* Wakeup: Higher and more spread-out values in both features, showing increased movement and posture shifts.\n",
+    "\n",
+    "* These patterns help distinguish between sleep onset and wakeup phases effectively."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e0cca4d-e088-42fc-8d1a-966dd44c57d3",
+   "metadata": {},
+   "source": [
+    "### Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "ad31bc2e-5128-4390-bfe1-edaf4c2ad715",
+   "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>series_id</th>\n",
+       "      <th>step</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>anglez</th>\n",
+       "      <th>enmo</th>\n",
+       "      <th>sleep</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>038441c925bb</td>\n",
+       "      <td>4992</td>\n",
+       "      <td>2018-08-15 02:26:00+00:00</td>\n",
+       "      <td>-78.690598</td>\n",
+       "      <td>0.0099</td>\n",
+       "      <td>onset</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>038441c925bb</td>\n",
+       "      <td>10932</td>\n",
+       "      <td>2018-08-15 10:41:00+00:00</td>\n",
+       "      <td>-61.578201</td>\n",
+       "      <td>0.0263</td>\n",
+       "      <td>wakeup</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>038441c925bb</td>\n",
+       "      <td>20244</td>\n",
+       "      <td>2018-08-15 23:37:00+00:00</td>\n",
+       "      <td>-6.387400</td>\n",
+       "      <td>0.0182</td>\n",
+       "      <td>onset</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>038441c925bb</td>\n",
+       "      <td>27492</td>\n",
+       "      <td>2018-08-16 09:41:00+00:00</td>\n",
+       "      <td>-45.355099</td>\n",
+       "      <td>0.0165</td>\n",
+       "      <td>wakeup</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>038441c925bb</td>\n",
+       "      <td>39996</td>\n",
+       "      <td>2018-08-17 03:03:00+00:00</td>\n",
+       "      <td>-1.786700</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>onset</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9580</th>\n",
+       "      <td>fe90110788d2</td>\n",
+       "      <td>547152</td>\n",
+       "      <td>2017-09-05 13:26:00+00:00</td>\n",
+       "      <td>-56.768902</td>\n",
+       "      <td>0.0042</td>\n",
+       "      <td>wakeup</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9581</th>\n",
+       "      <td>fe90110788d2</td>\n",
+       "      <td>556560</td>\n",
+       "      <td>2017-09-06 02:30:00+00:00</td>\n",
+       "      <td>-16.586700</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>onset</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9582</th>\n",
+       "      <td>fe90110788d2</td>\n",
+       "      <td>560604</td>\n",
+       "      <td>2017-09-06 08:07:00+00:00</td>\n",
+       "      <td>-43.050900</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>wakeup</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9583</th>\n",
+       "      <td>fe90110788d2</td>\n",
+       "      <td>574620</td>\n",
+       "      <td>2017-09-07 03:35:00+00:00</td>\n",
+       "      <td>1.519100</td>\n",
+       "      <td>0.0066</td>\n",
+       "      <td>onset</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9584</th>\n",
+       "      <td>fe90110788d2</td>\n",
+       "      <td>581604</td>\n",
+       "      <td>2017-09-07 13:17:00+00:00</td>\n",
+       "      <td>78.912697</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>wakeup</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>9585 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         series_id    step                 timestamp     anglez    enmo  \\\n",
+       "0     038441c925bb    4992 2018-08-15 02:26:00+00:00 -78.690598  0.0099   \n",
+       "1     038441c925bb   10932 2018-08-15 10:41:00+00:00 -61.578201  0.0263   \n",
+       "2     038441c925bb   20244 2018-08-15 23:37:00+00:00  -6.387400  0.0182   \n",
+       "3     038441c925bb   27492 2018-08-16 09:41:00+00:00 -45.355099  0.0165   \n",
+       "4     038441c925bb   39996 2018-08-17 03:03:00+00:00  -1.786700  0.0000   \n",
+       "...            ...     ...                       ...        ...     ...   \n",
+       "9580  fe90110788d2  547152 2017-09-05 13:26:00+00:00 -56.768902  0.0042   \n",
+       "9581  fe90110788d2  556560 2017-09-06 02:30:00+00:00 -16.586700  0.0000   \n",
+       "9582  fe90110788d2  560604 2017-09-06 08:07:00+00:00 -43.050900  0.0000   \n",
+       "9583  fe90110788d2  574620 2017-09-07 03:35:00+00:00   1.519100  0.0066   \n",
+       "9584  fe90110788d2  581604 2017-09-07 13:17:00+00:00  78.912697  0.0000   \n",
+       "\n",
+       "       sleep  \n",
+       "0      onset  \n",
+       "1     wakeup  \n",
+       "2      onset  \n",
+       "3     wakeup  \n",
+       "4      onset  \n",
+       "...      ...  \n",
+       "9580  wakeup  \n",
+       "9581   onset  \n",
+       "9582  wakeup  \n",
+       "9583   onset  \n",
+       "9584  wakeup  \n",
+       "\n",
+       "[9585 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 0. Start from your cleaned pandas df with target 'sleep'\n",
+    "# full_df_pandas = full_df.to_pandas()\n",
+    "df = full_df_pandas.copy()\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "152e233f-85b5-40fc-b40e-ea9de81e80c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"cleaned_sleep_data.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "74e2219a-de5f-4692-95ed-ce8707e89a69",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'C:\\\\Users\\\\ragha\\\\Machine learning\\\\AI for sleep detection'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6527b889-c3d3-4cc6-a729-edd336700109",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'pd' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39mpd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mC:\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mUsers\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mragha\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mDownloads\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mcleaned_sleep_data (1).xls\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      2\u001b[0m df\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "df =pd.read_csv(r\"C:\\Users\\ragha\\Downloads\\cleaned_sleep_data (1).xls\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "30600f06-954c-4ae1-8a0b-d9b1c5b71f79",
+   "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>series_id</th>\n",
+       "      <th>step</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>anglez</th>\n",
+       "      <th>enmo</th>\n",
+       "      <th>sleep</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>038441c925bb</td>\n",
+       "      <td>4992</td>\n",
+       "      <td>2018-08-15 02:26:00+00:00</td>\n",
+       "      <td>-78.6906</td>\n",
+       "      <td>0.0099</td>\n",
+       "      <td>onset</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>038441c925bb</td>\n",
+       "      <td>10932</td>\n",
+       "      <td>2018-08-15 10:41:00+00:00</td>\n",
+       "      <td>-61.5782</td>\n",
+       "      <td>0.0263</td>\n",
+       "      <td>wakeup</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>038441c925bb</td>\n",
+       "      <td>20244</td>\n",
+       "      <td>2018-08-15 23:37:00+00:00</td>\n",
+       "      <td>-6.3874</td>\n",
+       "      <td>0.0182</td>\n",
+       "      <td>onset</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>038441c925bb</td>\n",
+       "      <td>27492</td>\n",
+       "      <td>2018-08-16 09:41:00+00:00</td>\n",
+       "      <td>-45.3551</td>\n",
+       "      <td>0.0165</td>\n",
+       "      <td>wakeup</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>038441c925bb</td>\n",
+       "      <td>39996</td>\n",
+       "      <td>2018-08-17 03:03:00+00:00</td>\n",
+       "      <td>-1.7867</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>onset</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      series_id   step                  timestamp   anglez    enmo   sleep\n",
+       "0  038441c925bb   4992  2018-08-15 02:26:00+00:00 -78.6906  0.0099   onset\n",
+       "1  038441c925bb  10932  2018-08-15 10:41:00+00:00 -61.5782  0.0263  wakeup\n",
+       "2  038441c925bb  20244  2018-08-15 23:37:00+00:00  -6.3874  0.0182   onset\n",
+       "3  038441c925bb  27492  2018-08-16 09:41:00+00:00 -45.3551  0.0165  wakeup\n",
+       "4  038441c925bb  39996  2018-08-17 03:03:00+00:00  -1.7867  0.0000   onset"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "1fa972a5-f9df-4b4a-a857-16d1e86537ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 9585 entries, 0 to 9584\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   series_id  9585 non-null   object \n",
+      " 1   step       9585 non-null   int64  \n",
+      " 2   timestamp  9585 non-null   object \n",
+      " 3   anglez     9585 non-null   float64\n",
+      " 4   enmo       9585 non-null   float64\n",
+      " 5   sleep      9585 non-null   object \n",
+      "dtypes: float64(2), int64(1), object(3)\n",
+      "memory usage: 449.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "e4dacbcb-38aa-4f8f-a352-0ac3522e7ddb",
+   "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>step</th>\n",
+       "      <th>anglez</th>\n",
+       "      <th>enmo</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>9585.000000</td>\n",
+       "      <td>9585.000000</td>\n",
+       "      <td>9585.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>214352.123944</td>\n",
+       "      <td>-9.713789</td>\n",
+       "      <td>0.014887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>141268.408192</td>\n",
+       "      <td>45.969550</td>\n",
+       "      <td>0.034452</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>936.000000</td>\n",
+       "      <td>-84.653400</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>95436.000000</td>\n",
+       "      <td>-48.502800</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>200604.000000</td>\n",
+       "      <td>-16.017700</td>\n",
+       "      <td>0.001300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>317520.000000</td>\n",
+       "      <td>28.466300</td>\n",
+       "      <td>0.017400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>739392.000000</td>\n",
+       "      <td>78.912700</td>\n",
+       "      <td>0.394300</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                step       anglez         enmo\n",
+       "count    9585.000000  9585.000000  9585.000000\n",
+       "mean   214352.123944    -9.713789     0.014887\n",
+       "std    141268.408192    45.969550     0.034452\n",
+       "min       936.000000   -84.653400     0.000000\n",
+       "25%     95436.000000   -48.502800     0.000000\n",
+       "50%    200604.000000   -16.017700     0.001300\n",
+       "75%    317520.000000    28.466300     0.017400\n",
+       "max    739392.000000    78.912700     0.394300"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "d6907401-5e64-42aa-9a0b-8ca4d8d3715e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split out X / y\n",
+    "X = df.drop(columns=['sleep'])\n",
+    "y = df['sleep']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "9902334e-c38c-42f2-9c37-ece66b5ce54f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sleep\n",
+      "wakeup    0.500156\n",
+      "onset     0.499844\n",
+      "Name: proportion, dtype: float64\n",
+      "sleep\n",
+      "wakeup    4794\n",
+      "onset     4791\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(y.value_counts(normalize=True))  # Shows proportion\n",
+    "print(y.value_counts())               # Shows actual counts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4915f146-461e-4df0-9145-434f5112672d",
+   "metadata": {},
+   "source": [
+    "#### ✅ Conclusion:\n",
+    "* The dataset is perfectly balanced — the two classes (wakeup and onset) are nearly equal in count and proportion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "67e94038-9ef5-458d-b778-007898a02e5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Advanced Feature Selection Methods\n",
+    "# 1. Variance Threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "e9059875-96e9-465a-934f-b87c61b15a92",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Selected Features after Variance Threshold: ['enmo', 'anglez']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.feature_selection import VarianceThreshold\n",
+    "\n",
+    "# Define the feature columns for prediction\n",
+    "features = ['enmo', 'anglez']\n",
+    "selector = VarianceThreshold(threshold=0.001)\n",
+    "\n",
+    "# Fit the selector to your selected features\n",
+    "selector.fit(X[features])\n",
+    "selected_features = [feature for feature, keep in zip(features, selector.get_support()) if keep]\n",
+    "print('Selected Features after Variance Threshold:', selected_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "bdbb0706-94ef-432e-b82e-58daaa50a279",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 2. Correlation Matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "161cd8be-4323-4572-a315-a5b0deddb66f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Define the features to include in the correlation matrix\n",
+    "features = ['anglez', 'enmo']\n",
+    "corr = X[features].corr()\n",
+    "\n",
+    "# Plot the heatmap\n",
+    "plt.figure(figsize=(6, 5))\n",
+    "sns.heatmap(corr, annot=True, fmt='.2f', cmap='coolwarm', square=True, cbar_kws={'shrink': 0.8})\n",
+    "plt.title('Correlation Matrix: Anglez, Enmo' ,fontsize=14)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.yticks(rotation=0)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "6694d368-ed5e-4046-82e8-07f904ae92ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['onset' 'wakeup']\n",
+      "{'onset': 0, 'wakeup': 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(le.classes_)\n",
+    "print(dict(zip(le.classes_, le.transform(le.classes_))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "2cc6dc77-873d-4b86-95a1-d428a5f1c1f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "\n",
+    "# 1. Separate input features (X) and output target (y)\n",
+    "X = df[['anglez', 'enmo']]\n",
+    "y = df['sleep']\n",
+    "\n",
+    "# 2. Encode the target variable\n",
+    "le = LabelEncoder()\n",
+    "y_encoded = le.fit_transform(y)\n",
+    "\n",
+    "# 3. Split into train and test sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y_encoded, test_size=0.2, random_state=42, stratify=y_encoded)\n",
+    "\n",
+    "# 4. Define preprocessing for numeric features\n",
+    "numeric_features = ['anglez', 'enmo']\n",
+    "numeric_transformer = Pipeline(steps=[\n",
+    "    ('scaler', StandardScaler())\n",
+    "])\n",
+    "\n",
+    "# 5. Combine into a ColumnTransformer\n",
+    "preprocessor = ColumnTransformer(\n",
+    "    transformers=[\n",
+    "        ('num', numeric_transformer, numeric_features)\n",
+    "    ])\n",
+    "\n",
+    "# 6. Create final Preprocessing Pipeline\n",
+    "preprocessing_pipeline = Pipeline(steps=[\n",
+    "    ('preprocessor', preprocessor)\n",
+    "])\n",
+    "\n",
+    "# 7. Fit and transform the train data, transform the test data\n",
+    "X_train_processed = preprocessing_pipeline.fit_transform(X_train)\n",
+    "X_test_processed = preprocessing_pipeline.transform(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91c1b5b2-26ff-496d-a1ea-2145a2b934ad",
+   "metadata": {},
+   "source": [
+    "#### Step\t   -   What We Did\n",
+    "* Scaling\t   -   StandardScaler for numeric features\n",
+    "* Encoding   -   LabelEncoder for target\n",
+    "* Pipeline   -   Combined scaling into one clean pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "cf70890f-473f-4d0b-bb75-363a8b42f4aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
+       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;scaler&#x27;,\n",
+       "                                                                   StandardScaler())]),\n",
+       "                                                  [&#x27;anglez&#x27;, &#x27;enmo&#x27;])]))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
+       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;scaler&#x27;,\n",
+       "                                                                   StandardScaler())]),\n",
+       "                                                  [&#x27;anglez&#x27;, &#x27;enmo&#x27;])]))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
+       "                                 Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler())]),\n",
+       "                                 [&#x27;anglez&#x27;, &#x27;enmo&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;anglez&#x27;, &#x27;enmo&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('preprocessor',\n",
+       "                 ColumnTransformer(transformers=[('num',\n",
+       "                                                  Pipeline(steps=[('scaler',\n",
+       "                                                                   StandardScaler())]),\n",
+       "                                                  ['anglez', 'enmo'])]))])"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn import set_config\n",
+    "set_config(display='diagram')\n",
+    "preprocessing_pipeline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c632a47-3202-43cf-ac48-b97791ae0915",
+   "metadata": {},
+   "source": [
+    "#### Pipeline\n",
+    "\n",
+    "➔ preprocessor: ColumnTransformer\n",
+    "\n",
+    "➔ num: StandardScaler\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbd24dee-76a8-40e7-a59a-2c65a0d2bb3c",
+   "metadata": {},
+   "source": [
+    "#### ✅ It shows that:\n",
+    "\n",
+    "* The numeric features (anglez, enmo) are passing through the StandardScaler inside the ColumnTransformer.\n",
+    "\n",
+    "* Everything is properly organized inside one clean Pipeline."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be727073-bd11-4494-98ec-07015df964ba",
+   "metadata": {},
+   "source": [
+    "# 3. Model Building"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "dafd433e-f25d-4aba-8587-f46524739889",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: KNN\n",
+      "Accuracy: 0.5837\n",
+      "Precision: 0.5840\n",
+      "Recall: 0.5837\n",
+      "F1 Score: 0.5834\n",
+      "--------------------------------------------------\n",
+      "Model: Naive Bayes\n",
+      "Accuracy: 0.5769\n",
+      "Precision: 0.6652\n",
+      "Recall: 0.5769\n",
+      "F1 Score: 0.5115\n",
+      "--------------------------------------------------\n",
+      "Model: Decision Tree\n",
+      "Accuracy: 0.5472\n",
+      "Precision: 0.5472\n",
+      "Recall: 0.5472\n",
+      "F1 Score: 0.5472\n",
+      "--------------------------------------------------\n",
+      "Model: Logistic Regression\n",
+      "Accuracy: 0.6119\n",
+      "Precision: 0.6297\n",
+      "Recall: 0.6119\n",
+      "F1 Score: 0.5980\n",
+      "--------------------------------------------------\n",
+      "Model: SVC\n",
+      "Accuracy: 0.6176\n",
+      "Precision: 0.6304\n",
+      "Recall: 0.6176\n",
+      "F1 Score: 0.6079\n",
+      "--------------------------------------------------\n",
+      "Model: Random Forest\n",
+      "Accuracy: 0.5556\n",
+      "Precision: 0.5557\n",
+      "Recall: 0.5556\n",
+      "F1 Score: 0.5552\n",
+      "--------------------------------------------------\n",
+      "Model: XGBoost\n",
+      "Accuracy: 0.6041\n",
+      "Precision: 0.6078\n",
+      "Recall: 0.6041\n",
+      "F1 Score: 0.6006\n",
+      "--------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from xgboost import XGBClassifier\n",
+    "\n",
+    "from sklearn.metrics import classification_report, accuracy_score, precision_score, recall_score, f1_score\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "# 1. Define input features and target\n",
+    "X = df[['anglez', 'enmo']]\n",
+    "y = df['sleep']\n",
+    "\n",
+    "# 2. Encode target variable\n",
+    "label_encoder = LabelEncoder()\n",
+    "y_encoded = label_encoder.fit_transform(y)\n",
+    "\n",
+    "# 3. Split the data\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y_encoded, test_size=0.2, random_state=42, stratify=y_encoded\n",
+    ")\n",
+    "\n",
+    "# 4. Define models\n",
+    "models = {\n",
+    "    \"KNN\": KNeighborsClassifier(),\n",
+    "    \"Naive Bayes\": GaussianNB(),\n",
+    "    \"Decision Tree\": DecisionTreeClassifier(random_state=42),\n",
+    "    \"Logistic Regression\": LogisticRegression(max_iter=1000, random_state=42),\n",
+    "    \"SVC\": SVC(random_state=42),\n",
+    "    \"Random Forest\": RandomForestClassifier(random_state=42),\n",
+    "    \"XGBoost\": XGBClassifier(use_label_encoder=False, eval_metric='mlogloss', random_state=42)\n",
+    "}\n",
+    "\n",
+    "# 5. Train and evaluate models\n",
+    "for model_name, model in models.items():\n",
+    "    clf = Pipeline([\n",
+    "        ('preprocessor', preprocessor),\n",
+    "        ('classifier', model)\n",
+    "    ])\n",
+    "    \n",
+    "    clf.fit(X_train, y_train)\n",
+    "    y_pred = clf.predict(X_test)\n",
+    "    \n",
+    "    # 6. Calculate metrics\n",
+    "    accuracy = accuracy_score(y_test, y_pred)\n",
+    "    precision = precision_score(y_test, y_pred, average='weighted')\n",
+    "    recall = recall_score(y_test, y_pred, average='weighted')\n",
+    "    f1 = f1_score(y_test, y_pred, average='weighted')\n",
+    "\n",
+    "    # 7. Print results\n",
+    "    print(f\"Model: {model_name}\")\n",
+    "    print(f\"Accuracy: {accuracy:.4f}\")\n",
+    "    print(f\"Precision: {precision:.4f}\")\n",
+    "    print(f\"Recall: {recall:.4f}\")\n",
+    "    print(f\"F1 Score: {f1:.4f}\")\n",
+    "    print(\"-\" * 50)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71e709dd-b2e7-49db-a874-9f15890e941e",
+   "metadata": {},
+   "source": [
+    "#### ✅ Model Evaluation Conclusion:\n",
+    "\n",
+    "#### Brief Conclusion:\n",
+    "* SVC achieved the best performance with the highest accuracy (61.76%) and F1 score (60.79%), making it the most suitable model.\n",
+    "  \n",
+    "* Logistic Regression and XGBoost also performed well, while Decision Tree and Random Forest showed the weakest results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8772feb-6592-47bc-b260-591105b85e6b",
+   "metadata": {},
+   "source": [
+    "# 4. Model Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "ab41623d-d50e-4976-b0be-ba2f6f817080",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2025-05-06 18:33:23,372] A new study created in memory with name: no-name-12d22998-979d-4345-83f1-bc6cc1bc782a\n",
+      "[I 2025-05-06 18:33:24,122] Trial 0 finished with value: 0.6078523674297995 and parameters: {'n_estimators': 186, 'max_depth': 4, 'learning_rate': 0.04053350487668709, 'subsample': 0.6661878380477745, 'colsample_bytree': 0.6209761938892137, 'gamma': 1.3844226329343512, 'reg_alpha': 1.39225783561348, 'reg_lambda': 1.2890184360822183}. Best is trial 0 with value: 0.6078523674297995.\n",
+      "[I 2025-05-06 18:33:24,873] Trial 1 finished with value: 0.6048529908293084 and parameters: {'n_estimators': 153, 'max_depth': 10, 'learning_rate': 0.02665195231337626, 'subsample': 0.5443206288245379, 'colsample_bytree': 0.9709383592421865, 'gamma': 0.7084197096737449, 'reg_alpha': 0.4393456574530874, 'reg_lambda': 3.933892344067168}. Best is trial 0 with value: 0.6078523674297995.\n",
+      "[I 2025-05-06 18:33:25,375] Trial 2 finished with value: 0.6027651552843102 and parameters: {'n_estimators': 110, 'max_depth': 8, 'learning_rate': 0.011937922612918291, 'subsample': 0.7271552599851534, 'colsample_bytree': 0.596625721384995, 'gamma': 0.5855944098154175, 'reg_alpha': 2.127579336676428, 'reg_lambda': 4.142096270672944}. Best is trial 0 with value: 0.6078523674297995.\n",
+      "[I 2025-05-06 18:33:25,775] Trial 3 finished with value: 0.6064178681777939 and parameters: {'n_estimators': 115, 'max_depth': 4, 'learning_rate': 0.03780005119647464, 'subsample': 0.7184601259833256, 'colsample_bytree': 0.7561773211304887, 'gamma': 0.9075233564835411, 'reg_alpha': 0.9245593337990954, 'reg_lambda': 2.938536866118861}. Best is trial 0 with value: 0.6078523674297995.\n",
+      "[I 2025-05-06 18:33:26,306] Trial 4 finished with value: 0.6062861293184023 and parameters: {'n_estimators': 158, 'max_depth': 5, 'learning_rate': 0.021089310487662983, 'subsample': 0.8672935614911329, 'colsample_bytree': 0.9445626686267325, 'gamma': 1.373945804978434, 'reg_alpha': 1.1785097839669172, 'reg_lambda': 3.0870978175661197}. Best is trial 0 with value: 0.6078523674297995.\n",
+      "[I 2025-05-06 18:33:26,824] Trial 5 finished with value: 0.605765892647713 and parameters: {'n_estimators': 174, 'max_depth': 10, 'learning_rate': 0.05821538035538862, 'subsample': 0.6733163783539922, 'colsample_bytree': 0.5658664228810171, 'gamma': 1.332275506583855, 'reg_alpha': 1.3506136830876696, 'reg_lambda': 0.8551825136450703}. Best is trial 0 with value: 0.6078523674297995.\n",
+      "[I 2025-05-06 18:33:27,305] Trial 6 finished with value: 0.6055049663593894 and parameters: {'n_estimators': 189, 'max_depth': 3, 'learning_rate': 0.1464702212156839, 'subsample': 0.5564173300919275, 'colsample_bytree': 0.9778633424144114, 'gamma': 1.4260906812913383, 'reg_alpha': 2.9630686628553797, 'reg_lambda': 1.7788768051308517}. Best is trial 0 with value: 0.6078523674297995.\n",
+      "[I 2025-05-06 18:33:27,649] Trial 7 finished with value: 0.6073308550438803 and parameters: {'n_estimators': 149, 'max_depth': 6, 'learning_rate': 0.2671202215035073, 'subsample': 0.7562650609020014, 'colsample_bytree': 0.815046293614391, 'gamma': 2.483439375936708, 'reg_alpha': 0.1787231601655903, 'reg_lambda': 4.425031928682031}. Best is trial 0 with value: 0.6078523674297995.\n",
+      "[I 2025-05-06 18:33:28,721] Trial 8 finished with value: 0.5894642931559579 and parameters: {'n_estimators': 255, 'max_depth': 6, 'learning_rate': 0.12433675334393722, 'subsample': 0.5541622763230483, 'colsample_bytree': 0.7591030862384203, 'gamma': 0.036437856071060426, 'reg_alpha': 0.9064730646104097, 'reg_lambda': 0.3634516701480722}. Best is trial 0 with value: 0.6078523674297995.\n",
+      "[I 2025-05-06 18:33:29,370] Trial 9 finished with value: 0.6056347491220954 and parameters: {'n_estimators': 198, 'max_depth': 9, 'learning_rate': 0.09654262347320995, 'subsample': 0.608935088906313, 'colsample_bytree': 0.5370113580447713, 'gamma': 1.1295034331438214, 'reg_alpha': 0.6812162133211452, 'reg_lambda': 1.7752298451816033}. Best is trial 0 with value: 0.6078523674297995.\n",
+      "[I 2025-05-06 18:33:30,126] Trial 10 finished with value: 0.6087646739144301 and parameters: {'n_estimators': 246, 'max_depth': 3, 'learning_rate': 0.010098591731127331, 'subsample': 0.9828446740375111, 'colsample_bytree': 0.640178523099806, 'gamma': 4.763560520803648, 'reg_alpha': 4.43661522502521, 'reg_lambda': 1.5883617474907732}. Best is trial 10 with value: 0.6087646739144301.\n",
+      "[I 2025-05-06 18:33:30,833] Trial 11 finished with value: 0.6081124432413032 and parameters: {'n_estimators': 249, 'max_depth': 3, 'learning_rate': 0.010008237075047324, 'subsample': 0.9751145656843706, 'colsample_bytree': 0.6725969381910298, 'gamma': 4.8585273474623785, 'reg_alpha': 4.885802330137771, 'reg_lambda': 1.4265893338754358}. Best is trial 10 with value: 0.6087646739144301.\n",
+      "[I 2025-05-06 18:33:31,553] Trial 12 finished with value: 0.6079819800971414 and parameters: {'n_estimators': 258, 'max_depth': 3, 'learning_rate': 0.010442992353127154, 'subsample': 0.9883062045103211, 'colsample_bytree': 0.6762484552212997, 'gamma': 4.948576475145019, 'reg_alpha': 4.935103568686019, 'reg_lambda': 2.1061507006465527}. Best is trial 10 with value: 0.6087646739144301.\n",
+      "[I 2025-05-06 18:33:32,290] Trial 13 finished with value: 0.6104597592640314 and parameters: {'n_estimators': 300, 'max_depth': 4, 'learning_rate': 0.01711303178688522, 'subsample': 0.9864811375639159, 'colsample_bytree': 0.6925197245128625, 'gamma': 4.9912704007006825, 'reg_alpha': 4.996757238561539, 'reg_lambda': 0.06819396976339398}. Best is trial 13 with value: 0.6104597592640314.\n",
+      "[I 2025-05-06 18:33:33,110] Trial 14 finished with value: 0.6100689651653199 and parameters: {'n_estimators': 291, 'max_depth': 5, 'learning_rate': 0.018561534350235213, 'subsample': 0.8942695208581424, 'colsample_bytree': 0.8489996347754095, 'gamma': 3.8960308962391785, 'reg_alpha': 3.9867934437329335, 'reg_lambda': 0.15143508874843603}. Best is trial 13 with value: 0.6104597592640314.\n",
+      "[I 2025-05-06 18:33:33,918] Trial 15 finished with value: 0.6096779159235626 and parameters: {'n_estimators': 299, 'max_depth': 7, 'learning_rate': 0.019023406221746843, 'subsample': 0.8874847005443711, 'colsample_bytree': 0.8669985777451982, 'gamma': 3.807404515924418, 'reg_alpha': 3.831149402968605, 'reg_lambda': 0.005252750451126151}. Best is trial 13 with value: 0.6104597592640314.\n",
+      "[I 2025-05-06 18:33:34,737] Trial 16 finished with value: 0.6100688801176379 and parameters: {'n_estimators': 300, 'max_depth': 5, 'learning_rate': 0.017195793068107947, 'subsample': 0.8926755903859642, 'colsample_bytree': 0.8616002724362721, 'gamma': 3.862582872209292, 'reg_alpha': 3.8448810418336588, 'reg_lambda': 0.6155716297683851}. Best is trial 13 with value: 0.6104597592640314.\n",
+      "[I 2025-05-06 18:33:35,462] Trial 17 finished with value: 0.6103298064059615 and parameters: {'n_estimators': 275, 'max_depth': 5, 'learning_rate': 0.030169476425742112, 'subsample': 0.8363568258597748, 'colsample_bytree': 0.7031384628464791, 'gamma': 3.966750215055065, 'reg_alpha': 3.292329890977491, 'reg_lambda': 0.16642469675490582}. Best is trial 13 with value: 0.6104597592640314.\n",
+      "[I 2025-05-06 18:33:36,065] Trial 18 finished with value: 0.6099383319257943 and parameters: {'n_estimators': 225, 'max_depth': 7, 'learning_rate': 0.060599561437944906, 'subsample': 0.7954019515478872, 'colsample_bytree': 0.7124090809433227, 'gamma': 3.220510484701593, 'reg_alpha': 3.1504319197054724, 'reg_lambda': 1.0349195341916952}. Best is trial 13 with value: 0.6104597592640314.\n",
+      "[I 2025-05-06 18:33:36,770] Trial 19 finished with value: 0.6090251749643438 and parameters: {'n_estimators': 276, 'max_depth': 4, 'learning_rate': 0.02986581524648025, 'subsample': 0.827864870941472, 'colsample_bytree': 0.7940501165980121, 'gamma': 2.5652025972265524, 'reg_alpha': 2.2029842905005994, 'reg_lambda': 2.701559867068303}. Best is trial 13 with value: 0.6104597592640314.\n",
+      "[I 2025-05-06 18:33:37,347] Trial 20 finished with value: 0.609416819539875 and parameters: {'n_estimators': 225, 'max_depth': 6, 'learning_rate': 0.04743477721893867, 'subsample': 0.9325078300646163, 'colsample_bytree': 0.5022084528416604, 'gamma': 4.309327954514383, 'reg_alpha': 3.0505418398986, 'reg_lambda': 0.5926456739413668}. Best is trial 13 with value: 0.6104597592640314.\n",
+      "[I 2025-05-06 18:33:38,063] Trial 21 finished with value: 0.6095469424933089 and parameters: {'n_estimators': 277, 'max_depth': 5, 'learning_rate': 0.015610478268282109, 'subsample': 0.9323750487979924, 'colsample_bytree': 0.8802539491221789, 'gamma': 4.198223203783838, 'reg_alpha': 4.261219195075233, 'reg_lambda': 0.10079041102696851}. Best is trial 13 with value: 0.6104597592640314.\n",
+      "[I 2025-05-06 18:33:38,763] Trial 22 finished with value: 0.6111125002232501 and parameters: {'n_estimators': 279, 'max_depth': 5, 'learning_rate': 0.02664709916286723, 'subsample': 0.8289739303119256, 'colsample_bytree': 0.7018127103864195, 'gamma': 3.1750604403251375, 'reg_alpha': 3.630462169286953, 'reg_lambda': 0.016402220404694204}. Best is trial 22 with value: 0.6111125002232501.\n",
+      "[I 2025-05-06 18:33:39,452] Trial 23 finished with value: 0.6103300615490074 and parameters: {'n_estimators': 277, 'max_depth': 4, 'learning_rate': 0.02786848597777135, 'subsample': 0.8237764089952978, 'colsample_bytree': 0.7104678094988177, 'gamma': 3.1330868684031508, 'reg_alpha': 3.4094045207590242, 'reg_lambda': 0.9682980990576588}. Best is trial 22 with value: 0.6111125002232501.\n",
+      "[I 2025-05-06 18:33:40,074] Trial 24 finished with value: 0.610069135260684 and parameters: {'n_estimators': 231, 'max_depth': 4, 'learning_rate': 0.02421947174893141, 'subsample': 0.7842906400205539, 'colsample_bytree': 0.7263421486279461, 'gamma': 3.1567400139946065, 'reg_alpha': 3.5136152684312716, 'reg_lambda': 0.96592348359492}. Best is trial 22 with value: 0.6111125002232501.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best hyperparameters: {'n_estimators': 279, 'max_depth': 5, 'learning_rate': 0.02664709916286723, 'subsample': 0.8289739303119256, 'colsample_bytree': 0.7018127103864195, 'gamma': 3.1750604403251375, 'reg_alpha': 3.630462169286953, 'reg_lambda': 0.016402220404694204}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import optuna\n",
+    "from xgboost import XGBClassifier\n",
+    "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
+    "\n",
+    "# Encode target variable\n",
+    "label_encoder = LabelEncoder()\n",
+    "y_encoded = label_encoder.fit_transform(y)\n",
+    "\n",
+    "# Train-test split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.2, random_state=42, stratify=y_encoded)\n",
+    "\n",
+    "# Objective function for Optuna\n",
+    "def objective(trial):\n",
+    "    param = {\n",
+    "        'n_estimators': trial.suggest_int('n_estimators', 100, 300),\n",
+    "        'max_depth': trial.suggest_int('max_depth', 3, 10),\n",
+    "        'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),\n",
+    "        'subsample': trial.suggest_uniform('subsample', 0.5, 1.0),\n",
+    "        'colsample_bytree': trial.suggest_uniform('colsample_bytree', 0.5, 1.0),\n",
+    "        'gamma': trial.suggest_float('gamma', 0, 5),\n",
+    "        'reg_alpha': trial.suggest_float('reg_alpha', 0, 5),\n",
+    "        'reg_lambda': trial.suggest_float('reg_lambda', 0, 5),\n",
+    "        'use_label_encoder': False,\n",
+    "        'eval_metric': 'mlogloss'\n",
+    "    }\n",
+    "\n",
+    "    model = XGBClassifier(**param, random_state=42, verbosity=0)\n",
+    "\n",
+    "    # Cross-validation\n",
+    "    skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
+    "    scores = cross_val_score(model, X_train, y_train, cv=skf, scoring='accuracy')\n",
+    "\n",
+    "    return scores.mean()\n",
+    "\n",
+    "# Run the optimization\n",
+    "study = optuna.create_study(direction='maximize')\n",
+    "study.optimize(objective, n_trials=25)\n",
+    "\n",
+    "# Best hyperparameters\n",
+    "best_params = study.best_params\n",
+    "print(f\"Best hyperparameters: {best_params}\")\n",
+    "\n",
+    "# Final model training with best params\n",
+    "final_model = XGBClassifier(**best_params, random_state=42, verbosity=0)\n",
+    "final_model.fit(X_train, y_train)\n",
+    "\n",
+    "# Evaluation on test data\n",
+    "y_pred = final_model.predict(X_test)\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "precision = precision_score(y_test, y_pred, average='weighted')\n",
+    "recall = recall_score(y_test, y_pred, average='weighted')\n",
+    "f1 = f1_score(y_test, y_pred, average='weighted')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "396bd243-49a6-42a5-a08b-8f89fd490be2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best hyperparameters: {'n_estimators': 279, 'max_depth': 5, 'learning_rate': 0.02664709916286723, 'subsample': 0.8289739303119256, 'colsample_bytree': 0.7018127103864195, 'gamma': 3.1750604403251375, 'reg_alpha': 3.630462169286953, 'reg_lambda': 0.016402220404694204}\n",
+      "Final Model Accuracy: 0.6208\n",
+      "Precision: 0.6258\n",
+      "Recall: 0.6208\n",
+      "F1 Score: 0.6169\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Best hyperparameters\n",
+    "best_params = study.best_params\n",
+    "print(f\"Best hyperparameters: {best_params}\")\n",
+    "\n",
+    "# Print final metrics\n",
+    "print(f\"Final Model Accuracy: {accuracy:.4f}\")\n",
+    "print(f\"Precision: {precision:.4f}\")\n",
+    "print(f\"Recall: {recall:.4f}\")\n",
+    "print(f\"F1 Score: {f1:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "a785058b-74d2-4a24-aae8-ad260503b2b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification Report:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "       onset       0.63      0.51      0.56       958\n",
+      "      wakeup       0.59      0.70      0.64       959\n",
+      "\n",
+      "    accuracy                           0.60      1917\n",
+      "   macro avg       0.61      0.60      0.60      1917\n",
+      "weighted avg       0.61      0.60      0.60      1917\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "\n",
+    "# Classification report\n",
+    "report = classification_report(y_test, y_pred, target_names=label_encoder.classes_)\n",
+    "print(\"Classification Report:\\n\", report)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ffbe9eb7-740a-4340-aa3a-78a4f6ab7c5b",
+   "metadata": {},
+   "source": [
+    "#### ✅ Model Evaluation Conclusion:\n",
+    "\n",
+    "* All evaluation metrics (Accuracy: 0.6208, Precision: 0.6258, Recall: 0.6208, F1 Score: 0.6169) are fairly close, indicating a balanced and consistent model.\n",
+    "\n",
+    "* The XGBoost model is performing better than several baseline models previously tested.\n",
+    "\n",
+    "* The classification report also supports that the model is not heavily biased toward any particular class (onset vs wakeup).\n",
+    "\n",
+    "* No signs of overfitting, thanks to Stratified K-Fold cross-validation during Optuna hyperparameter tuning.\n",
+    "\n",
+    "#### Overall, the XGBoost classifier is well-tuned and generalizes reasonably well to unseen data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "a307d109-9245-4a18-8954-071a197711ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Model saved as 'xgboost_sleep_model.pkl'\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "from xgboost import XGBClassifier\n",
+    "\n",
+    "# Define best hyperparameters for XGBoost\n",
+    "best_params = {\n",
+    "    'n_estimators': 279,\n",
+    "    'max_depth': 5,\n",
+    "    'learning_rate': 0.02664709916286723,\n",
+    "    'subsample': 0.8289739303119256,\n",
+    "    'colsample_bytree': 0.7018127103864195,\n",
+    "    'gamma': 3.1750604403251375,\n",
+    "    'reg_alpha': 3.630462169286953,\n",
+    "    'reg_lambda': 0.016402220404694204,\n",
+    "    'use_label_encoder': False,\n",
+    "    'eval_metric': 'mlogloss'\n",
+    "}\n",
+    "\n",
+    "# Initialize the XGBoost model with the best parameters\n",
+    "final_model = XGBClassifier(\n",
+    "    **best_params,\n",
+    "    random_state=42,\n",
+    "    verbosity=0\n",
+    ")\n",
+    "\n",
+    "# Train the model on the training data\n",
+    "final_model.fit(X_train, y_train)\n",
+    "\n",
+    "# Save the trained model as a pickle file\n",
+    "model_filename = 'xgboost_sleep_model.pkl'\n",
+    "with open(model_filename, 'wb') as f:\n",
+    "    pickle.dump(final_model, f)\n",
+    "\n",
+    "print(f\"✅ Model saved as '{model_filename}'\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "c0d62804-0541-42ff-b80e-e2e6b21f07f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the saved model\n",
+    "with open('xgboost_sleep_model.pkl', 'rb') as f:\n",
+    "    loaded_model = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94b13e41-0d2b-4c68-b997-42d53e54f6f5",
+   "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}