RISK-ANALYSIS-OF-STROKE / Git / Diff of /STROKE

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 2.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 45px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color: #66689c;\n",
+    "  background-size: auto 200%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 200px;\n",
+    "  background-position: 0 100%;\">ĐỀ TÀI: DỰ ĐOÁN NGUY CƠ ĐỘT QUỴ SỬ DỤNG PHÂN TÍCH DỮ LIỆU LÂM SÀN</h1>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "%md\n",
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Nhóm 5:</strong>\n",
+    "<ul>\n",
+    "<li>\n",
+    "Lê Phúc Hậu - 21110879\n",
+    "</li>\n",
+    "<li>\n",
+    "Nguyễn Quang Vinh – 21110727\n",
+    "</li>\n",
+    "<li>\n",
+    "Nguyễn Thi Phú - 21110600\n",
+    "</li>\n",
+    "</ul>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "%md\n",
+    "\n",
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 42px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 120px;\n",
+    "  background-position: 0 100%;\"> 1. Giới Thiệu </h1>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 38px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 80px;\n",
+    "  background-position: 0 100%;\"> 1.1. Mô tả </h1>\n",
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">Dự án sẽ sử dụng các tập dữ liệu để dự đoán, phân tích khả năng mắc đột quỵ của các tình nguyện viên được khảo sát dựa trên các thông số đầu vào như giới tính, tuổi tác, chỉ số sinh hóa và tình trạng sức khỏe của cơ thể..</div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 38px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 80px;\n",
+    "  background-position: 0 100%;\"> 1.2.\tPhạm vi dự án </h1>\n",
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Địa điểm:</strong> Dự án có thể thực hiện ở bất kỳ địa điểm nào có sẵn dữ liệu lâm sàn về bệnh đột quỵ. Một số nơi như bệnh viện, trung tâm y tế hoặc các cơ sở chăm sóc sức khỏe khác là những nơi có dữ liệu lâm sàn về bệnh đột quỵ và có thể triển khai dự án.<br>\n",
+    "\n",
+    "<strong>Thời gian:</strong> Thu thập dữ liệu trong 1 – 2  năm và 2 tháng để phân tích dữ liệu<br>\n",
+    "<strong>Đối tượng:</strong> Những người đang điều trị và những người không mắc bệnh. Dựa trên các chỉ số lâm sàn của 2 nhóm này từ đó tìm ra những điểm khác biệt và mối liên hệ.<br>\n",
+    "<ul>\n",
+    "<li>P: Những người bị bệnh đột quỵ và những người không bị bệnh đột quỵ tại Việt Nam</li>\n",
+    "<li>A: Các đối tượng bị bệnh đột quỵ và những người không bị bệnh đột quỵ ở thành phố Hồ Chí Minh</li>\n",
+    "<li>S: Những bệnh nhân đang điều trị bệnh đột quỵ hoặc theo dõi sức khỏe tại các bệnh viện ở thành phố Hồ Chí Minh</li>\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 38px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 80px;\n",
+    "  background-position: 0 100%;\"> 1.3.\tInstruments </h1>\n",
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "\n",
+    "- Khảo sát trực tiếp:\n",
+    "<ul>\n",
+    "<li>\n",
+    "Bộ câu hỏi về các triệu chứng<br>\n",
+    "Yêu cầu: Câu hỏi dạng có hoặc không (Yes-No Question)\n",
+    "</li>\n",
+    "<li>Hồ sơ bệnh án <br>\n",
+    "Yêu cầu: Cần có các mục cơ bản: họ tên, địa chỉ, giới tính, SĐT, tiền sử bệnh lý, danh sách các thuốc đang sử dụng (nếu có), kết quả đo lường về huyết áp, đường huyết, nồng độ cồn, lipid, cân nặng, chiều cao,...\n",
+    "</li>\n",
+    "<li>\n",
+    "Cuff và Sphygmomanometer <br>\n",
+    "Yêu cầu: Cuff bao phủ ít nhất 80% chiều dài của cánh tay và phải bao phủ toàn bộ vùng xung quanh.<br>\n",
+    "Thương hiệu: Omron\n",
+    "</li>\n",
+    "<li>\n",
+    "Ống nghe và đồng hồ đo nhịp tim (Stethoscope - Watch)<br>\n",
+    "Yêu cầu: Ống nghe có chất lượng cao, với độ dẻo và khả năng truyền âm tốt.<br>\n",
+    "Thương hiệu: Littmann 3M\n",
+    "</li>\n",
+    "<li>\n",
+    "Thiết bị đo SPO2 (Pulse Oximeter)<br>\n",
+    "Yêu cầu: Không<br>\n",
+    "Thương hiệu: Omron\n",
+    "</li>\n",
+    "<li>Đường kế (Glucometer)<br>\n",
+    "Yêu cầu: Không<br>\n",
+    "Thương hiệu: Accu-Check - Roche\n",
+    "</li>\n",
+    "<li>Thước dây dùng để đo chiều cao<br>\n",
+    "Yêu cầu: Độ chia nhỏ nhất của thước là 1mm<br>\n",
+    "Thương hiệu: Không yêu cầu\n",
+    "</li>\n",
+    "<li>Cân điện tử<br>\n",
+    "Yêu cầu: Thiết bị đạt độ chính xác cấp III theo tiêu chuẩn OIML. Bên cạnh đó giới hạn đo phải đạt tới 300kg.\n",
+    "<br>\n",
+    "Thương hiệu: Cân bàn điện tử JWI 700W Jadever\n",
+    "</li>\n",
+    "</ul>\n",
+    "- Khảo sát trực tuyến (online): Dùng Google Form (<a style=\"color: white\"href=\"https://docs.google.com\">https://docs.google.com</a>) để khảo sát\n",
+    "\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 38px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 80px;\n",
+    "  background-position: 0 100%;\">1.4.\tProtocols </h1>\n",
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "\n",
+    "- Đo huyết áp\n",
+    "<ul>\n",
+    "<li>\n",
+    "Bước 1: Người bệnh ngồi yên, lưng thẳng, chân không chạm đất. Tay được đặt trên bàn ở tư thế thoải mái.\n",
+    "</li>\n",
+    "<li>Bước 2: Người đo sẽ đeo cái cuff quanh cánh tay trên của người bệnh, đảm bảo cuff bao phủ ít nhất 80% chiều dài cánh tay.\n",
+    "</li>\n",
+    "<li>\n",
+    "Bước 3: Bóp nút xả hơi trên bóp huyết áp để xả hết không khí trong cuff.\n",
+    "</li>\n",
+    "<li>\n",
+    "Bước 4: Bật nút bơm để bơm hơi dần vào cuff cho đến khi cuff bắt đầu chèn ép động mạch cánh tay.\n",
+    "</li>\n",
+    "<li>\n",
+    "Bước 5: Nghe âm thanh tiếng máu chảy qua động mạch bằng ống nghe và đọc chỉ số huyết áp cao nhất.\n",
+    "</li>\n",
+    "<liBước 6: Nút xả hơi cho hơi trong cuff thoát ra từ từ cho đến khi không còn nghe âm thanh và đọc chỉ số huyết áp thấp nhất.\n",
+    "</li>\n",
+    "<li>Bước 7: Ghi lại kết quả 2 lần đo và trung bình chúng\n",
+    "</li>\n",
+    "</ul>\n",
+    "- Đối với đo nhịp tim:\n",
+    "<ul>\n",
+    "<li>\n",
+    "Bước 1: Người bệnh nằm ngửa, chân duỗi thẳng.\n",
+    "</li>\n",
+    "<li>Bước 2: Người đo đặt ống nghe lên vị trí tim, và định vị được âm thanh truyền nhất.\n",
+    "</li>\n",
+    "<li>\n",
+    "Bước 3: Người đo nhìn vào đồng hồ đo nhịp và đếm số nhịp trong 15 giây.\n",
+    "</li>\n",
+    "<li>\n",
+    "Bước 4: Nhân kết quả đếm nhịp với 4 để tính toán nhịp tim trong 1 phút (nhịp/phút).\n",
+    "</li>\n",
+    "<li>\n",
+    "Bước 5: Đo 2 lần và tính trung bình các kết quả.\n",
+    "</li>\n",
+    "</ul>\n",
+    "- Đối với đo đường huyết\n",
+    "<ul>\n",
+    "<li>\n",
+    "Bước 1: Lấy máy đo đường huyết và que thử.\n",
+    "</li>\n",
+    "<li>\n",
+    "Bước 2: Lấy một giọt máu từ ngón tay cái bằng lấy máu\n",
+    "</li>\n",
+    "<li>\n",
+    "Bước 3: Đặt máu lên đầu que thử.\n",
+    "</li>\n",
+    "<li>\n",
+    "Bước 4: Bật máy và đọc kết quả trên màn hình.\n",
+    "</li>\n",
+    "<li>\n",
+    "Bước 5: Ghi lại kết quả. \n",
+    "</li>\n",
+    "</ul>\n",
+    "- Đối với đo chiều cao: Sử dụng thước dây có độ chia nhỏ nhất 1mm, đặt thước dây lên đỉnh đầu và đọc kết quả tại điểm gặp nhau của đầu đo và đầu thước.<br>\n",
+    "- Đối với việc đo cân nặng: Sử dụng cân điện tử JWI 700W Jadever, yêu cầu người được đo đứng trên cân và đọc kết quả trên màn hình hiển thị.<br>\n",
+    "- Đối với việc đo nồng độ cồn: Sử dụng thiết bị đo nồng độ cồn Alcohol Tester AT6000, người được kiểm tra thở vào ống hút hoặc cảm biến và đọc kết quả trên màn hình hiển thị.<br>\n",
+    "\n",
+    "\n",
+    "</div>\n",
+    "<div class=\"symple-box yellow center \" style=\"background: #fffdf3; border-radius: 2px; border: 1px solid #f2dfa4; box-sizing: border-box; color: #c4690e; float: none; font-family: Arial, sans-serif; font-size: 22px; margin: 0px auto; outline: 0px; padding: 15px 20px; vertical-align: baseline;\"><strong style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Lưu ý</strong>:\n",
+    "<ul style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px 0px 20px 20px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Phải đảm bảo vệ sinh đối với các thiết bị đo và cần được thay, vệ sinh thiết bị để đảm bảo vệ sinh, độ chính xác, phải cài đặt lại thiết bị sau mỗi lần sử dụng.</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Thực hiện đo, sử dụng thiết bị theo hướng dẫn của nhà sản xuất và tuân thủ các quy định về an toàn và sử dụng thiết bị.</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Tất cả những người thực hiện đo các thông sô trên đều phải được đào tạo và có chứng chỉ y tế theo đúng quy định của pháp luật.</li>\n",
+    "</ul>\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 38px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 80px;\n",
+    "  background-position: 0 100%;\">1.5.\tCác yếu tố ảnh hưởng đến sự hữu ích của dữ liệu</h1>\n",
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "\n",
+    "- Dữ liệu bị thiếu:\n",
+    "<ul>\n",
+    "<li>\n",
+    "Người được khảo sát ngại trả lời các câu hỏi về tiền sử bệnh, tình trạng hôn nhân, các chỉ số cơ thể,...\n",
+    "</li>\n",
+    "<li>\n",
+    "Người được khảo sát thiếu kiên nhẫn khi thực hiện quá nhiều bước đo để lấy các thông tin về chỉ số sinh hóa.\n",
+    "</li>\n",
+    "<li>\n",
+    "Người khảo sát không rõ về tiền sử bệnh của người thân.\n",
+    "</li>\n",
+    "\n",
+    "</ul>\n",
+    "- Dữ liệu không đúng đắn\n",
+    "<ul>\n",
+    "<li>\n",
+    "Khi khảo sát online qua form người khảo sát có thể điền các dữ liệu không được đo một cách chính xác, hoặc cố tình điền sai\n",
+    "</li>\n",
+    "<li>\n",
+    "Người được khảo sát cố tình trả lời sai để che giấu các thông tin cá nhân quan trọng như SĐT, địa chỉ nhà, công việc hiện tại,...\n",
+    "</li>\n",
+    "<li>\n",
+    "Người khảo sát không muốn người khác biết bản thân đang stress, hay mắc các bệnh trong người\n",
+    "</li>\n",
+    "</ul>\n",
+    "- Dữ liệu mang tính chủ quan: Tình nguyên viên được khảo sát chỉ dựa trên quan điểm cá nhân, chứ không có bất kì đánh giá khách quan nào của chuyên gia để trả lời các câu hỏi sức khỏe.<br>\n",
+    "- Câu hỏi mang tính chất hàn lâm, thuộc lĩnh vực Y tế: Nếu trả lời qua form khảo sát trực tuyến, người được khảo sát sẽ bối rối, lúng túng khi gặp các câu hỏi về chỉ số đường huyết, huyết áp, nhịp tim,…\n",
+    "\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 42px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 120px;\n",
+    "  background-position: 0 100%;\"> 2. Tổng quan bộ dữ liệu </h1>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "  <div style=\"width: 100%; height: 100%; background: #66689c;color: white; position:relative; padding:20px; font-size: 25px;\">\n",
+    "        <div style=\"display: flex; flex-direction: column;  margin-left:20px;\">\n",
+    "        <p style=\"font-size:  30px;font-weight: 800; text-align: center; margin-bottom:20px;\">Tên bộ dữ liệu: BRAIN STROKE DATASET</p>\n",
+    "        <div style=\"display: flex; flex-direction: column;  margin-left:20px; gap:5px\">\n",
+    "          <strong>Tác giả : Jillani Soft Tech</strong>\n",
+    "          <strong>Tên thật: Muhammad Ghulam Jillani</strong>\n",
+    "          <strong>\n",
+    "            Hiện đang là: Data Scientist và Machine <br>Learning Engineer ở Blocbelt\n",
+    "            Lahore, <br>Punjab, Pakistan\n",
+    "          </strong>\n",
+    "          <ul>\n",
+    "            <li>Top 100 Global Kaggle Master</li>\n",
+    "            <li>Lãnh đạo và Cố vấn nổi bật trong \n",
+    "            <br>Chương trình KaggleX BIPOC</li>\n",
+    "            <li>Có nhiều đóng góp quan trọng cho <br>nhóm Google Developer</li>\n",
+    "          </ul>\n",
+    "        </div>\n",
+    "      </div>\n",
+    "      <img\n",
+    "      style =\"position:absolute; bottom:0; right:0; background: #82cbff; width : 360px; height : 360px\"\n",
+    "        src=\"https://media.licdn.com/dms/image/D4D03AQEnHShp_sDHtw/profile-displayphoto-shrink_800_800/0/1699010356404?e=1720051200&v=beta&t=28adnzOaIGtSKC80XaPdSUZdhjpYFc6JA0Kit3sffh8\"\n",
+    "        alt=\"\"\n",
+    "      />\n",
+    "    </div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<div class=\"my-paragraph\" style=\"font-size: 22px;\">\n",
+    " \n",
+    " <div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    " <strong>Link</strong> <a style=\"color: white\"href=\"https://www.kaggle.com/datasets/jillanisofttech/brain-stroke-dataset\">https://www.kaggle.com/datasets/jillanisofttech/brain-stroke-dataset</a><br>\n",
+    "  Gồm 4.981 dòng dữ liệu, 11 trường dữ liệu và không có dữ liệu trống :<br>\n",
+    "  1. Gender: Giới tính  <strong>( Male: nam (2074 - 58.36%), Female: nữ (2907 - 41.64%) </strong> <br>\n",
+    "  2. Age: Tuổi <br>\n",
+    "  3. Hypertension: Cao huyết áp <strong>( 0: không bị cao huyết áp (4502 - 90.38%), 1: có cao huyết áp (479- 9.62%) )</strong><br>\n",
+    "  4. Heart_disease: Bệnh tim  <strong> (0: không bị bệnh tim (4706- 94.48%), 1: có bị bệnh tim (275- 5.52%)) </strong> <br>\n",
+    "  5. Ever_married: Đã lập gia đình chưa  <strong>(No: chưa có gia đình (1701 - 34.15%), Yes: đã có gia đình (3280 - 65.85%)) </strong><br>\n",
+    "  6. Work_type: Loại công việc  <strong> (Self-employed: nghề tự do (804 - 16.14%), Private: không tiết lộ (2860 - 57.42%), Government Job: cán bộ Nhà nước (644 - 12.93%), Children: trẻ em (673 - 13.51%)) </strong><br>\n",
+    "  7. Residence_type: Nơi ở  <strong>(Urban: thành thị (2532 - 50.83%) hoặc Rural: nông thôn (2449 - 49.17%)) </strong><br>\n",
+    "  8. Avg_glucose_level: Chỉ số đường huyết trung bình (mg/dL)<br>\n",
+    "  9. Bmi: Chỉ số thể trọng   = cân nặng/chiều cao ^2   ( kq/m^2 ) <br>\n",
+    "  10. Smoking_status: Tình trạng hút thuốc  <strong> (smokes: hút gần đây (776 - 15.58%), formerly smoked: từng hút (867 - 17.41%), never smoked: chưa từng hút (1838 - 36.9%), Unknown: không biết hoặc bất khả dụng (1500 - 30.11%)) </strong><br>\n",
+    "  11. Stroke: Đột quỵ  <strong>(1: đột quỵ (248 - 95.02%), 0: không bị đột quỵ (4733 - 4.98%)) </strong><br></div>\n",
+    " \n",
+    "  \n",
+    "  \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from pylab import *\n",
+    "from scipy import stats\n",
+    "import umap\n",
+    "import networkx as nx\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.preprocessing import OrdinalEncoder\n",
+    "from imblearn.over_sampling import SMOTE\n",
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "from xgboost import XGBClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.ensemble import StackingClassifier\n",
+    "df = pd.read_csv(\"https://drive.google.com/uc?export=download&id=1QMtnX4HTvhTWFBOBfjVG7aWOke_mBZ1M\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def draw_box(data, col, title):\n",
+    "    fig = plt.figure(figsize=(15,3), dpi=80)\n",
+    "    plt.title(title, fontsize=16)\n",
+    "    plt.xticks(fontsize=15)\n",
+    "    plt.yticks(fontsize=15)\n",
+    "    \n",
+    "    bp_dict1 = boxplot(data[col], vert=False, widths = 0.5)\n",
+    "    \n",
+    "    for line in bp_dict1['medians']:\n",
+    "        # get position data for median line\n",
+    "        x, y = line.get_xydata()[1] # top of median line\n",
+    "        # overlay median value\n",
+    "        text(x, y + 0.05, '%.1f' % x,\n",
+    "             horizontalalignment='center', fontsize=15)\n",
+    "        \n",
+    "    for line in bp_dict1['boxes']:\n",
+    "        x, y = line.get_xydata()[0] # bottom of left line\n",
+    "        text(x,y - 0.05, '%.1f' % x,\n",
+    "             horizontalalignment='center', # centered\n",
+    "             verticalalignment='top', fontsize=15)      # below\n",
+    "        x, y = line.get_xydata()[3] # bottom of right line\n",
+    "        text(x,y - 0.05, '%.1f' % x,\n",
+    "             horizontalalignment='center', # centered\n",
+    "                 verticalalignment='top', fontsize=15)\n",
+    "    plt.xlabel(col, fontsize=16)\n",
+    "    plt.xlim([df[col].min() - 1, df[col].max() + 1])\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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5u7vb9D169Kh8fX3veHZYkvVBVOHh4bnWknUWNKezarefRT106JB+//13TZs2LdtThG/9qJ7c/J2zyGXLls1TnVln8Y8ePZqtb1bb/ZztrFChgtzc3HTs2LFs23Jqu9M+SpcurcjIyDvWdqvc5ufkyZP39VFjd6rL09NTiYmJeXrT6da6bv1YpNvn4n6lpaVJUq6ff+zs7Kzg4GAtWLDAegk1AKBgcA8xAJRAWWcFbz0DmJSUpE8++aRAx50zZ471P/zSXx8PtGHDBnXo0MF6dvSZZ55Renq6PvzwQ5vXLlu2TMePH1fPnj1zHaNhw4YKDAzUl19+qaioqGzbs47Z3d1dFStW1ObNm22279q1Szt27LBpy/p53X7mfMuWLdq1a1eu9WTJemrw/VzWWrNmTe3YscN6n7X018f8/PDDDzb9vL291bJlS61cudImqGZmZuq9996TpLv+/HJib2+vLl26aPv27dmO96OPPsrzPrp166adO3dq69at1nbDMPT+++9n61+lShU5ODhkO/seGhqqs2fP3vMx5FbX888/r23btmn16tXZthuGocuXL1u/r1mzpiTZ1GUYhubOnZsv9axYsUKS7vqZ4GPHjtW0adP0r3/9677viwcA3B1niAGgBOrVq5cmT56sp556Sr169dK1a9cUEhKS7YxsfktOTlabNm0UHBysq1ev6rPPPpOzs7NmzZpl7TNkyBCFhIRo+vTp+vPPP/XYY48pMjJSn3/+uSpXrqxp06blOobFYtHXX3+t9u3bq0mTJtaPXbpy5YrWr1+vsWPHqkePHpKkkSNH6o033lC3bt3UrVs3RUdH68svv1T9+vV18OBB6z4DAgIUGBio999/X0lJSapVq5YOHTqkkJAQ1a1bV4cOHbrrsZcrV07Vq1fXd999pxo1algfkvbkk0/e9bUjR47UoEGD1L59e/Xv31+XL1/WvHnzFBgYqH379tn0nTt3rtq0aaPHHntMI0aMsH7s0ubNmzVixIi7Bq07mTFjhtatW6cOHTpo1KhR8vX11Zo1a6xhMS9nwGfMmKGff/5ZXbp00ejRo+Xr66uwsDCbwJnF3d1dgwYN0tdff62BAweqVatWOnz4sEJDQ1W9evU7PnTqfsycOVO//fabevTooX79+unRRx+VxWLRyZMntWrVKg0YMMB6ZUC/fv00adIkDRs2TBEREfL09NSKFSts3qzIq59++kmHDx+WJMXFxWnr1q0KDQ2Vn59ftofj3S4gIOBvfeYxACBvCMQAUAK9+uqrysjI0MKFCzVmzBj5+/tr6NChevTRRwv08stvv/1W8+bN0zvvvKOEhAQ98sgj+vDDD20+R7ZUqVJav369pk+frtDQUIWGhqps2bIaOHCgZsyYkaenMDdv3ly7d+/W9OnTtXjxYsXFxcnb21uPP/64zVivvfaarl27piVLlmjjxo1q2LChvv/+e3399dc2gdjBwUFr1qzRhAkT9PXXXyslJUWNGzdWWFiYFi5cmKdALP31ROBx48Zp4sSJ1jcH8hKIBwwYoDNnzmjevHkaN26cAgMD9fnnn+v333/PFoibNm2qbdu26Y033tCnn36qmzdvqmbNmpo7d65Gjx6dpzpzUrt2bf3666965ZVXNGfOHDk7O6tbt27617/+pWrVquXpac/Vq1fX1q1bNWHCBH3yySdycnJSly5dtGjRomyfySxJs2fPVkZGhlatWqXvv/9eLVu21MaNGzVq1ChFR0ff97HczsvLS9u3b9cHH3ygZcuWafny5XJycpK/v7+eeuop9e3b19rX09NTYWFhGj9+vN5++22VKVNG/fv3t77xci8mT55s/bODg4N8fX31P//zP3rjjTfk7e2db8cHALh/FuNBfVoKAAAocvv27VPTpk317rvv6rXXXivqcgAAyFfclAIAACQp22XBt97/W1Ie7PTmm2/KYrGobdu2RV0KAOABwCXTAABAklS3bl117NhR9erV040bN/Tjjz/qt99+U3Bw8H3fmwwAwIOMS6YBAIAkacKECVqzZo1iYmKUnp6uatWqqX///po4caJKlSpV1OXli/T0dCUmJsrV1TXXj/cCAJgDgRgAAAAAYErcQwwAAAAAMCUCMQAAAADAlArtoVpOTk6qUKFCYQ0HAAAAADC5y5cvKyUl5Y7bCy0QV6hQQTExMYU1HAAAAADA5Pz8/HLdziXTAAAAAABTIhADAAAAAEyJQAwAAAAAMCUCMQAAAADAlAjEAAAAAABTIhADAAAAAEyJQAwAAAAAMCUCMQAAAADAlAjEAAAAAABTIhADAAAAAEyJQAwAAAAAMCUCMQAAAADAlAjEAAAAAABTIhADAAAAAEyJQAwAAAAAMCUCMQAAAADAlAjEAAAAAABTIhADAAAAAEyJQAwAAAAAMCUCMQAAAADAlAjEAAAAAABTIhADAAAAAEyJQAwAsLFnzx6NGjVKQUFBcnNzU+XKldW3b19FRUVl6xsaGqrmzZvLy8tL5cqVU5s2bbRmzZo8jbN06VINGDBANWvWlMViUdu2bfP5SAAAAHJHIAYA2Jg1a5ZWrFihdu3aae7cuRo+fLh+/fVXNW7cWIcPH7b2+/TTTxUcHKzy5cvrvffe09SpUxUXF6du3brp+++/v+s4n3/+uVatWiV/f3+VKVOmIA8JAAAgRxbDMIzCGMjPz08xMTGFMRQA4G/Yvn27mjZtKkdHR2vb8ePHVa9ePfXu3VuLFy+WJNWqVUteXl7atWuXLBaLJCk+Pl6+vr568skntWrVqlzHOXPmjHx9fWVnZ6e6deuqfPny2rJlS4EdFwAAMJ+75VDOEAMAbLRs2dImDEtSzZo1FRQUpIiICGtbfHy8vL29rWFYkjw8PFS6dGm5uLjcdRx/f3/Z2fHPEAAAKDr8TwQAcFeGYejixYsqX768ta1t27Zau3atPv30U0VHRysyMlIjR45UXFycXn755SKsFgAAIG8ciroAAMCDb8mSJTp79qymT59ubfvkk0905coVjRkzRmPGjJEklS9fXhs3blSLFi2KqlQAAIA8IxADAHKVdea3RYsWGjx4sLXd1dVVtWvXlp+fn7p166aEhATNnj1bvXr10tatW1WjRo0irBoAAODuCMQAgDu6cOGCunbtKk9PTy1fvlz29vbWbX369JGDg4PCwsKsbT169FDNmjU1efJkLV26tChKBgAAyDPuIQYA5CguLk5dunRRbGys1q5dKx8fH+u2EydOaO3atXr66adtXlO2bFm1atVK27ZtK+xyAQAA7hlniAEA2SQnJ6t79+6KiorSL7/8ojp16thsv3jxoiQpIyMj22vT0tKUnp5eKHUCAAD8HZwhBgDYyMjIUHBwsHbs2KFly5bl+ICsGjVqyM7OTkuXLtWtH2cfExOjrVu3qlGjRta2tLQ0RUZG6vz584VSPwAAQF5xhhgAYGPChAn68ccf1b17d127dk2LFy+22T5gwABVqFBB//jHP/Tll1+qXbt26tWrlxISEjRv3jwlJSXp9ddft/Y/e/asAgMDNXjwYIWEhFjbf/31V/3666+SpMuXL+vGjRuaMWOGJKl169Zq3bp1wR8sAAAwNQIxAMBGeHi4JCksLMzmgVlZBgwYIEn6/PPP1aBBA3311VfWAPzII4/o22+/zVOY3bRpk9566y2btqlTp0qSpk2bRiAGAAAFzmLceq1bAfLz81NMTExhDAUAAAAAwF1zKPcQAwAAAABMiUAMAAAAADAlAjEAAAAAwJQIxAAAAAAAUyIQAwAAAABMiUAMAAAAADAlAjEAAAAAwJQciroAALiT5ORkpaamFnUZgCk5OjrK2dm5qMsAAKBAEYgBPJCSk5NVpkwZJScnF3UpuEeO9tLrrZz07m8pSs0o6mpwv5ydnXX9+nVCMQCgRCMQA3ggpaamKjk5WWfOnJGHh0dRl4N7kZIgj3/V0fhlf0pO7kVdDe5DfHy8/P39lZqaSiAGAJRoBGIADzQPDw8CcXHz/0/qe7i7S87MHQAAeHDxUC0AAAAAgCkRiAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCkRiAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCkRiAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCkRiAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCkRiAEAAAAApkQgBgAAAACYUokKxIZhKD4+XoZhFHUpAAAAAEyGPFL8lKhAnJCQIE9PTyUkJBR1KQAAAABMhjxS/JSoQAwAAAAAQF4RiAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCkRiAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCkRiAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCkRiAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCnlORAnJiZq2rRp6ty5s8qWLSuLxaKQkJACLA0AAAAAgILjkNeOV65c0fTp01W5cmU1aNBAW7ZsKcCyAAAAAKB4GjJkiI4cOaIGDRqoVatWCg8P140bN+Tm5qYmTZqoXr16+vPPP5WUlCQXFxdVr15dhw4d0v79++Xp6am6deuqVq1aeuyxxyRJ27Zt0x9//KFq1arp0KFDOnDggBo3bqwXX3xRFovFur1GjRp67LHHZBiG5s+fr3379ln3V716da1atUq7d+/WzZs35ebmpkceeUQffPCBLBaL5s+fr/3796tRo0aqV6+eTpw4oerVq0uS/vjjD2utNWrUkCQdP35chw8fVlxcnBo3bqx69erpjz/+sLY1adIkW31Z9e/bt0/R0dFKTU1Vs2bN9MEHH8je3r5I5spiGIaRl44pKSm6fv26KlWqpL179+qRRx7RwoULNWTIkDwN5Ofnp5iYmL9T613Fx8fL09NTcXFx8vDwKNCxABQs1nMxlhwvvecvvXZGcmbuiiPWHwDcn9mzZ2v8+PF56uvi4qLk5GQ5OzsrKSlJkmRnZ6fMzEzZ2dmpdOnS8vPzkyRduHBBVapU0f79+yVJnp6eio+Pl5OTk/z8/HTt2jU1bNhQ4eHhKleunE6fPq3U1FTZ2dkpIyMj1zosFotKlSqltLQ0ubu7Kz4+XpJUt25dRUREyDAMa40uLi5KSUmRYRjWr6yab2Vvb6/MzEw5OjqqcuXKunr1qk39OdWwbNkyPfvss3n62d2Lu+XQPF8y7eTkpEqVKuVLUQAAAABQkqSnp1vD8MGDB+Xi4qIqVapYt0dGRsrJyUl2dnZydHRUamqqLl68qNTUVDk5OcnLy0tlypRRTEyMatWqpdTUVF24cEEXL17U6dOnFRERoVq1amngwIGqVq2aLl26JMMwdPLkSZ05c0YbN27U6dOndeLECUlSUFCQ/vGPf+j06dM2ddrZ2VnPHEuSYRhKTU3VpUuXVK1aNQ0cOFC1atXSkSNH1L9/f3l5ecnFxUWXL1+Wi4uL3N3dZbFY5OjoqJiYGJUpU0aenp6yWCyyWCyKiYnRP/7xDwUFBUmSTpw4oVOnTlnrz6rhyJEjCggIkLOzs0qXLq0+ffrcNbwXhDxfMl0cZJ3sznpXA0DxlbWO83gRC4B8xL+nAHDvbr1ydv369UpOTtaIESM0ceJESdJLL70kNzc3Xbt2TQ4ODkpNTdWoUaOUkZEhV1dXZWRkyM7OTidPntS2bdtUrlw52dvby9HRUVOnTlVycrJ27dplPSscGhoqNzc3Xb9+XSEhIRoxYoS++eYb6/5Onz6tPXv2aNKkSTZ1uru76+bNmzpy5Ij1DLQkjRw5UmfOnNGOHTu0adMmde3a1Rp0MzMztXTpUhmGoZSUFGVkZMjDw0OrVq2SJKWmpsowDFksFq1atUpz585VxYoV5eTkpJSUFL3wwgtKTk5Wu3btFBUVJQ8PD127dk3btm1T+fLlNXXqVE2ePFn/+7//q48//rjgJ+sWBRaIP/74Y5uDSUxMLKihrBISEiRJ/v7+BT4WgMKRkJBgfQcTQOHg31MA+Hv27t0rLy8vHTt2TGXKlFFsbKyOHj2qhg0baseOHfL09JSjo6PCw8NVpkwZVaxYURaLRZUqVdIff/yhVq1aycXFRR4eHgoICLDuz8vLS5LUoEED7d+/Xw0bNtT+/futlyLv37/fZn/Ozs7au3evHBwcZBiGMjMz1bhxY+sYFotFdnZ2slgsCg8PV4MGDeTs7KxLly7JwcHB2mYYhnW8o0ePymKxWMdu0KCBtc3R0VH79+/XiBEj5Ovra91/eHi4vLy8dPjwYTk4OKhRo0bWGjw9PXXixAk5ODho7969hT5XBRaIx48fb3P9/K3vPhQUd3d3SdKZM2e45wko5uLj4+Xv729d1wAKD/+eAsC9GzJkiFauXClJatq0qUJDQ1W7dm1dv35dklSnTh0dOHBASUlJslgsunnzpjp27KjQ0FClp6dLki5evKgaNWro2rVr1n7h4eEaMmSIfvvtN8XGxsrJyUkHDx5U79699f3331sfaiVJjRs31ldffWXdX3Jyspo2baqtW7da69y/f7+mT5+us2fPyjAM62XKDRs21C+//KLk5GR5e3srPT1dDRs21JIlS2QYhnW85ORkJSUlKTw8XL1799bKlSuVlJRk/WrcuLGSkpJ09uxZSX+9ydqxY0ctW7ZMdevW1datW3XgwAFNnz5d165dU1xcnKpVq6b09HQ1bdq0cCbrFnl+qNateKgWgILGei7GeKhWscf6A4B7l56erlKlSkmS9u3bp1atWqlixYqKjo6WJEVERKhhw4ZKS0uTg4ODMjIydO7cOfn4+MjBwUEuLi6yt7dXeHi4OnTooOjoaLm6uspisSg6Olre3t7y9/fXo48+qsOHD2v9+vXy8/NTenq64uLi5Obmphs3bsjT01MODg6qUaOGWrZsqSlTpujhhx+21mlnZ6edO3eqQ4cOiouLs7ZfvHhRnTp1Ur169bRr1y4dP35cAwcO1OrVq2WxWBQZGamAgABlZGQoISFBDg4OOn78uBo1aqT09HTrbTanTp3S22+/rR07duj48eNKT0/X9evXVbFiRfn7+ysqKsp61rhv376Kjo5WqVKllJiYqLS0tHx/2vTdcmiJuocYAAAAAIqCg4ODPv74Y40fP15NmjSRJGsYlqTAwEDrn1NTU+Xi4qJKlSpZn+CckpIiOzs7+fv7W58y/dBDD8kwDD388MMKDAzU/v37rffgent7y8nJSdWqVbN+NO7BgwdVvXp1nTp1SkePHlVkZKS++OILmzozMzPVrFkz6/dZT5muVKmS3N3dFR4eLumvp0xnnR12dnaWt7e3XF1dlZycbH0QV+XKlbM9Zbpy5co2T5muVq2aqlSpYq0/q4b69etbX5OSkqIVK1YUyUcvEYgBAAAAIB+88MILGj9+vHr27MnnEJe0zyG+FZdMAyhorOdijEumiz3WHwDcH/7+fPDk6yXTn332mWJjY3Xu3DlJUlhYmHXno0eP5kmwAAAAAIBi454C8YcffqhTp05Zv//+++/1/fffS5IGDBhAIAYAAAAAFBv3FIhvvSkcAAAAAIDizK6oCwAAAAAAoCgQiAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCkRiAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCkRiAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCkRiAEAAAAApkQgBgAAAACYUokKxO7u7oqLi5O7u3tRlwIAAADAZMgjxY9DUReQnywWizw8PIq6DAAAAAAmRB4pfkrUGWIAAAAAAPKKQAwAAAAAMCUCMQAAAADAlAjEAAAAAABTIhADAAAAAEyJQAwAAAAAMCUCMQAAAADAlAjEAAAAAABTIhADAAAAAEyJQAwAAAAAMCUCMQAAAADAlAjEAAAAAABTIhADAAAAAEyJQAwAAAAAMCUCMQAAAADAlAjEAAAAAABTIhADAAAAAEyJQAwAAAAAMCUCMQAAAADAlAjEAAAAAABTcijqAgAgJ4ZhSJLi4+OLuBLcs5QEeUiKT0iQUou6GNwP1h0AwCwIxAAeSAkJCZIkf3//Iq4E98rRXnq9lZPeneGn1Iyirgb3y9nZWY6OjkVdBgAABYpADOCB5OPjozNnzsjd3V0Wi6Woy8F9GF/UBeBvcXR0lLOzc1GXAQBAgSIQA3gg2dnZyc/Pr6jLAAAAQAnGQ7UAAAAAAKZEIAYAAAAAmBKBGAAAAABgSgRiAAAAAIApEYgBAAAAAKZEIAYAAAAAmBKBGAAAAABgSgRiAAViz549GjVqlIKCguTm5qbKlSurb9++ioqKsun3xRdfqE2bNqpYsaKcnJxUtWpVDR06VNHR0XcdIzo6WhaL5Y5f//znPwvo6IAH25EjR9SnTx9Vq1ZNrq6uKl++vFq3bq2wsDBrn8zMTIWEhOjpp5+Wv7+/3NzcVLduXc2YMUPJycl5His1NVUzZ85UQECAnJ2dVbFiRXXt2lUxMTEFcWgAAOQrh6IuAEDJNGvWLG3btk19+vRR/fr1deHCBX322Wdq3Lixdu7cqbp160qSDhw4oKpVq+rpp59WmTJldPLkSX3xxRdavXq1Dh48KB8fnzuOUaFCBS1atChb+9q1a7VkyRJ17NixwI4PeJCdOnVKCQkJGjx4sHx8fHTz5k2tWLFCTz/9tBYsWKDhw4fr5s2bGjp0qJo3b64XX3xR3t7e2rFjh6ZNm6aNGzdq06ZNslgsuY6Tlpamrl27avv27frnP/+p+vXr6/r169q1a5fi4uLk5+dXSEcMAMD9sRiGYRTGQH5+frxbDJjI9u3b1bRpUzk6Olrbjh8/rnr16ql3795avHjxHV+7b98+NW3aVO+++65ee+21ex67ffv22rNnjy5evChnZ+f7qh8oaTIyMtSkSRMlJycrMjJSqamp2rt3r1q2bGnTb/r06Zo2bZo2bNig9u3b57rP999/X1OmTNFvv/2mZs2aFWT5AADcl7vlUC6ZBlAgWrZsaROGJalmzZoKCgpSRERErq+tUqWKJCk2Nvaexz1//rw2b96sXr16EYaBW9jb28vf39+6rhwdHbOFYUnq2bOnJN11nWZmZmru3Lnq2bOnmjVrpvT0dN28eTPf6wYAoCARiAEUGsMwdPHiRZUvXz7btqtXr+rSpUvau3evhg4dKklq167dPY/x3XffKTMzU/379//b9QLF3Y0bN3TlyhX9+eefmj17tn7++ee7rqsLFy5IUo7r9FZHjx7VuXPnVL9+fQ0fPlxubm5yc3NT/fr1tXnz5nw7BgAAChL3EAMoNEuWLNHZs2c1ffr0bNt8fX2VkpIiSSpXrpw++eQTdejQ4b7GeOihh/Tkk0/+7XqB4m7ChAlasGCBJMnOzk69evXSZ599lutr3n//fXl4eKhLly659jt+/Lgkafbs2Spbtqx1nJkzZ6pz587as2eP6tevnw9HAQBAwSEQAygUkZGRGjlypFq0aKHBgwdn2/7zzz8rOTlZERERWrx4sW7cuHHPY0RFRWnfvn0aN26c7Oy4AAYYO3asevfurXPnzik0NFQZGRlKTU29Y/+ZM2fql19+0bx58+Tl5ZXrvhMTEyVJCQkJOnDggPz9/SVJTz75pGrUqKH3338/12cFAADwICAQAyhwFy5cUNeuXeXp6anly5fL3t4+W58nnnhCktSlSxf16NFDdevWVenSpTVq1Kg8j7NkyRJJ4nJp4P8LCAhQQECAJGnQoEHq2LGjunfvrl27dmV7gvTSpUs1ZcoUvfDCC3rppZfuum8XFxdJ0mOPPWYNw5JUuXJltWrVStu3b8/HIwEAoGBwCgVAgYqLi1OXLl0UGxurtWvX5voxSlmqV6+uRo0aWQNuXv3nP/9R7dq11aRJk/stFyjRevfurT179mT7PPANGzZo0KBB6tq1q+bPn5+nfWWt5YoVK2bb5u3trevXr//9ggEAKGAEYgAFJjk5Wd27d1dUVJRWr16tOnXq5Pm1SUlJiouLy3P/Xbt26Y8//uDsMJCLpKQkSbJZW7t27VLPnj3VtGlThYaGysEhbxeP1atXT6VKldLZs2ezbTt37pwqVKiQP0UDAFCACMQACkRGRoaCg4O1Y8cOLVu2TC1atMjWJz09PcezSLt379ahQ4fUtGlTm/bIyEidPn06x/H+85//SJKef/75fKgeKN4uXbqUrS0tLU3ffvutXFxcrG9ORUREqGvXrqpSpYpWr15tvQw6J7evP3d3dz311FPavn27IiMjre0RERHavn37fT0UDwCAwmYxDMMojIHu9oHIAEqWsWPHau7cuerevbv69u2bbfuAAQMUGxsrPz8/BQcHKygoSG5ubjp06JAWLlwoZ2dn7dy5UzVr1rS+xmKxqE2bNtqyZYvNvjIyMuTr66uqVatqx44dBX1owAOvZ8+eio+PV+vWreXr66sLFy5oyZIlioyM1EcffaTx48crISFBQUFBOnv2rGbOnClfX1+bfVSvXt3mjayc1t/Ro0f16KOPyt3dXWPGjJEkffLJJ0pPT9eBAwey7RMAgMJ2txzKQ7UAFIjw8HBJUlhYmMLCwrJtHzBggFxdXTVs2DBt3rxZy5cvV1JSknx8fNSvXz9NmTJFVapUydNYv/zyiy5evKjJkyfn4xEAxVdwcLC++uorff7557p69arc3d3VpEkTzZo1S08//bSkvz77+8yZM5Kk1157Lds+Bg8enOOVHbeqU6eO/u///k+vvvqqZsyYITs7Oz355JP64IMPCMMAgGKBM8QAAAAAgBLpbjmUe4gBAAAAAKZEIAYAAAAAmBKBGAAAAABgSgRiAAAAAIApEYgBAAAAAKZEIAYAAAAAmBKBGAAAAABgSgRiAAAAAIApEYgBAAAAAKZEIAYAAAAAmBKBGAAAAABgSgRiAAAAAIApEYgBAAAAAKZEIAYAAAAAmBKBGAAAAABgSgRiAAAAAIApEYgBAAAAAKZEIAYAAAAAmBKBGAAAAABgSgRiAAAAAIApEYgBAAAAAKZEIAYAAAAAmJLFMAyjMAZycnJShQoVCmOoApeYmKjSpUsXdRkoZMy7OTHv5sS8mxPzbk7Muzkx7+Zx+fJlpaSk3HF7oQXiksTPz08xMTFFXQYKGfNuTsy7OTHv5sS8mxPzbk7MO7JwyTQAAAAAwJQIxAAAAAAAUyIQ34fx48cXdQkoAsy7OTHv5sS8mxPzbk7Muzkx78jCPcQAAAAAAFPiDDEAAAAAwJQIxAAAAAAAUyIQAwAAAABMiUB8m8TERE2bNk2dO3dW2bJlZbFYFBISkq3fkCFDZLFYsn0FBAQUftH42/bs2aNRo0YpKChIbm5uqly5svr27auoqKhsfSMiItS5c2eVLl1aZcuW1cCBA3X58uUiqBp/V17nnfVeshw5ckR9+vRRtWrV5OrqqvLly6t169YKCwvL1pf1XnLkdd5Z7yXbO++8I4vForp162bbtn37drVq1Uqurq6qVKmSxowZo8TExCKoEvntTvPetm3bHNd7586di6hSFAWHoi7gQXPlyhVNnz5dlStXVoMGDbRly5Y79nVyctKXX35p0+bp6VnAFaIgzJo1S9u2bVOfPn1Uv359XbhwQZ999pkaN26snTt3Wv8CjYmJUevWreXp6amZM2cqMTFRH374oQ4dOqTdu3fL0dGxiI8E9yKv8y6x3kuSU6dOKSEhQYMHD5aPj49u3rypFStW6Omnn9aCBQs0fPhwSaz3kiav8y6x3kuqmJgYzZw5U25ubtm2hYeHq127dgoMDNTHH3+smJgYffjhhzp+/Lh+/vnnIqgW+SW3eZckPz8/vfvuuzZtPj4+hVEaHhQGbCQnJxvnz583DMMw9uzZY0gyFi5cmK3f4MGDDTc3t0KuDgVl27ZtRkpKik1bVFSU4eTkZPTv39/a9tJLLxkuLi7GqVOnrG0bNmwwJBkLFiwotHqRP/I676z3ki89Pd1o0KCBUbt2bWsb673ky2neWe8lV3BwsPHkk08abdq0MYKCgmy2denSxXjooYeMuLg4a9sXX3xhSDLWrVtX2KUiH+U27zm1wXy4ZPo2Tk5OqlSpUp77Z2RkKD4+vgArQmFo2bJltrM9NWvWVFBQkCIiIqxtK1asULdu3VS5cmVrW/v27VWrVi2FhoYWWr3IH3md9yys95LL3t5e/v7+io2Ntbax3ku+nOY9C+u9ZPn111+1fPlyzZkzJ9u2+Ph4bdiwQQMGDJCHh4e1fdCgQSpdujTrvRjLbd5vlZ6ezuXxJkYg/htu3rwpDw8PeXp6qmzZsho5ciSLqQQxDEMXL15U+fLlJUlnz57VpUuX1LRp02x9mzVrpgMHDhR2iSgAt897FtZ7yXPjxg1duXJFf/75p2bPnq2ff/5Z7dq1k8R6L8lym/csrPeSJSMjQ6NHj9awYcNUr169bNsPHTqk9PT0bOvd0dFRDRs2ZL0XU3eb9yxRUVFyc3OTu7u7KlWqpKlTpyotLa0QK0VR4x7i+/TQQw9p4sSJaty4sTIzM7V27VrNmzdPBw8e1JYtW+TgwI+2uFuyZInOnj2r6dOnS5LOnz8v6a+5v91DDz2ka9euKSUlRU5OToVaJ/LX7fMusd5LqgkTJmjBggWSJDs7O/Xq1UufffaZJNZ7SZbbvEus95Jo/vz5OnXqlH755Zcct99tvW/durVA60PBuNu8S1L16tX1xBNPqF69erpx44aWL1+uGTNmKCoqSkuXLi3EalGU+Fv9Pt1+8/1zzz2nWrVqafLkyVq+fLmee+65IqoM+SEyMlIjR45UixYtNHjwYElSUlKSJOX4H2BnZ2drH/6DXHzlNO8S672kGjt2rHr37q1z584pNDRUGRkZSk1NlcR6L8lym3eJ9V7SXL16VW+88YamTp2qChUq5Njnbus9azuKj7zMuyR99dVXNt8PHDhQw4cP1xdffKFx48apefPmBV0qHgBcMp2Pxo0bJzs7u1zficKD78KFC+ratas8PT21fPly2dvbS5JcXFwkSSkpKdlek5ycbNMHxc+d5v1OWO/FX0BAgNq3b69BgwZp9erVSkxMVPfu3WUYBuu9BMtt3u+E9V58TZkyRWXLltXo0aPv2Odu6521XvzkZd7vZMKECZLEejcRAnE+cnFxUbly5XTt2rWiLgX3KS4uTl26dFFsbKzWrl1r89j9rEupsi6tutX58+dVtmxZzhYVU7nN+52w3kue3r17a8+ePYqKimK9m8it834nrPfi6fjx4/r3v/+tMWPG6Ny5c4qOjlZ0dLSSk5OVlpam6OhoXbt27a7rnY/gKV7yOu934u/vL0msdxMhEOejhIQEXblyJddLM/DgSk5OVvfu3RUVFaXVq1erTp06Ntt9fX1VoUIF7d27N9trd+/erYYNGxZSpchPd5v3O2G9lzxZl0XGxcWx3k3k1nm/E9Z78XT27FllZmZqzJgxqlq1qvVr165dioqKUtWqVTV9+nTVrVtXDg4O2dZ7amqqwsPDWe/FTF7n/U5OnDghSax3E+Ee4vuQ9Q6Tu7u7Tfvbb78twzDUuXPnIqoM9ysjI0PBwcHasWOHVq1apRYtWuTY79lnn9U333yjM2fOWN9B3Lhxo6KiojRu3LjCLBn5IC/zznoveS5duiRvb2+btrS0NH377bdycXGxvinCei9Z8jLvrPeSpW7dulq5cmW29ilTpighIUFz585V9erV5enpqfbt22vx4sWaOnWqdf4XLVqkxMRE9enTp7BLx9+Q13mPj4+Xk5OTzdU+hmFoxowZkqROnToVWs0oWhYjt5tmTOqzzz5TbGyszp07p88//1y9evVSo0aNJEmjR4/W9evX1ahRI/Xr108BAQGSpHXr1umnn35S586dtWbNGtnZcfK9OBk7dqzmzp2r7t27q2/fvtm2DxgwQJJ05swZNWrUSF5eXnr55ZeVmJioDz74QH5+ftqzZw+XUBYzeZn36Oho1nsJ07NnT8XHx6t169by9fXVhQsXtGTJEkVGRuqjjz7S+PHjJbHeS5q8zDvr3Rzatm2rK1eu6PDhw9a2/fv3q2XLlqpTp46GDx+umJgYffTRR2rdurXWrVtXhNUiv9w+71u2bFG/fv3Ur18/1ahRQ0lJSVq5cqW2bdum4cOHW59GDxMwkM3DDz9sSMrx6+TJk8b169eNAQMGGDVq1DBcXV0NJycnIygoyJg5c6aRmppa1OXjPrRp0+aOc377Mjl8+LDRsWNHw9XV1fDy8jL69+9vXLhwoYgqx9+Rl3lnvZc8//3vf4327dsbFStWNBwcHIwyZcoY7du3N1atWpWtL+u95MjLvLPezaFNmzZGUFBQtvatW7caLVu2NJydnY0KFSoYI0eONOLj44ugQhSE2+f9xIkTRp8+fYwqVaoYzs7Ohqurq9GkSRNj/vz5RmZmZhFWisLGGWIAAAAAgClx3Q8AAAAAwJQIxAAAAAAAUyIQAwAAAABMiUAMAAAAADAlAjEAAAAAwJQIxAAAAAAAUyIQAwAAAABMiUAMAEAhCAkJkcVi0ZYtWwplvCpVqqht27aFMhYAAMUVgRgAAAAAYEoORV0AAADIf8eOHZPFYinqMgAAeKARiAEAKIGcnJyKugQAAB54XDINAEAhSktL05QpU+Tn5ydnZ2c1bdpU69evt26Pjo6WxWLRm2++qaVLl6pevXpycXFRYGCgfvjhB0nSwYMH1aFDB5UuXVre3t564403ZBiGzTjcQwwAwN0RiAEAKEQTJ07UDz/8oLFjx2rq1Km6dOmSunbtqo0bN9r0W716tSZMmKDnnntO7733ntLT09W7d299//336tChg+rXr68PPvhADRo00Ntvv61vv/22iI4IAIDiy2Lc/pYyAADIdyEhIRo6dKiqVKmi33//Xe7u7pKkc+fOKSAgQP7+/jpy5Iiio6NVtWpVlS5dWpGRkfL19ZUkHTlyRHXr1pXFYtGPP/6obt26SfrrjPPDDz+sypUra+fOndbxqlSpoipVqhTaU60BACiOOEMMAEAhevHFF61hWJJ8fHz0/PPP6+jRozp+/Li1/ZlnnrGGYUkKCgqSp6en/Pz8rGFYkkqVKqVmzZrZvBYAAOQNgRgAgEIUEBCQrS0wMFCSdOLECWtblSpVsvXz8vLSww8/nGP7tWvX8q9IAABMgkAMAMADyN7e/p7aAQDAvSMQAwBQiCIjI7O1RURESJKqVatW2OUAAGBqBGIAAArR/PnzlZCQYP3+3Llz+s9//qPAwEDVrFmzCCsDAMB8HIq6AAAAzMTLy0stWrTQkCFDlJqaqvnz5+vmzZuaM2dOUZcGAIDpEIgBAChE77//vrZs2aI5c+boypUrCgoK0r///W917NixqEsDAMB0+BxiAAAAAIApcQ8xAAAAAMCUCMQAAAAAAFMiEAMAAAAATIlADAAAAAAwJQIxAAAAAMCUCMQAAAAAAFMiEAMAAAAATIlADAAAAAAwJQIxAAAAAMCUCMQAAAAAAFMiEAMAAAAATOn/AYa3ejofLwBhAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x240 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "draw_box(df, 'bmi', 'Phân bố của trường dữ liệu BMI')\n",
+    "print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x240 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "draw_box(df, 'avg_glucose_level', 'Phân bố của trường dữ liệu chỉ số đường huyết trung bình ')\n",
+    "print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12,6))  \n",
+    "sns.histplot(data=df, x='age', bins = 'auto', color = sns.color_palette(\"pastel\", 1)[0])\n",
+    "plt.title(\"Phân bố của trường dữ liệu độ tuổi\",fontsize=16)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bảng dữ liệu tổng quan\n",
+    "![Bảng dữ liệu tổng quan](https://i.imgur.com/Bnzhf0g.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<ul>\n",
+    "<li>\n",
+    " <strong>Nhận xét:</strong> Tất cả các cột đều đủ dữ liệu.\n",
+    "</li>\n",
+    "<li>\n",
+    "<strong>Thống kê chất lượng dữ liệu:</strong> Tốt.\n",
+    "</li>\n",
+    "</ul>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 42px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 120px;\n",
+    "  background-position: 0 100%;\"> 3. Phân tích mối tương quan giữa các đặc trưng</h1>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 38px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 80px;\n",
+    "  background-position: 0 100%;\"> 3.1. Mối tương quan giữa bmi và chỉ số đường huyết trung bình: </h1>\n",
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:20px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<ul>\n",
+    "<li>Chỉ số BMI theo phân loại quốc tế (WHO) thì dưới 18,5 là nhẹ cân, 18,5-24,9 là bình thường, 25-29,9 là thừa cân và từ 30 trở lên là béo phì.</li>\n",
+    "<li>Béo phì có thể làm cho các tế bào của cơ thể đề kháng với insulin. Insulin là một loại hormone mang đường từ máu đến các tế bào trong cơ thể của bạn, nơi nó được sử dụng để tạo năng lượng. Nếu bạn đề kháng với insulin, đường không thể được các tế bào hấp thụ, dẫn đến lượng đường trong máu cao.</li>\n",
+    "<li>Theo Thư viện Y khoa Quốc gia Hoa Kỳ, nghiên cứu về mối tương quan giữa chỉ số bmi và đường huyết ở bệnh nhân tiểu đường và không tiểu đường đã chỉ ra rằng những người mắc bệnh tiểu đường có chỉ số bmi và lượng đường cao hơn so với nhóm không mắc bệnh.</li>\n",
+    "<li>Kiểm định tính xác thực, ta chia bộ dữ liệu thành 3 nhóm dựa trên chỉ số đường huyết trung bình: bình thường (đường huyết < 140), tiền tiểu đường (140 ≤ đường huyết < 200) và tiểu đường (đường huyết ≥ 200). Quan sát những thay đổi về chỉ số bmi giữa 3 nhóm này.</li>\n",
+    "</ul>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def draw_hist_2d(data, x, y, title, lines = []):\n",
+    "    plt.figure (figsize= (8,6))\n",
+    "    plt.hist2d(y = data['bmi'], x = data['avg_glucose_level'], cmap=plt.cm.Blues) \n",
+    "    plt.colorbar()\n",
+    "    plt.xlabel(x, fontsize = 11)\n",
+    "    plt.ylabel(y, fontsize = 11)\n",
+    "    plt.title(title, fontsize = 20)\n",
+    "\n",
+    "    for line in lines:\n",
+    "        plt.axhline(y = line, color = 'black',  linestyle='--')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "draw_hist_2d(df[df['avg_glucose_level'] < 140], 'avg_glucose_level', 'bmi', 'avg_glucose_level < 140', [17, 35])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = df[(df['avg_glucose_level'] >= 140) & (df['avg_glucose_level'] < 200)]\n",
+    "draw_hist_2d(data, 'avg_glucose_level', 'bmi', 'avg_glucose_level: 140 - 200', [26, 35])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = df[df['avg_glucose_level'] >= 200]\n",
+    "draw_hist_2d(data, 'avg_glucose_level', 'bmi', 'avg_glucose_level >= 200', [25, 42])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Quan sát 3 biểu đồ:</strong><br>\n",
+    "<ul>\n",
+    "<li>Biểu đồ chỉ số đường huyết bình thường: chỉ số bmi tập trung nhiều trong khoảng 17 đến 35.</li> \n",
+    "<li>Biểu đồ chỉ số đường huyết từ 140 đến 200: chỉ số bmi tập trung nhiều trong khoảng 25 đến 35. Phân bố của chỉ số bmi dưới 25 (bmi của nhóm bình thường và nhóm nhẹ cân) thấp hơn so với nhóm đường huyết bình thường.</li> \n",
+    "<li>Biểu đồ chỉ số đường huyết từ 200 trở lên: chỉ số bmi tập trung nhiều trong khoảng 25 đến 42. Phân bố của chỉ số bmi dưới 25 chiếm số lượng cực thấp. Đa phần nhóm này có chỉ số bmi thuộc nhóm béo phì và số ít thuộc nhóm thừa cân</li>\n",
+    "</ul>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Có mối quan hệ tuyến tính giữa bmi và chỉ số đường huyết trung bình\n",
+      "Hệ số alpha (intercept): 70.69170001301529\n",
+      "Hệ số beta (slope): 1.2369867166410764\n",
+      "avg_gluco_level = 1.2369867166410764.bmi + 70.69170001301529\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# avg_glu = alpha + beta*bmi\n",
+    "H0 = \"Không có mối quan hệ tuyến tính giữa bmi và chỉ số đường huyết trung bình\"\n",
+    "H1 = \"Có mối quan hệ tuyến tính giữa bmi và chỉ số đường huyết trung bình\"\n",
+    "X = df['bmi']\n",
+    "y = df['avg_glucose_level']\n",
+    "n = len(X)\n",
+    "X_mean = np.mean(X)\n",
+    "y_mean = np.mean(y)\n",
+    "XY_mean = np.mean(X * y)\n",
+    "X_squared_mean = np.mean(X**2)\n",
+    "beta = (XY_mean - X_mean * y_mean) / (X_squared_mean - X_mean**2)\n",
+    "alpha = y_mean - beta * X_mean\n",
+    "if(beta != 0):\n",
+    "  print(H1)\n",
+    "  print(\"Hệ số alpha (intercept):\", alpha)\n",
+    "  print(\"Hệ số beta (slope):\", beta)\n",
+    "  print(f\"avg_gluco_level = {beta}.bmi + {alpha}\")\n",
+    "else:\n",
+    "  print(H0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Kiểm định bằng phương pháp Least Squares Estimate:</strong><br>\n",
+    "<ul>\n",
+    "<li>\n",
+    "H0 : “Không có mối quan hệ tuyến tính giữa BMI và chỉ số đường huyết trung bình.”\n",
+    "</li>\n",
+    "<li>\n",
+    "H1 : “Có mối quan hệ tuyến tính giữa BMI và chỉ số đường huyết trung bình.”\n",
+    "</li>\n",
+    "</ul>\n",
+    "\n",
+    "</div>\n",
+    "<div class=\"symple-box yellow center \" style=\"background: #fffdf3; border-radius: 2px; border: 1px solid #f2dfa4; box-sizing: border-box; color: #c4690e; float: none; font-family: Arial, sans-serif; font-size: 22px; margin: 0px auto; outline: 0px; padding: 15px 20px; vertical-align: baseline;\"><strong style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Kết quả của kiểm định least squares để xác định mối quan hệ giữa chỉ số bmi và chỉ số đường huyết trung bình</strong>:\n",
+    "<ul style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px 0px 20px 20px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Có mối quan hệ tuyến tính giữa bmi và chỉ số đường huyết trung bình</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Hệ số alpha (intercept): 70.69170001301529</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Hệ số beta (slope): 1.2369867166410764</li>\n",
+    "</ul>\n",
+    "</div>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "\n",
+    "<ul>\n",
+    "<li>\n",
+    "Do β ≠ 0, nên ta bác bỏ giả thuyết H0\n",
+    "</li>\n",
+    "<li>\n",
+    "BMI và chỉ số đường huyết trung bình có mối quan hệ tuyến tính, tuân theo:\n",
+    "</li>\n",
+    "</ul>\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "<div class=\"symple-box yellow center \" style=\"background: #fffdf3; border-radius: 2px; border: 1px solid #f2dfa4; box-sizing: border-box; color: #c4690e; float: none; font-family: Arial, sans-serif; font-size: 22px; margin: 0px auto; outline: 0px; padding: 15px 20px; vertical-align: baseline;\"><strong style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Hàm tuyến tính của chỉ số đường huyết trung bình theo bmi</strong>:\n",
+    "<ul style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px 0px 20px 20px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">avg_gluco_level = 1.2369867166410764.bmi + 70.69170001301529</li>\n",
+    "\n",
+    "</ul>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Kết luận:</strong> Có mối quan hệ tuyến tính giữa chỉ số bmi và chỉ số đường huyết trung bình. Người có chỉ số bmi thuộc nhóm thừa cân và béo phì có nguy cơ cao tăng chỉ số đường huyết cao. Điều này phù hợp với kết quả nghiên cứu của y khoa.\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 38px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 80px;\n",
+    "  background-position: 0 100%;\">3.2 Mối tương quan giữa bệnh tim với một số đặc trưng khác: </h1>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 32px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 60px;\n",
+    "  background-position: 0 100%;\">3.2.1. Bệnh tim và chỉ số bmi </h1>\n",
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:24px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<ul>\n",
+    "<li><strong> Theo nghiên cứu của Theo Thư viện Y khoa Quốc gia Hoa Kỳ: </strong> béo phì làm tăng đáng kể nguy cơ mắc bệnh tim mạch so với nhóm bmi thông thường, thừa cân liên quan đến đến nguy cơ phát triển bệnh tim mạch đáng kể ở độ tuổi sớm hơn.\n",
+    "</li>\n",
+    "<li><strong>Kiểm định tính xác thực: </strong>Chia bộ dữ liệu thành với nhóm không mắc bệnh tim và nhóm mắc bệnh tim quan sát sự khác biệt về phân bố bmi giữa hai nhóm\n",
+    "</li>\n",
+    "</ul>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x240 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x240 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_heart_disease_1 = df[df['heart_disease'] == 1]\n",
+    "df_heart_disease_0 = df[df['heart_disease'] == 0]\n",
+    "draw_box(df_heart_disease_0, 'bmi', 'heart disease = 0')\n",
+    "draw_box(df_heart_disease_1, 'bmi', 'heart disease = 1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:24px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Quan sát 2 biểu đồ: :</strong><br>\n",
+    "<ul>\n",
+    "<li>Biểu đồ hộp về chỉ số bmi của người không bị bệnh tim: Q1 = 23.5, Q2 = 28.0, Q3 = 32.6.</li> \n",
+    "<li>Biểu đồ hộp về chỉ số bmi của người bị bệnh tim: Q1 = 27.3, Q2 = 29.9, Q3 = 32.6. Có hơn 75% người thuộc nhóm thừa cân và béo phì (bmi ≥ 25). Có đến 50% người bị béo phì (bmi từ 30 trở lên).</li> \n",
+    "<li>Có thể thấy, giá trị Q3 giữa hai biểu đồ không có sự khác biệt giá trị Q1, Q2 giữa hai nhóm lại cho thấy sự khác biệt. Ở nhóm bị bệnh tim thì những giá trị này cao hơn nhiều so với nhóm bị người không bị bệnh tim. </li>\n",
+    "</ul>\n",
+    "\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Giá trị t-statistic: 5.92917346374878\n",
+      "Giá trị v:  343\n",
+      "Giá trị p-value: 0.000000003713738\n",
+      "Có mối tương quan giữa BMI và bệnh tim.\n"
+     ]
+    }
+   ],
+   "source": [
+    "H0 = \"Không có mối tương quan giữa BMI và bệnh tim.\"\n",
+    "H1 = \"Có mối tương quan giữa BMI và bệnh tim.\"\n",
+    "bmi_heart_disease = df[df['heart_disease'] == 1]['bmi']\n",
+    "bmi_no_heart_disease = df[df['heart_disease'] == 0]['bmi']\n",
+    "n1 = len(bmi_heart_disease)\n",
+    "n2 = len(bmi_no_heart_disease)\n",
+    "mean1 = np.mean(bmi_heart_disease)\n",
+    "mean2 = np.mean(bmi_no_heart_disease)\n",
+    "std1 = np.std(bmi_heart_disease, ddof=1)  # ddof=1 để tính bằng mẫu, không phải toàn bộ quần thể\n",
+    "std2 = np.std(bmi_no_heart_disease, ddof=1)\n",
+    "v = ((std1**2 / n1) + (std2**2 / n2))**2 / (((std1**2 / n1)**2 / (n1 - 1)) + ((std2**2 / n2)**2 / (n2 - 1)))\n",
+    "v = int(v)\n",
+    "t_statistic = (mean1 - mean2) / np.sqrt(std1**2 / n1 + std2**2 / n2)\n",
+    "p_value = 1 - stats.t.cdf(np.abs(t_statistic), v)\n",
+    "print(\"Giá trị t-statistic:\", t_statistic)\n",
+    "print(\"Giá trị v: \",v)\n",
+    "print(\"Giá trị p-value: {:.15f}\".format(p_value))\n",
+    "alpha = 0.05\n",
+    "if p_value <= alpha:\n",
+    "    print(H1)\n",
+    "else:\n",
+    "    print(H0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Kiểm định t – test với mức ý nghĩa 5%:</strong><br>\n",
+    "<ul>\n",
+    "<li>\n",
+    "H0 : “Không có mối tương quan giữa chỉ số BMI và bệnh tim.”\n",
+    "</li>\n",
+    "<li>\n",
+    "H1 : “Có mối tương quan giữa chỉ số BMI và bệnh tim.”\n",
+    "</li>\n",
+    "</ul>\n",
+    "\n",
+    "</div>\n",
+    "<div class=\"symple-box yellow center \" style=\"background: #fffdf3; border-radius: 2px; border: 1px solid #f2dfa4; box-sizing: border-box; color: #c4690e; float: none; font-family: Arial, sans-serif; font-size: 22px; margin: 0px auto; outline: 0px; padding: 15px 20px; vertical-align: baseline;\"><strong style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Kết quả của kiểm định t – test về mối tương quan giữa bệnh tim và chỉ số bmi</strong>:\n",
+    "<ul style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px 0px 20px 20px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "Có mối tương quan giữa BMI và bệnh tim.</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Giá trị t-statistic: 5.92917346374878</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Giá trị v:  343</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Giá trị p-value: 0.000000003713738 </li>\n",
+    "</ul>\n",
+    "</div>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "\n",
+    "<ul>\n",
+    "<li>\n",
+    "Do p-value bé hơn mức ý nghĩa, nên ta bác bỏ giả thuyết H0\n",
+    "</li>\n",
+    "<li>\n",
+    "Với mức ý nghĩa 5%, ta thấy có mối tương quan giữa chỉ số BMI và bệnh tim mạch.\n",
+    "</li>\n",
+    "</ul>\n",
+    "<strong>Kết luận:</strong>  Có mối tương quan giữa bmi và bệnh tim mạch. Nhóm bị bệnh tim có tỉ lệ người bị thừa cân và béo phì cao hơn so với nhóm không bị bệnh tim. Điều này phù hợp với kết quả nghiên cứu của y khoa.\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 32px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 60px;\n",
+    "  background-position: 0 100%;\">3.2.2. Bệnh tim và giới tính, tuổi </h1>\n",
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<ul>\n",
+    "<li> <strong>Theo Thư viện Y khoa Quốc gia Hoa Kỳ:</strong> \n",
+    "\n",
+    "Nguy cơ xảy ra các biến cố tim mạch thường gia tăng nhanh hơn khi tuổi đời càng cao. Nam giới có nguy cơ mắc các bệnh tim mạch cao hơn so với nữ giới. Tuy nhiên sau thời kỳ mãn kinh (khoảng ngoài 50 tuổi), nguy cơ mắc bệnh tim mạch ở nữ giới sẽ gia tăng nhanh hơn.</li>\n",
+    "<li>\n",
+    "<strong>Kiểm định tính xác thực: </strong>Ta chọn mốc tuổi dễ bị bệnh tim là từ 40 tuổi, chia bộ dữ liệu theo 3 mốc tuổi: từ 40 - 60 tuổi, từ 60 - 80 tuổi và ngoài 80 tuổi. Quan sát tỉ lệ nam nữ mắc bệnh tim mạch.</li>\n",
+    "\n",
+    "</ul>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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378+ePXto374927Zto3Pnzrz44ou0bNmSiRMnUrRoUWbPns3atWstrssT5cbreicnJ1555RUA/vzzz1S321y0aBHHjh0z/5x0rv/9ObfMXURyHyV0iYhkobi4OBYuXAhA69atk20z4ebmRsuWLYH7e7intBVHYGAgd+7cAZI/PZJUwYIFadasWarlq1evBu7vi165cuU0x92gQQPg/l7mcXFxFmUlSpTAMAwMw2D79u1p9pOW27dv8/777wPw5ZdfPnCv8pTcu3fP/D6t/ckBi60co6KiMnyuzBIREWF+f+/ePRwdHdm2bRtvvvkm+fPnx8HBgQYNGrB161aef/55AFasWMG+ffss+smNcxcRERGR3EXxTMr+e03v6enJzp07ad26NS4uLjg7O9OqVSt27NhB4cKFAZgyZYrFE9yJbRPltmv6mJgY5s2bx6pVqzAMI1n59evXmT9/Plu2bEmxfW6eu4iIiIjkHopp0hYWFoa/v3+q1+1nz57l559/TnZ/IlFuva738/PDxub+RmY+Pj58/vnnXLhwgdjYWC5cuMDnn3+Oj4+PxZz+O+bcOncRyV2U0CUikoU2btxo3lM9tQv9xOOXLl1i27ZtycqDgoLM76tXr57m+WrUqJFqWWBgIAAnT57EZDKl+Xr33XcBiI2NtXj6PDMNGTKEmzdv8uKLL6a493p6JH3aJSYmJs260dHR5vcODg4ZOs+NGzcICgpK8XXu3LmHHjPc36e+XLlyyeo5ODhY7Fu/ZMmSVPvJyrmLiIiIyNNL8UzK/ntN/+GHH/LMM88kq+fu7s7HH39sHsvy5ctT7Se3xDNwP6GtcePGjB07lpCQED788ENOnDhBdHQ0oaGhbNq0ifr16xMYGMhrr73GxIkTk/WheEZEREREHgfFNKm7du0adevWZfr06cTFxTF27FjOnDlDTEwMt2/fZsWKFVSsWJHt27fzyiuvJLtHAbk3pqlduzYzZszAxsaG2NhYRo4cSfHixbGzs6N48eKMHDkSGxsbi1jGxcXFog/FNCLyOCihS0QkCyUu0VugQAGaN2+eYp1XX32VfPnyWdRP6t9//zW/T+kmQVJpld+4ceNBw01RZGTkQ7VLy9atW5k7dy7W1tZMnz4dK6uH+3OU9AL6QcvUJn2K/kHL3/7X1KlTqVy5coqvnj17PvSYAZo2bZpq3UaNGpmfEjlw4ECq/WTl3EVERETk6aV4JmUZuaZP+oR+dl3TZ2Y8A/efZt+xYwcAs2fPZvz48ZQvXx47Ozvy5s1LkyZN2LZtG97e3hiGwdChQzl69KhFH4pnRERERORxUEyTuoEDB/LXX39hMplYt24dw4YN49lnn8XW1hY3Nzdee+019uzZQ4UKFYiJiaFXr17m5LhEuTWmAejVqxf79u2jXbt2Flsi2tjY0KZNGw4dOmSRoJc/f36L9oppRORxsMnuAYiIPKlCQ0PNS+jevn37gUuuAixfvpypU6emuBf5o4qPjwfg+eefZ/78+eluV6RIkUwfy/jx44H7T6ucPHmSkydPJquT9KmKNWvWmAOhzp07m497eXmZ31+6dCnNp18uXrxofl+0aNGHH/wjsre355lnnuHmzZsPHEuePHlwd3fn2rVr5vqJ/jv3tOSUuYuIiIhI7qF4JnVeXl6YTCbzVoNpXWMnLXvQNX1uiGcMw8Df3x+AsmXL4uPjk2I9GxsbPvvsM+rXr09CQgJz5szh22+/NZcrnhERERGRrKaYJnX//vuveQXhxo0b8/LLL6dYz9nZmU8++YRu3boRGRnJ4sWLee+998zluTGmSapatWosX76cuLg4rl69SkxMDEWKFDGvvpX036lixYoWbRXTiMjjoIQuEZEs8ssvv1jsoZ0ed+/eZfny5XTv3t18LGnW/82bNylbtmyq7f97gyCpAgUKmM9RqVKlDI0rsyUuL7tv3z66dOnywPqDBg0yv0+a0PXcc8+Z3//9999p9pFYbmNjQ5kyZTI0Xj8/P/z8/DLUJi2JyxTD/wVxqUksT1ypK9HDzB2gQoUKGRmqiIiIiDylFM+kzsnJieLFixMcHAykfU2ftCwzrumzO565fv26ecuXqlWrplk36XY0/52f4hkRERERyWqKaVJ38uRJEhISgPtJTWnJ7Ov67I5pUmJjY5NiotXBgwfN71988UWLsrJly2JtbU18fLxiGhHJMkroEhHJIolL8xYqVMhin+3UDB06lEuXLjFv3jyLYCFp1v/BgwepV69eqn0k7sGekqpVq7J7927Onj3LtWvX8PT0TM80crSaNWtiZ2dHTEwMv//+O8OGDUuxXkxMDHv37jW3sbW1fZzDTKZBgwbmhK6zZ8+meiMkLCyMW7duAcmfwilZsiSFCxfmypUr/P7772me748//jD3UaJEiUcbvIiIiIg8FRTPpK1BgwbmhK6zZ89a3ORI6syZM+b3/72mz43xTNKktLi4uDTrxsbGptgOFM+IiIiISNZTTJO6zLquz40xTXrFx8ebVzErWrQodevWtSi3s7PjxRdfZM+ePezZs4eYmJhUV4FLjHns7e3TXMVMROS/rLJ7ACIiT6Jz586xa9cuADp06EDnzp0f+OrQoQMAW7du5fLly+a+atSogaurK0Cay/Bev36djRs3plrepk0b4P4WGZMmTXrkOT6K7du3YxhGmq+kW3ecO3fOfDwpFxcXGjVqBMCWLVtSXdZ2+fLlhIWFAdCuXbssmlX6Jf5bA6xYsSLVeitWrDDP+aWXXrIoM5lMtG3bFrj/dEdiMPRfe/fuNT/90bZtW0wm0yONXURERESefIpnHiy91/SJNwAg+TV9boxn3NzcyJs3LwB79uxJ8+ZP0kStkiVLWpQpnhERERGRrKSYJm0lSpQwX1vv2LEjzbppXdfnxpgmvWbPns2FCxcA6N+/P9bW1snqvPbaa8D9h/OTxn5JXbp0iS1btgDQqFEjXFxcsmbAIvJkMkREJNP5+fkZgAEY27dvT1ebHTt2mNuMGzfOomzgwIHmsu+++y5Z2/j4eKNt27bmOoBx7ty5ZPVefPFFAzCsra2NJUuWpDmeP//801i9enWy4+fOnTOfo2HDhuma28Pw8fFJcy6JAgICzPXatGljxMXFWZTfvHnTKFasmAEY+fLlM0JCQrJszBnRokULAzCsrKyMLVu2JCu/evWq4eXlZQCGnZ2dcenSpWR1Tp48aVhbWxuAUaNGDSMyMtKiPDIy0qhRo4YBGDY2NsapU6eybD4iIiIi8uRQPPNg8fHxRqVKlQzAcHJyMoKCgpLVOX78uOHs7GwAxjPPPGNEREQkq5Mb45kuXbqYx+zn55dinZCQEOO5554z19u4cWOyOopnRERERCSrKKZ5sDp16pj7mTNnTop1goODDU9PTwMwTCaT8ffffyerkxtjGsMwUrznkiggIMBwcHAwAKNs2bJGVFRUivVu375tuLq6GoBRvHhx49atWxblcXFxRuvWrc2/n23btmXmFETkKaCELhGRLFCqVCkDMDw8PIz4+Ph0tYmPjzcKFSpkAEbFihUtym7fvm2+aAaM7t27Gxs2bDAOHjxoLFmyxKhbt64BmIMBwAgODk52jtOnTxtubm7mOq1btzbmz59v7Nu3zwgMDDTWr19vfPHFF0bt2rUNwBgyZEiyPnJaQpdhGEbnzp3Ndb29vY1Vq1YZBw4cMPz9/c3/FoAxY8aMLBtvRp08edLIly+fARh58uQxhg0bZvzxxx/GgQMHjB9++MGczAUY48ePT7WfYcOGmetVrVrVWLx4sXHgwAFj8eLFRtWqVc1lw4cPf4yzExEREZHcTPFM+uzYscOws7Mz35j48ssvjT179hh79uwxxo8fb+TPn998rsWLF6faT26LZ06cOGE4Ojpa/DssW7bMOHTokLF7925j4sSJ5hs2gNGoUaNU+1I8IyIiIiJZQTHNg23bts38gIXJZDK6d+9urFmzxjh8+LDxxx9/GJ999plRoEAB87l69+6dal+5LaYxDMPIly+f0bZtW2PmzJnGrl27jMDAQGP58uVG9+7dDSsrKwMw3NzcjCNHjqTZz/Tp083zK1WqlOHv728cOHDAWLVqleHt7W0u69Kly2OamYg8SZTQJSKSyXbu3Gm+QOvfv3+G2r7zzjvmtoGBgRZlR44cMZ555hmLJzySvnx9fY3Zs2ebf7527VqK5zh58qT5SfIHvcaMGZOsfU5M6IqMjDRatmyZ6jysrKyM0aNHZ9lYH9aOHTuMggULpjpuk8lkjBgxIs0+4uPjjV69eqX579i7d+90B60iIiIi8nRTPJMxy5YtM1xcXFIdg62trTFt2rQ0+8iN8czmzZsNd3f3B/4bvPLKK2k+ga94RkREREQym2Ka9FuwYIHh5OT0wHF07tzZiI6OTrWf3BjTPGjeFStWfGAyV6JRo0YZJpMp1b5atmyZ6ipfIiJpsUJERDLVvHnzzO8T91xPr6T1k/YD8Pzzz3P8+HGGDBlCmTJlsLe3x93dHW9vbxYuXMhPP/1k3oMcMO/p/l9ly5blyJEjLFy4kA4dOlCsWDEcHByws7OjUKFCvPzyy4wYMYKDBw8yatSoDI0/uzg4OLBu3ToWLFhAkyZN8PDwwM7OjqJFi9K1a1d27tyJn59fdg8zmfr16/PXX38xevRonn/+efLmzUuePHkoWbIkPXv25ODBg3z22Wdp9mFlZcXs2bNZt24dbdu2pXDhwtjZ2VG4cGHatm3L+vXrmTVrFlZW+pMvIiIiIg+meCZjOnToQFBQEB988AEVKlTA2dkZR0dHypUrx4ABAzh+/DhvvfVWmn3kxnimcePG/P3334wfP56XX36ZZ555BltbWxwcHChZsiRvvPEGK1euZMuWLeTPnz/VfhTPiIiIiEhmU0yTfl27duXvv/9m5MiR1KlTBzc3N2xsbHBycqJcuXL4+PiwdetWFi1ahJ2dXar95MaYZtasWfTs2ZOKFSvi5uaGnZ0dRYoUoUWLFvj7+3P48GGef/75dPU1ZswYdu7cSdeuXSlatCh2dnZ4eHjQpEkTFi5cyLp168iTJ08Wz0hEnkQmwzCM7B6EiIhkjj59+jB79my8vLy4ePFidg9HREREREQk3RTPiIiIiIhIbqaYRkREMpMebxMReUJERUWxatUqAGrXrp3NoxEREREREUk/xTMiIiIiIpKbKaYREZHMpoQuEZFc4syZM6S2qGJ8fDxvv/02t27dAsDHx+dxDk1ERERERCRNimdERERERCQ3U0wjIiKPm7ZcFBHJJXx9fdm/fz+dO3emVq1aeHh4EBUVxZ9//smPP/7IoUOHAGjcuDGbNm3CZDJl84hFRERERETuUzwjIiIiIiK5mWIaERF53GyyewAiIpJ+J06cYPTo0amW16tXj8WLFytQEBERERGRHEfxjIiIiIiI5GaKaURE5HHSCl0iIrnEyZMn+fXXX9myZQvBwcHcvHmT2NhYChQoQI0aNejUqROdO3fGykq76YqIiIiISM6ieEZERERERHIzxTQiIvK4KaFLREREREREREREREREREREREQkh9CWi5ksISGBK1eu4OLiouU0RURERCTHMQyD8PBwChcurCcGJUWKaUREREQkJ1NMI2lRPCMiIiIiOVlG4hkldGWyK1euULRo0ewehoiIiIhImi5evIiXl1d2D0NyIMU0IiIiIpIbKKaRlCieEREREZHcID3xjBK6MpmLiwtw/5efN2/ebB6NiIiIiIilsLAwihYtar5uFfkvxTQiIiIikpMpppG0KJ4RERERkZwsI/GMEroyWeISvnnz5lWwICIiIiI5lraekNQophERERGR3EAxjaRE8YyIiIiI5AbpiWdy7AbzkZGR/Pbbb3z++ee0b9+e4sWLYzKZMJlM+Pn5Zco5rl+/zpAhQyhXrhwODg64ubnx0ksvMWvWLAzDyJRzPI0uXrzIu+++S6lSpbC3t8fd3Z1mzZqxbt26FOsn/rs+6DVv3ryHGs+qVato06YNnp6e2NnZ4eHhQd26dfn000+T1T158iTfffcdLVu2pEiRItjZ2ZE3b15q1qzJ2LFjuXv3bqrnmTRpEqVLl8be3p4yZcrwww8/pFr38uXLuLq60rRp04eak4iIiIiIZI2MxjOJtmzZQsuWLXF3d8fBwYHy5cvzySefpBlDPMjp06fx9fXFy8sLe3t7vLy88PX15ezZs2m2Cw8P5+OPPzbHuu7u7rRq1YqtW7em2ubgwYN4e3vj4OBAgQIF8PHx4datW6nW79ChA46Ojpw5c+ah5yciIiIiIpnvwoULvPvuu+Z4IE+ePJQsWRIfHx+OHj2aarvMjGm2b9/+wHs+06dPT9YuLCyMkSNH0qpVK0qVKoWrqyt2dnYULlyYtm3bphmXbd68mZo1a5InTx48PT0ZNGgQUVFRKdZNSEjgxRdfxNPTk3///TfD8xMRERF5HExGDs1c2r59O97e3imWjR49+pGTug4ePEizZs24ffs2AM7Ozty7d4+4uDgAmjVrxurVq7Gzs8tQv2FhYbi6uhIaGvpUPv1x4MABmjdvTkhICIUKFeLFF1/k9u3b7Nu3j9jYWEaNGsWYMWMs2vj6+qba34ULF9i2bRsmk4lz585RvHjxdI8lJiaGbt26sXTpUhwcHKhTpw4FCxbk2rVr/PXXX8THxye7QeHl5cXly5fJkycPNWrUwMvLi+vXr7Nnzx7u3btHqVKl2Lp1K8WKFbNoN2XKFAYOHEihQoWoXbs2e/bs4dq1a0yYMIEhQ4YkG1u7du3YtGkTx44d49lnn033nEREREQe1dN+vSoP9jR/Rh4mngH49ttvef/99zGZTLz00ksULFiQHTt2cO3aNcqVK8fOnTtxd3fP0Fh27dpF06ZNiYyMpGLFilSqVImgoCD++usvnJyc2LJlC7Vr107W7saNG7z00kucOnWKQoUKUb9+fa5fv86OHTuA+w+iDBw40KLN1atXKVeuHJGRkTRr1oxbt26xf/9+qlatyr59+7C1tbWov3r1atq2bcvYsWMZNmxYhuYlIiIi8qie5utVebCn/fOxb98+mjRpQnh4OEWKFKF69epYW1tz5MgRzp07h42NDQsXLqRjx44W7TI7pkm8x1ewYEGaN2+eYh0fH59k9wFPnz5NmTJlcHZ2plKlShQqVAgrKytOnz5tTkZ75513kj1Qf+TIEWrWrImdnR2NGzfmzJkz/PXXX7z66qusWbMm2bknTZrE4MGDWbRoEZ07d073vEREREQeVYauV40catu2bUb+/PmNRo0aGUOHDjUWLVpkeHp6GoAxevToR+r7zp075r7Kly9vHDhwwDAMw4iOjjamTJli2NraGoDx9ttvZ7jv0NBQAzBCQ0MfaYy5UVRUlFG0aFEDMDp16mRERkaay/bv328UKFDAAIxNmzalu8+3337bAIwmTZpkeDw9evQwAOO1114zbt68aVEWHx9v7NmzJ1mbV155xZg9e7YRHh5ucfzcuXNGxYoVDcDw9va2KIuLizPc3d0Nd3d383muX79uuLm5Gfny5TNiYmIs6i9fvtwAjK+++irDcxIRERF5VE/z9aqkz9P6GXnYeObQoUOGyWQyrK2tjfXr15uPR0REGI0aNTIAo0OHDhkaS0REhFG4cGEDMIYPH25RNnz4cAMwihYtajHGRG3btjUAo1GjRkZERIT5+Lp16wxra2vDysrKOHr0qEWbwYMHG4AxZ84c87Hu3bsbgLF06VKLumFhYYaXl5dRpUoVIzY2NkPzEhEREckMT+v1qqTP0/75qFKligEY/fr1s7g3ER8fb4wYMcIAjHz58hlRUVHmsqyIabZt22YARsOGDTPULjw83NizZ0+KscbWrVsNR0dHAzA2bNhgUfbaa68ZgLFt2zbDMAwjNjbWePnllw3AfA8w0YULFwxnZ2ejZcuWGRqbiIiISGbIyPVqjk3oiouLS3asePHimZLQlXjR6uDgYJw9ezZZ+ZdffmkAhrW1tXHy5MkM9f00BwsLFy40BwP//vtvsvJJkyYZgFG/fv109RcVFWXky5fPAIzFixdnaCxbtmwxAKNSpUrJEqoe1o4dOwzAAIyLFy+aj58+fdoAjL59+1rU79OnjwFY3CwJCwszihQpYrzwwgu6+SEiIiLZ4mm+XpX0eVo/Iw8bz3Ts2NEAjD59+iRrExwcbFhZWRmAceLEiXSP5YcffjAAo2zZskZ8fLxFWXx8vFG2bFkDMKZPn25R9tdff5lj2eDg4GT99u7d2wCMzp07WxyvX7++kSdPHotz7dy50wCM//3vfxZ1Bw4caFhZWRn79+9P93xEREREMtPTer0q6fM0fz5u3bplvodx48aNZOVxcXGGg4ODARiHDh0yH8+KmOZhE7oepFevXgZgvP/++xbHvby8jDJlylgcmz9/vgEYkyZNsjjeunVrw8nJKcWYSURERCSrZeR61Sp9i349ftbW1lnW97x58wDo3LkzJUuWTFY+cOBAnJ2diY+PZ8GCBVk2jifNgQMHAKhevTr58uVLVt64cWPg/tYh165de2B/v/76K3fu3MHNzY3XXnstQ2OZPHkyAIMHD062PcjDqlq1qvn9xYsXze8Tt+10c3OzqF+gQAEAi/3lhw8fzrVr1/jxxx+xsbHJlHGJiIiIiMije5h4JiYmhnXr1gHQtWvXZG2KFy9OvXr1AFixYkW6x5JYt3PnzlhZWYbtVlZWdOrUCYDly5en2K5evXopblefOMY1a9YQGxtrPn779m1cXV0tzpVSPLN//35++OEHBg4cSM2aNdM9HxERERERyXr29vbprpu4fWJWxTRZJfG+yn/nevv27XTdo1m2bBlr1qzh888/TzFmEhEREclJcmxCV1Y5efIkFy5cAKBFixYp1nF2duall14CYNOmTY9tbLld4kVx4kXyfyUGCIZhcOjQoQf25+/vD0C3bt0yFIjEx8cTEBAAQIMGDbh27Rrfffcdb7/9NoMHD2bu3LkWF/Dp9c8//5jfFypUyPy+RIkSAJw4ccKifuLPRYoUAWDv3r1MmzaNQYMGUaNGjQyfX0REREREss7DxDOnTp0iMjISINVr/MTjhw8fTvdYEutmtM/0touIiLCIb0qUKMHNmzfND6tA8ngmLi6Ovn374uXlxeeff57uuYiIiIiIyOOR9N7WiBEjLB7iSEhIwM/Pj6ioKFq0aEHRokWBrItpEl2/fp1PP/2U/v3789577zFt2jTzPbqMOnDgAEuWLMFkMtG6dWuLshIlSnDmzBmLOf83pgkNDTXfnxk0aNBDjUFERETkcXrqlggKCgoyv69UqVKq9SpVqsRvv/3G8ePHH8ewnggeHh4AnD17NsXypMfPnTuXZl/BwcFs27YNgN69e2doHGfPnjXfjNm7dy/vvPNOsgSuoUOHsnjxYl555ZV09ztu3DgAqlWrZk7igvvzrlOnDuvWrWPx4sW0atWKtWvXsm7dOqpUqULx4sWJjY2lX79+FC1alM8++yxD8xERERERkaz3MPFM4v/my5cPFxeXFNsl3ih5UAyUKDw83JxYVaxYsTT7vHnzJhERETg5OVmcI7V2efPmJW/evISFhXHu3Dmee+45ANq2bctvv/3Gu+++y/fff09ISAijRo0CMN8omTBhAn/++Sdr167F2dk5XXMREREREZHH68cff6Rly5bMnDmTdevWUaNGDaytrTl8+DCXL1+me/fuTJkyxVw/K2KapP7++29Gjx5tcczGxoaBAwfy1VdfpbmTyahRo7hw4QJRUVGcO3eOAwcOYGdnx/fff0+dOnUs6rZt25Zx48YxbNgwRo4cyT///MOECROws7OjWbNmAHz00UfcvHmT9evXJ1sJWURERCQneuoSuq5cuWJ+n5iVn5LEsrCwMO7evZvqF9bR0dFER0ebfw4LC8ukkeY+r7zyCl988QUHDx7k8OHDFlsUAkyfPt38/kG/p59++gnDMKhRowZVqlTJ0DiSPlXeu3dv6taty4QJEyhfvjxnzpzh448/Zv369bRt25ZDhw5RpkyZB/Y5Z84clixZgrW1NZMmTUpW/v333+Pt7U2XLl3Mx/LmzcuPP/4I3L/5cezYMdavX2++2QIQFRVFnjx5MJlMGZqjiIjkfLGxscTHx2f3MOQJZmVlha2tra4jRDLJw8Qz4eHhABbX+P+VGEumN1ZM7DOtfpPGp2FhYeZ66R1PWFiYxXh69+7Nzz//zOLFi1m8eLH5+EcffUS1atU4e/Ysn376KW+88QatWrUyl8fFxZGQkICdnV265iYiIrmHYRjExsaSkJCQ3UORJ5i1tTW2trbZPQyRJ0q5cuXYs2cP3bt3Z9OmTVy+fNlc9txzz/Hyyy+TN29e87GsiGkAXF1dGTx4MO3ataNs2bLkzZuXM2fO8NNPPzFlyhS+/fZb7t69y8yZM1PtY/Xq1Rw9etRiHBMnTqRXr17J6g4bNowVK1YwceJEJk6cCIDJZOKHH37A09OTXbt2MXPmTIYOHcoLL7xgbhcTE4OVlVWaiWUiIpI76R6NPA5ZHdM8dVcoSb8cd3R0TLVe0rLw8PBUE7rGjh3LmDFjMm+Audgrr7xCgwYN+OOPP2jTpg1Tp06lQYMG3L59m6lTpzJv3jxsbW2JjY1N8+mHhIQE5syZA5DihfmDGIZhfl+kSBE2btxo3rLx+eefZ/Xq1bzwwgsEBQUxbtw4Zs+enWZ/AQEB9O/fH4CvvvqK+vXrJ6tTo0YNgoKCmDt3LpcuXaJYsWL4+PhQtGhRzpw5w2effUaXLl3M23xOnjyZr776ikuXLuHg4MBrr73G5MmTU93eRUREco+wsDBu3bplkfAtklWsra1xdHTEw8NDCRUijyiz4pncyMbGhm3btrFgwQL27dtHnjx5aNWqFY0bNwbgrbfeIk+ePHz//ffA/a1W3nvvPXbt2kVCQgJVq1bl66+/plGjRtk5DRERyQQxMTHcuHGDyMhI3fyQx8Le3h53d3eLBBMReXi7du2iffv22NjYsHDhQl555RXs7OzYtWsX77//Pr1792bXrl0PvC/yqKpWrZrsIZnKlSszceJE6tevT4cOHfjxxx955513LBKskjpy5Ahw/7u2kydP8t1339GvXz8WL17MypUrLVYUc3V1JTAwkDlz5nDs2DFcXV3p2LEjNWvWJDY2lv79+/Pss8/i5+cH3L/vM3ToUA4fPoyVlRX16tVj0qRJycYsIiK5j+7RyOOWlTHNU5fQldmGDx/O+++/b/45LCzMvPzs02jp0qW0b9+eXbt20aZNG4uywYMHs3PnTgIDA3Fzc0u1jy1btnDhwgUcHBzo2rVrhseQ9CLe19fXnMyVyNramv79+zNw4EC2bNmSZl87d+6kbdu2xMTEMHr0aIt/6/8qXry4eVuSpPr374+DgwPfffcdcH81r/fee4+2bdsyZcoUjh8/jp+fH6dPn2bv3r1P3M0hEZGnSVhYGJcvX8bZ2Rl3d3etniRZxjAM4uPjiYqKIjQ0lODgYLy8vNJ8YEFEHiyj8Uxi7BEREZFqn4nbv6c3oE8az6TWb9It5ZP2+yjjsbW1xdfXF19fX4vjP//8M5s3b+bHH3+kYMGCXLhwAW9vb5ydnZk9ezaOjo74+fnRokUL9u7dS7Vq1dI1TxERyXkiIyO5ePEi1tbW5M+fHwcHB6ytrRXTSJZIXAUuNDTUvIKQkrpEHs2dO3do164dt27dYs+ePdSqVctc9uqrr/Lcc89RuXJl/P396datG97e3lkS0zxI+/bteeGFFzhy5Ahr1qxJNaErUd68ealZsyYLFiwgX758TJ06lTFjxjBhwgSLes7Ozrz77rvJ2o8bN46//vqLzZs34+DgQGBgIC1atKBMmTIsXryYqKgoPvnkE7y9vQkKCsLLyytT5ikiIo+f7tHI4/Q4YpqnLqEr6ZfjkZGRqf5CIyMjU2zzX/b29skShp5mHh4e7Nixgy1btrB161Zu375NwYIFadu2LTVq1KBw4cLA/ScxUuPv7w9Ahw4dcHV1zfAYSpQogclkwjAMnn322RTrJB6/evVqqv3s3r2bli1bEhERwSeffGJ+ciMj5s6dS0BAAP7+/nh4eAD3g4fixYuzbNkybGxsaNu2LaGhoYwfP54tW7bQtGnTDJ9HRERyhlu3buHs7IyXl5eCBHksnJ2dcXNz4/z589y6dYtixYpl95BEcrWMxjMlSpQA7t84CQ8PTzF2vHjxokXdB3FxccHNzY2QkBAuXLjA888/n2qf7u7uFlujlChRgkOHDnHhwoUU+0661WJ6xnP79m3ef/99GjZsSO/evQGYNm0aoaGhLF26lCZNmgBQpUoVKlSowIQJE1i4cGG65ikiIjnPrVu3sLW1pXjx4lhbW2f3cOQp4ODggIuLC5cuXeLWrVtK6BJ5ROvWrePmzZuUKlXKIpkr0bPPPkutWrXYtm0bW7ZswdvbO0timvSoUKECR44c4dKlSxlq17NnT6ZOncqKFSuSJXSl5NSpU3zxxRf06NHDvALxN998Q2xsLCtWrKBs2bIAeHp60qJFC6ZOncqXX36Z8QmJiEiOoHs08rhldUzz1C0FlPgFPGCxd/h/Jc2gS227RUmZyWSiSZMmjB07lpkzZ/LZZ59Ro0YNzpw5w9WrVylQoECqT22HhISwcuVKAPMNg4xydnamXLlywP3/aKck8Xhq/7Z79+6lefPmhIeH8/HHH/P5559neBy3bt1iyJAheHt707NnTwCuX7/O1atXqVmzpsWe7InbOCYuISwiIrlPbGws0dHRuLq6KlCQx8ra2ho3NzciIiKIi4vL7uGI5HoZiWfKlStnXhkvMDAwxf4Sj2dk5arEuhntM73tnJyczDcu0jJkyBDCw8OZOXOm+W9bYsxSt25dc73y5ctToEABxTMiIrlYXFwcERERuLm5KZlLHiuTyYSrqyvR0dHExsZm93BEcrXEBzvSupGY+BB9SEgIkHUxzYPcvn0bSHtBhZQkPtBy48aNdNXv378/Li4uTJw40XzsyJEjuLu7W8REukcjIpL76R6NZJesjGmeuoSuSpUqmd8HBQWlWi+x7LnnnsvyMT0tEp+W6NevH3Z2dinWWbBgAdHR0ZQqVYqGDRs+9Lk6duwIkOqWips3bwbgxRdfTFa2f/9+mjVrZk7m+uKLLx5qDO+//z4RERHMmDHDfCzxj8d/ly9O/Fl/XEREcq/4+Hjg/pZVIo9b4oqxSugSyTopxTN2dna0atUKIMWVqc6fP8/u3bsBaNeuXbrPlVh38eLFJCQkWJQlJCSwZMkS4P5WJUm99tprAOzatSvFVboSx9i6desH/r3aunUrc+fOZcSIERY3OlKKaQzDICoqSvGMiEgulngdqZ0IJDskXpckxtUi8nCKFCkCwN9//01oaGiy8tjYWA4dOgRAyZIlgayLadJy+fJlduzYAaR8jyYtAQEBAOl6QMXf35/t27fz7bffUqBAAfNxk8lEZGQkhmGYj+kejYhI7qd7NJKdsiqmeeoSusqWLWveimbDhg0p1omIiDBfTGr7u4w5fvy4eQuPRHFxcXz55ZfMmDGD0qVL88knn6TaPnG7xV69ej3wwnnFihWUL1+eRo0aJSsbNGgQ+fPnZ/369RYJVXD/psiCBQvM9ZIKDAykadOmhIWFPVIy15YtW/j5558ZOXIkZcqUMR/38PDAy8uLbdu2cebMGeD+/6kT552ZT7iIiEj20Bc/kh30uRPJHA8TzwwbNgyTycRPP/1kEWNGRkbSu3dv4uPj6dChA+XLl7dot3//fsqXL5/sOICvry+FCxfm1KlTjBw50qJs5MiRnDp1Ci8vL3r06GFRVrFiRdq2bUt8fDy9e/cmKirKXPbbb78xZ84crKysGD58eJq/h3v37tG/f38qVqzIRx99ZFGWGLPMmjXLfGz+/PlERkYqnhEReQLoulKygz53IpmjRYsWODk5ERUVRd++fbl79665LCYmhv/9739cuHABW1tbXn/9dXNZVsQ0kyZNSnEHlT///JPWrVsTFRVFqVKlaNu2rUX5woULOXjwYLJ2hmGwfPlyRowYAdx/0CYtN27cYOjQoTRt2pRu3bpZlFWrVo3IyEgWLVpkPjZz5kxzmYiI5G66tpTskFWfO5ORNAU9hytRogTnz59n9OjR+Pn5PXQ/I0eO5PPPP8fR0ZG//vor2b7fX331FR999BHW1tYcP348XZn+icLCwnB1dSU0NDTT98fMDQYPHsyMGTOoXr06RYoUITo6mr1793L9+nVKly7N5s2bU91n/fDhw1SrVg1ra2suXLhgsT1mSubMmUPPnj0pXrw4wcHByco3b95MmzZtuHfvHhUrVqRChQqcOXOGw4cPA/c/B59++qlFGzc3N/7991/y5cuXLJBIatiwYSkGKQBRUVFUrlwZR0dHDh48mCwLeObMmfTv3598+fLh7e3NqVOn+Ouvv6hXrx47duzQHxkRkVzq3r17nDt3jpIlS5InT57sHo48ZTLy+Xvar1flwZ7mz8jDxjPffvst77//PiaTiYYNG+Lh4cGOHTu4evUq5cqVY+fOnbi7u1u02b59O97e3gCkFJbv2rWLpk2bEhkZSaVKlahUqRJBQUEEBQXh5OTEli1bqF27drJ2N27coH79+vzzzz8UKlSIl156iRs3bvD7779jGAaTJk1K9mDLf3388ceMHz+enTt3UqdOHYuyK1euUKVKFW7fvs3LL7+Mg4MDmzZtwtbWln379lGlSpUH/ZpFRCQHUjwj2Smjn7+n+Xo1O4SHh/PNN9/w66+/cu7cOaytrSlbtiydO3dm4MCBqe7GkR7Lli1j7ty5HDx4kFu3bmFra4uXlxcNGjRgwIABvPDCCxnu82n/fMyfP5+ePXsSFxfHM888Q82aNbG1tSUwMJDLly9jZWXFDz/8wFtvvWXRLrNjmnz58nH37l1eeOEFSpYsiZWVlfn+TEJCAsWKFWPDhg1UqFDBop2vry9z587Fy8uLKlWqkC9fPm7fvs3ff//N+fPnARgwYACTJ09O815K165dWbVqFUFBQebVyBIdPXqU2rVrExsbS5MmTYiOjmbbtm0UKFCAY8eOUahQoYz90kVEJEdQTCPZKcvu0Rg5WEhIiHHz5k3zq2jRogZgDB061OJ4eHi4RbvRo0cbgAEY586dS9bvnTt3DE9PTwMwnnvuOSMwMNAwDMOIjo42pk6datjZ2RmA8fbbb2d4zKGhoQZghIaGPtScc7uNGzcabdu2NYoWLWrY29sbefPmNWrWrGl89dVXRmRkZJpt3333XQMwWrZsma5z/fTTTwZgFC9ePNU6J0+eNHx8fIwiRYoYtra2RoECBYyWLVsaGzduTLF+4ufmQa9t27ales5hw4YZVlZWxt69e9Mce8WKFQ1bW1vjmWeeMfr372/cuXMnXfMWEZGcKSoqyjh+/LgRFRWV3UORp1BGPn9P+/WqPNjT/Bl5lHhm8+bNRvPmzQ03NzfD3t7eKFOmjDF8+HAjLCwsxfrbtm0zxxep+eeff4wePXoYhQsXNmxtbY3ChQsbPXr0ME6fPp3mWEJDQ41hw4YZZcqUMezt7Q03NzejefPmxpYtWx74Ozh27Jhha2trvPPOO6nWOX78uPHqq68azs7OhoODg9GwYUNj9+7dD+xbRERyLsUzkp0y+vl7mq9XH7fg4GCjRIkS5utWR0dHw97e3vxz1apVjZCQkAz3e+/ePaN169YW37k7Ozub780AhpWVlTFx4sQM963Ph2EcOXLE8PX1NZ599lnD3t7esLOzM4oXL268+eabxr59+1Jtl5kxzVdffWW0bdvWKF26tOHq6mrY2NgYbm5uRv369Y2vv/461T537txpDBo0yKhRo4bh6elp2NraGo6OjkbZsmUNHx8fY8eOHQ+c/4YNGwzA+Oqrr1Kts3v3bqNhw4aGg4OD4ezsbLRq1co4fvz4A/sWEZGcSzGNZKesukeTo1foSlyR60F8fHyYM2eO+Wc/Pz/GjBkDwLlz51J8gvrgwYM0a9aM27dvA+Di4sK9e/eIjY0F7m+1uHr1auzt7TM05qf96Q+RB7l48SLjx4/nt99+49KlS7i4uFC9enUGDRpEq1at0tXH1KlTGTBgAAC9e/e22O4lIw4ePMi4ceP4448/CA0NpVChQrz66quMHDkSDw+PVNstWrSImTNncuTIEaKioihWrBht27bl448/Jn/+/Cm2WbhwIV988QWnT5+mYMGC9OrVi5EjR2JtbZ2sbkREBBUrVsTZ2ZlDhw490lNuIvL46OkPyU5aoUsykz4jIiIiTx/FM5KdtEJXzhQXF0e1atXMKxbNmzePxo0bk5CQwNKlS+nbty/h4eG0bNmSdevWZajv0aNHm3fPeOedd/j4448pUqQICQkJHD58mMGDB7Nz505MJhMHDhygevXq6e5bnw8REZGnk2IayU5ZdY/GJjMHmZtUr16dv/76i/Hjx7N27VouXryIk5MTlSpVwsfHh169emFlZZXdwxR5ohw4cIDmzZsTEhJCoUKFaNGiBbdv32bbtm1s2rSJUaNGmZMxU3P27Fk+/PBDTCZTilvTpNeyZcvo0qULcXFx1KxZk5IlSxIYGMiUKVNYunQpO3fupHTp0hZtDMPA19eXefPmYW1tzYsvvoinpyeBgYFMmDCBJUuWsHPnTooVK2bRbu3atbz55pvkz5+fVq1aceTIEcaMGcPt27eZPHlysrGNGDGCCxcusHPnTiVziTyBlu2/md1DyJDXX3wmU/tLT8L+t99+y+DBgzP1vNkh8SGDR90uXEREREQkp8ht8QxkbkyjeEaeJnPnzuXYsWMA/Prrr+atuK2srOjUqRMJCQl07dqV9evXExAQQKNGjdLd97x58wBo2LAhP/zwg/m4lZUV1atXZ+3atXh5eXH37l2WLVuWoYQuERERkbTktphG92ge3pMQ0+TohK7g4OCHaufn55euf5CCBQsyceJEJk6c+FDnEZH0u3fvHh06dCAkJIROnTrx008/4eDgANxP9GrRogWffvop9evXp0mTJin2kZCQgK+vLyaTiR49ejB37tyHGsuVK1fw8fEhLi6OGTNm0K9fPwDi4+Px9fVl/vz5dO3alX379mEymcztpk2bxrx583BxcWHt2rU0aNAAgNjYWN555x1mzZpF165d2blzp8X5Ro4ciZ2dHXv37qVs2bJERERQo0YNpk2bxieffIKnp6e5bmBgIJMnT+btt9+mbt26DzU/EZHcoF69eskSZxM999xzj3k0IiIiIiIi6ad4Rp4Gid+9ent7m5O5kurcuTOffPIJ586dY968eRlK6Lp69SoANWrUSLHc1dWVsmXLcujQIe7evfsQoxcRERGRtCimyR1ydEKXPJrcll0qj19mZ/SmZcWKFVy8eJF8+fIxffp0czIXQM2aNRk1ahTvvfcen376aaoJXZMmTWLHjh388MMP3Lhx46HH8t133xEZGUnjxo3NyVwA1tbWTJs2jTVr1nDgwAE2bdpEs2bNLM4P8P7775uTuQBsbW35/vvvWb9+Pbt27bJ4Ii0mJoZjx47xyiuvULZsWQCcnJzo1q0bI0aMYP/+/bRp0wa4n1DWr18/PD09GTt27EPPT0QkN+jTpw++vr7ZPQwRyeEU08iDPM6YRkREJJHiGXnSRUZGsmvXLgBatGiRYh2TyUTz5s2ZNm0amzZtylD/zz77LCdOnODgwYMploeGhnLq1Ckg9aSv3EIxjaRF8YyIiGQXxTS5g/YUFJHH4sCBA8D97U7z5cuXrLxx48YA7Nq1i2vXriUrP3nyJJ988gkNGzbk7bfffqSxrFixAoCuXbsmK3N2djYnWC1fvtx8PCwszPwlQuJYk3JwcKBevXrA/e0cE925c4f4+Hjc3Nws6hcoUADA4gmzb7/9lsOHDzNlypQH7pcrIiIiIiIiIiIikhVOnDhBQkICAJUqVUq1XmLZtWvXCAkJSXf/id/vbt++nQEDBnD58mUADMPg0KFDvPrqq9y9e5c6derQrVu3h52GiIiIiEiupoQuEXksEhOXEhOZ/svd3R34v6A9qfj4eHx8fDCZTMyePdtiG8SMCg8P5/Tp00DqT3clHj98+HCy8adnDkmfLPPw8MDR0ZETJ05Y1E38uUiRIsD9LWZHjx5Nu3bteO211zIyJRGRJ9rBgwd58803KVasGPb29ri5udGsWTPWr1+fYv0SJUpgMpkIDg7mt99+4+WXX8bV1ZX8+fPz6quvcuzYMXPdhQsXUqdOHVxcXMiXLx/t27fnzJkzKfa7fPly+vTpQ6VKlcifPz958uShZMmS9OrVi5MnTz7U3E6dOkX//v0pVaoUefLkwdXVlQYNGjB//vyH6k9ERERERHIWxTOSW125csX8PvH7y5QkLUva5kEGDBjAhx9+iJWVFVOnTsXLywsXFxfy5MlD9erVOX36NMOGDSMgIABra+s0+4qOjiYsLMziJSIiIiKZQzFN9lJCl4g8Fh4eHgCcPXs2xfKkx8+dO2dR9vXXX7Nv3z6++OILSpUq9UjjCA4ONr8vVqxYinWKFi2abBxubm7mLw8eNIf/jr9Nmzb8+eefTJw4kbCwMDZt2sRPP/2Eh4cHtWvXBu4/lWZjY8OUKVMebmIiIk+gSZMm8eKLL7Jw4UIKFChAmzZtqFixItu3b6dVq1Z8+umnqbadMWMGrVq1Ii4ujubNm+Ph4cG6deto0KABZ86c4cMPP8THxwdHR0eaN29O3rx5WbFiBQ0aNODff/9N1t8bb7zBokWLcHBw4JVXXqFZs2ZYWVnx008/Ub16dXbv3p2huS1dupTnn3+emTNnYmdnR8uWLalRowaHDh2ie/fu9OrVK8O/LxERERERyTkUz0huFh4ebn7v6OiYar2kZUnbPIiVlRVjx47F398fZ2dn4P4DtTExMQDcu3eP0NBQIiIiHtjX2LFjcXV1Nb8Sv9sVERERkUejmCb7KaFLRB6LV155BbifxZt05atE06dPN79P+hRVUFAQo0ePpm7dugwaNOiRx5H0iwUnJ6cU6yR+iZB0HHny5KFu3boA/Pjjj8na/PPPP2zdujVZO7j/pULBggUZMmQIrq6uNGvWjHv37vHjjz9ib2/PokWL2LBhA+PGjaNw4cLmdvfu3SM+Pv4hZyoikrtt3LiR//3vf7i5ufH7779z+PBhli5dyo4dOwgMDMTLy4vRo0fz+++/p9j+22+/ZfPmzezcuZMlS5Zw/PhxOnbsyJ07d3jttdfw9/cnMDCQgIAAli5dyt9//03dunW5cuUKU6dOTdbfggULuHHjBgcOHODXX39l1apVnD59mh9++IGIiAj69euHYRjpmtuxY8fo3r07AL/++isnTpxg+fLlBAQE8Ndff1G5cmV++ukn5s2b9/C/QBERERERyTaKZxTPSNpu3bpFo0aN8PX1pU6dOuzcuZM7d+5w9epVli9fzjPPPMO0adOoVauWeTvG1AwfPpzQ0FDz6+LFi49pFiIiIiJPLsU0OSOmUUKXiDwWr7zyCg0aNMAwDNq0acOaNWsIDQ3l7NmzfPDBB8ybNw9bW1vg/hNaAHFxcfj4+GBlZYW/v7/5eHYZNWoUJpOJVatW8dZbb/HPP/8QFhbG5s2badGihbnef8dZokQJgoKCGD9+PH379mXEiBH8+eeftGnThn///ZfBgwdTr1493nrrLQCWLFlCuXLlcHBwwMHBgVatWiVb9UtEJLfr2bMnJpMp2evll18GYPTo0RiGwfTp02nQoIFF28qVKzNx4kQAJk+enGL/gwYNolGjRuafra2tGT58OHA/WfjTTz/l+eefN5c7OjoyZMgQAAICApL116lTp2SJwCaTiXfeeYc6derw119/JdteNzVffPEF0dHRfP7557Rv396irHjx4syePRuA77//Pl39iYiIiIjI46V4RvHMk87FxcX8PjIyMtV6ScuStnkQHx8ftm/fTsOGDdm4cSP16tXD1dUVT09P2rVrx86dO3F3d+fs2bMMGzYszb7s7e3JmzevxUtERERE0qaYJnfENDbZPQAReXosXbqU9u3bs2vXLtq0aWNRNnjwYHbu3ElgYCBubm7A/f+YHjp0iPHjx1OuXLlMGUPSLxYiIiJwdXVNVufu3bsAyYL/xo0b8+OPP/Luu+8yY8YMZsyYYS4rWrQon332GcOGDTOPPyl3d3c+/PDDZMc/+OAD7ty5w8yZM83JYp07d6Z+/fqMHTuWq1ev8sknn+Dt7U1QUJB59TARkdyuXr16lC5dOtnx8uXLc+vWLfbv34+DgwOtW7dOsX1iUJHaMrotW7ZMdqxMmTLpKr9y5UqKfZ4+fZoNGzZw+vRpwsPDzasoXr9+HYCTJ0/y3HPPpdg2UUJCAr/99htwPwBJSY0aNXB2dubw4cPcu3ePPHnypNmniIiIiIg8XopnFM886ZLuInD58mWqVKmSYr2kq2clbZOWEydOsH79egCGDBmCyWRKVsfDw4MePXowceJEli9fzrx581KsJyIiIiIPRzFN7ohplNAlIo+Nh4cHO3bsYMuWLWzdupXbt29TsGBB2rZtS40aNcxBf+XKlQFYsWIFAGvWrDEH+YmCg4MBWLdunfkPxvbt2x84huLFi5vfX7hwwXyupBKX5S5RokSyst69e9OqVSuWLVvGiRMnMJlMVK1alU6dOrFw4UKL8T/I77//zk8//cTIkSPNf1zGjRuHk5MTq1evJn/+/MD9jOW3336bhQsX0q9fv3T1LSKS0/Xp0wdfX98Uyw4cOIBhGERFRWFvb59mPzdv3kzxeLFixZIdS5oUm1J5YtLvvXv3LI7Hx8ebk3nTWrL3v1vupuT27dvmekWLFk1X/SJFijywnoiIiIiIPD6KZxTPPOkqVKiAlZUVCQkJBAUFWexOkFRQUBAAnp6eKT7kmpLjx4+b35cqVSrVeok39CIjI7lx4wYFCxZM7/BFRERE5AEU0+SOmEYJXSLyWJlMJpo0aUKTJk0sjp85c4arV69SoEABqlWrZlG2c+fOVPu7du0a165dS/f58+bNS+nSpTl9+jSBgYEpJl8FBgYCJBtHIk9PT959991kx3fs2AGQbG4piY6Opn///pQrV46PP/7YfPzIkSNUqlTJnMwFUL9+fXOZiMjTICEhAbh/cd+hQ4eH6uNB2/RmZBvfSZMmMX36dDw9PZk4cSJ169alYMGC5qcyunbtyqJFi9K1P3vi3OD+FhMP8qBgSUREREREchbFM/9H8Uzu5ejoSL169dixYwcbNmxg6NChyeoYhsHGjRsBaNq0abr7Tvr5PX/+fKqrKCSutABo1wIRERGRx0gxzf/J7phGCV0ikiNMmDABgH79+mFnZwekncDk5+fHmDFj6N27N7NmzcrQudq1a8fXX3/NwoUL6dmzp0XZ3bt3WbNmDUCyPXPTEhwczK+//oqzs3Oq2cxJffHFF5w6dYrt27db/CEwmUxERERY1E38WcuKi8jTIvGpCJPJhL+/f4Yu7LPCL7/8AsCMGTOSbRkM8M8//6S7L3d3dxwcHIiKimLChAm4u7tn2jhFRERERCT7KZ6RJ4WPjw87duxg27Zt7Nu3j1q1almUL126lLNnzwLQo0ePdPeb9CHaadOmpbj6V0REBPPmzQOgSpUqODk5PcwUREREROQhKKbJObL3Ny8iT5Xjx48nW+owLi6OL7/8khkzZlC6dGk++eSTTDnXihUrKF++PI0aNUpWNnjwYBwdHdmyZQs//vij+Xh8fDzvvPMOd+7coWbNmsmeLIuJieHw4cPJ+vv7779p1aoVUVFRfPPNNxQoUCDNsR0/fpzx48fTp08fGjRoYFFWrVo1Tpw4wa5du8zHZs6caS4TEXkaFC5cmCpVqhAeHs6GDRuyeziEhIQAltv2Jvrrr78ytIKitbW1eSXHxCBERERERESeHIpn5Enh4+ND5cqVMQyDDh06EBAQANxf1WDp0qX07dsXgBYtWiT7DtbPzw+TyYTJZCI4ONiirHjx4rRu3RqANWvW0L17d86cOYNhGMTGxrJ7925efvllc7LYkCFDsnimIiIiIpKUYpqcQwldIvLYzJw5k4IFC1K/fn06derEa6+9hpeXF5988gmlSpVi8+bNmfa0VWhoKCdPnuTMmTPJygoXLsycOXOwtramX79+1K5dm86dO1O2bFl+/vlnChYsyMKFC5OtiBUZGUm1atUoVaoULVu2pGvXrtStW5dKlSpx4sQJPv30U/r165fmuAzDoF+/fri5ufHVV18lKx8xYoR5W8q2bdtSq1Yt/P39KV26NF26dHm0X4qISC7y+eefA9CzZ0/zyolJGYbBvn372LRpU5aPpUKFCgD88MMPFsvxXr16lR49ehAXF5eh/kaPHo2dnR1Dhw5l7ty5Fn0mCgoKYvny5Y82cBERERERyRaKZxTPPAlsbGxYvXo1JUqU4PLlyzRu3BgnJyecnJx44403CAsLo2rVqixYsCDDffv7+1O9enUA5s+fT+nSpXF2djZv9RgYGAjA0KFDM7T6l4iIiIhkDsU0OSOmUUKXiDw2LVu2pFmzZly4cIFVq1axbds2ihUrxldffcWff/5JiRIlHttYOnbsyL59+2jfvj1nz55lxYoVxMfHM2DAAI4ePUrp0qWTtXF0dOS9994jX7587N27l19//ZWLFy/SuXNn9u7dy8iRIx943pkzZ7Jr1y6+//578uXLl6y8efPmrF69mkqVKrFhwwZOnTpF586d+f3333F0dMyMqYuI5AqtW7dm0qRJhISE0KZNG8qUKcOrr77Km2++SdOmTfH09KR27dps3bo1y8fy8ccfY2dnx48//ki5cuXo1KkTLVq0oFSpUkRHR9OuXbsM9VetWjXmz58PgK+vL8WLF6dZs2Z069aNli1bUrRoUSpXrpwrng4REREREZHkFM8onnlSlChRgj///JNRo0ZRqVIlTCYTtra2VK9enQkTJrB3717y58+f4X7d3d3Zu3cvs2bNolmzZhQsWJDY2FhsbGx49tln6datGzt27EjxgVgRERERyXqKaXJGTGOT3QMQkadH06ZNk21j+LD8/Pzw8/NLtdzX1xdfX980+6hevTq//vprus9pZ2fHd999l+76Kenfvz/9+/dPs86rr77Kq6+++kjnERF5EgwaNIhXXnmFyZMns23bNgICArCyssLT05OqVavSqlUrOnTokOXjqFWrFoGBgYwYMYIDBw6wevVqihYtysCBAxkxYgQDBw7McJ8dO3akZs2afP/992zevJldu3YRHx9PwYIFKV26NO+++y6vv/56FsxGREREREQeB8UzimeeFC4uLowZM4YxY8aku82DvruF+yuA9e7dm969ez/iCEVEREQkKyimyf6YxmQYhpHdg3iShIWF4erqSmhoKHnz5s3WsSzbfzNbzy853+svPpPdQxAReWLcu3ePc+fOUbJkSfLkyZPdw5GnTEY+fznpelVyppz0GVFMIw+imEZEJHMonpHslNHPX066XpWcJ6d9PhTTSFoUz4iIZB7FNJKdsuoejbZcFBERERERERERERERERERERERySG05aLIU+zOhsnZPQTJ4fI1z/gSlSIiIiIiIiIiIiIiIiIiIvLwtEKXiIiIiIiIiIiIiIiIiIiIiIhIDqGELhERERERERERERERERERERERkRxCCV0iIiIiIiIiIiIiIiIiIiIiIiI5hBK6REREREREREREREREREREREREcggldImIiIiIiIiIiIiIiIiIiIiIiOQQSugSERERERERERERERERERERERHJIZTQJSIiIiIiIiIiIiIiIiIiIiIikkMooUtERERERERERERERERERERERCSHUEKXiIiIiIiIiIiIiIiIiIiIiIhIDqGELhERERERERERERERERERERERkRxCCV0iIiIiIiIiIiIiIiIiIiIiIiI5hBK6REREREREREREREREREREREREcgib7B6AiIiIyNPkzobJ2T2EDMnXfGCm9leiRAnOnz8PwKBBg5g0aVKqdb/++ms+/PBDAKytrYmLi3ukcwcHB1OyZEmKFy9OcHDwI/UlIiIiIvI0ym3xDGRuTKN4RkREREQkd8ttMY3u0TzdtEKXiIiIiGSLBQsWEBMTk2q5v7//YxyNiIiIiIhI+imeERERERGR3EwxTc6nhC4REREReexq1KjB7du3WbVqVYrlu3fv5u+//6ZmzZqPeWQiIiKpCw4OxmQypev1xx9/WLQNCQlh+PDhVKhQAQcHB/Lnz0+DBg34+eefH3lcq1atok2bNnh6emJnZ4eHhwd169bl008/TbXN6dOn8fX1xcvLC3t7e7y8vPD19eXs2bOptlm4cCEVK1bE3t6eYsWK4efnR3x8fIp1IyIiKFGiBJUqVUrzy0ERkdxI8YyIiIiIiORmimlyByV0iYiIiMhj16tXLyD1Jzxmz55tUU9ERCQncHZ2xsfHJ9VX4pdcLi4uVK9e3dzu7NmzVKtWjXHjxnHz5k0aNWpEtWrVOHjwID169MDX1xfDMDI8npiYGN544w1ee+01tmzZQsWKFXn99depVKkSZ86c4fvvv0+x3a5du3j++eeZO3cu+fLlo127duTLl4+5c+dSpUoV9u7dm6zN2rVrefPNN7l69SqtWrXCxsaGMWPGMHjw4BTPMWLECC5cuMDMmTOxs7PL8NxERHIyxTMiIiIiIpKbKabJHZTQJSIiIiKPXeXKlalRowabNm3i8uXLFmV3797ll19+wcvLi6ZNm6bY/vjx44wePZp69epRpEgR7OzsKFCgAI0bN+aXX355qDFFRUXxzTffULt2bfLly0eePHkoV64cH374Ibdv336oPkVE5Mni7u7OnDlzUn25u7sD0LlzZ5ycnMztunTpwvnz53n55Zf5559/WLt2LQEBARw9epRSpUoxd+5cZs2aleHx9O3bl6VLl/Laa69x4cIFAgICWLhwIVu3buXq1ausXbs2WZvIyEjeeOMNIiMjGT58OEFBQSxevJigoCCGDx9OREQEb7zxBlFRURbtRo4ciZ2dHXv37mX58uUcO3aM8uXLM23aNK5du2ZRNzAwkMmTJ/P2229Tt27dDM9LRCSnUzwjIiIiIiK5mWKa3EEJXSIiIiKSLXr16kVCQgJz5syxOP7LL79w9+5dfHx8sLJK+XJ14sSJfPrpp4SEhFC5cmXat29PuXLl2LZtG506deL999/P0FiuXLlCrVq1+OCDD/jnn3+oWbMmLVu2JDo6mq+//poaNWpw/vz5h52qiIg8BS5fvszGjRsB6N27t/n4nj172L9/P9bW1syaNYv8+fOby0qXLs3EiRMB+OyzzzK0SldAQADz5s2jUqVK/PLLL+ZkskRWVlbUrl07Wbs5c+Zw5coVypYty+eff25R9vnnn1O2bFkuXrzIvHnzzMdjYmI4duwYDRs2pGzZsgA4OTnRrVs34uPj2b9/v7lufHw8/fr1w9PTk7Fjx6Z7PiIiuY3iGRERERERyc0U0+R8SugSERERkWzRtWtXHBwckgUL/v7+mEymNJfy7d69O2fOnOHEiRNs2LCBxYsXs3v3bo4fP46Xlxfffvutxc3ltBiGwRtvvMGxY8fo3bs3wcHBbN68meXLl3P69GmGDBlCcHAwPXv2fJTpiojIE27OnDkkJCRQsWJFatWqZT5+4MABAEqUKEGpUqWStWvcuDEAFy9eTPffLoDJkycDMHjwYGxtbdPdbsWKFcD9VcT++6WclZUVnTp1AmD58uXm43fu3CE+Ph43NzeL+gUKFADuP7mZ6Ntvv+Xw4cNMmTKFvHnzpntcIiK5jeIZERERERHJzRTT5HxK6BIRERGRbOHq6kr79u05ffo0v//+OwAnT55k165dNGzYkGeffTbVtqmVlytXjpEjRwKwbNmydI1j48aN7Nq1ixdeeIHp06fj4uJiLrOxseGrr76iUqVKbNu2jaCgoIxMUUREniKJX34lXZ0L/i/ZKTH56b8cHR1xcHAA4ODBg+k6V3x8PAEBAQA0aNCAa9eu8d133/H2228zePBg5s6da5FkldThw4cBqFGjRorliccT6wF4eHjg6OjIiRMnLOom/lykSBEAgoODGT16NO3ateO1115L11xERHIrxTMiIiIiIpKbKabJ+WyyewAiIiIi8vTq1asXCxYswN/fn4YNG+Lv728+/iB3797lt99+4/Dhw9y6dYuYmBgArl69CtwPPNJj3bp1AHTo0AEbm+SXx1ZWVjRo0ICgoCB2795NpUqV0tWviIg8PX7//XdOnz6NnZ0d3bt3tyjz8PAA4Ny5cym2vXbtGlFRUWnW+a+zZ8+aE7b27t3LO++8kyyBa+jQoSxevJhXXnnFfCw8PJzbt28DUKxYsRT7Llq0KAA3b94kIiICJycnANq0acPixYuZOHEiffr0Ye/evfz00094eHiYt3Z8++23sbGxYcqUKemah4hIbqd4RkREREREcjPFNDmbErpEREREJNt4e3tTsmRJli1bxnfffce8efPImzcvr7/+eprt1qxZQ8+ePc03pVMSFhaWrjGcPXsWgJEjR5qfHEnNzZs309WniIg8XRK/7GrTpg3u7u4WZd7e3phMJm7evMnKlSuTrVw1ffp08/v0/u1K+vevd+/e1K1blwkTJlC+fHnOnDnDxx9/zPr162nbti2HDh2iTJkywP2ErkSJiVr/5ezsbDGexHpjx45l27ZtDBkyhCFDhgBga2vL/Pnzsbe3Z9GiRWzYsIGpU6dSuHBhcx/37t3D1tYWa2vrdM1NRCQ3UTwjIiIiIiK5mWKanE0JXSIiIiKSbUwmE76+vowePRofHx+uXbtGv379zFtPpeTy5ct06tSJqKgoPvzwQ958801KlCiBs7MzVlZWbNq0iWbNmmEYRrrGkJCQAED9+vUpVapUmnUrVqyY/smJiMhTISwszLyEfEpPL5YqVYpu3brx888/06tXL+7evUuLFi2IiopiwYIFfPnll9ja2hIbG4uVlVW6zpn0b1yRIkXYuHEj9vb2ADz//POsXr2aF154gaCgIMaNG8fs2bMfeZ4lSpQgKCgIf39/Tp8+TcGCBXnzzTcpX748//77L4MHD6ZevXq89dZbACxZsoRRo0Zx6tQpbG1tadKkCVOmTKFkyZKPPBYRkZxC8YyIiORWMTExTJ8+nV9++YXjx48TGRmJu7s7lStXxtfXl06dOlnUDwkJ4euvv2blypUEBweTJ08eKleuTN++fZOtUpxeERERfP/99/z666+cOnWKqKgoChQoQI0aNejXrx9t2rRJs/0ff/zB1KlT2blzJzdv3sTFxYXixYvToEEDvvrqK2xtbS3qT5o0icmTJ3Px4kWKFSvG4MGDGTBgQIp9X758meeee45atWqxadOmh5qfiEhuoJgmZ1NCl4iIiIhkK19fX8aMGcOaNWuABy/lu2bNGqKiomjXrh3jx49PVv7PP/9k6PyJW0u1bduWDz74IENtRUREFi9eTGRkJF5eXjRr1izFOtOmTSM8PJyVK1cmu9nxxhtvEBMTw8qVK3Fzc0vXOV1cXMzvfX19zclciaytrenfvz8DBw5ky5YtKbaLiIhIse+kWzfmzZvXoszd3Z0PP/wwWZsPPviAO3fuMHPmTEwmE6tWraJz587Ur1+fsWPHcvXqVT755BO8vb0JCgqyWAVMRCS3UzwjIiK5zaVLl2jWrBnHjx/H3d2devXq4eTkxMWLF/njjz9wcnKySOg6e/Ysr7zyCufPn6dAgQI0atSIqKgo9u7dy44dOwgICOCnn37CZDKlewy3b9+mQYMGHD9+HGdnZ+rWrUu+fPk4ffo069atY926dQwaNIhJkyYla2sYBv/73/+YNGkStra21KpViwYNGnDr1i1OnDjBd999x2effWaR0DVlyhQGDx5MoUKFaNWqFXv27OHdd9/l3r175hWIk3r33XeJi4uzWFFZRORJpZgm50rfo58iIiIiIlmkWLFitG3blgIFClC7dm1q1aqVZv2QkBAAihcvnqzMMAwWLlyYofO3aNECgKVLl6b7iRGRjAgPD8fPz4/KlSvj7OyMq6srNWvW5JtvviEmJuaR+l62bBmtW7emcOHC2NnZ4eTkRLly5ejbty9HjhzJnAmISJoSt1v09fVNdYUtJycnVqxYwe7duxk1ahR9+/blo48+YuvWrSxZsoTr168DULly5XSds0SJEuabJc8++2yKdRKPX7161XzMxcXFnDR24cKFFNtdvHgRuJ+8ldq2jEn9/vvv/PTTTwwbNoznnnsOgHHjxuHk5MTq1atp3749AwYMYNy4cZw/fz7Df6dFRHI6xTMiIpKbREVF0aRJE44fP46fnx9XrlxhzZo1LF68mF27dnHz5k1GjBhh0aZLly6cP3+el19+mX/++Ye1a9cSEBDA0aNHKVWqFHPnzmXWrFkZGsenn37K8ePHqV69OufPn2fjxo0sWbKEgwcPsm7dOmxsbPj+++/Zu3dvsrZ+fn5MmjSJunXr8s8//7Bjxw4WLlzIpk2buHjxIvv37ydPnjzm+vHx8YwZMwZ3d3f+/PNPli9fztGjR3Fzc+Pzzz8nNjbWov8VK1awcuVK/Pz8Uo23RESeJIppci4ldImIiIhItlu+fDm3bt1iz549D6xboUIF4H4iS9Kb1PHx8YwaNYrdu3dn6Nxt27alZs2a7N+/n549e6a4B/u///7L9OnTiYuLy1DfIufPn6dKlSqMGTOGoKAgDMMgOjqawMBAPvjgA2rXrs2///6b4X6jo6Np06YNHTt2ZO3atVy9ehV7e3vi4uI4deoUs2bNonr16nz77bdZMCsRSXT8+HH27duHyWSiZ8+eD6xfp04dxowZw8yZMxk3bhze3t6Eh4dz5MgRbGxs8Pb2Ttd5nZ2dKVeuHAC3bt1KsU7i8f+uhlWtWjUAAgMDU2yXeDyxXlqio6Pp378/5cqV4+OPPzYfP3LkCBUqVCB//vzmY/Xr1zeXiYg8aRTPiIhIbjF27Fj+/vtv+vXrx+jRo5NtS+jo6MgLL7xg/nnPnj3s378fa2trZs2aZXGNX7p0aSZOnAjAZ599lqGb8Fu3bgXgo48+SrZSccuWLc2x0X//tp48eZIvv/ySggULsm7duhSTCWrWrImNzf9tUhUcHMytW7do164d7u7uAHh4eNC+fXvu3LnDiRMnzHXDw8MZOHAgL7zwAv/73//SPR8RkdxOMU3OpIQuEREREclVWrduTfXq1bl06RJly5bl1VdfpVOnTpQqVYrx48fz0UcfZag/KysrVq5cyQsvvMDcuXMpWbIk9erVo0uXLnTo0IGqVavyzDPP8Pbbbz91wYI8mri4OFq3bk1wcDCFChVi8+bNREREEBkZyeLFi3FxceHw4cN069Ytw31/+eWX5iWw33nnHS5dukR4eDhRUVEEBgZSv359EhISGDJkCAcPHszsqYnI/zd79mwAvL29H/rJ7alTpxIVFUXHjh0pWLBgutt17NgRwGJLxaQ2b94MwIsvvmhxvF27dsD9rSITEhIsyhISEliyZAkA7du3f+AYvvjiC06dOsWMGTMstn00mUzJtnRM/Dkj27CIiDyJFM+IiEh2iY2NZdq0aQAMHTo0XW0OHDgA3F8luFSpUsnKGzduDGBeGSu9kq6glZbEBKxE06ZNIy4ujr59+5IvX7509XH79m2AZIljBQoUACy3nR8+fDjXrl3jxx9/tEgKExGR/6OY5vFRQpeIiIiI5Co2NjZs376djz/+mCJFihAQEMD27dupWrUqe/bsoXnz5hnus3Dhwuzdu5fp06fz4osvcvLkSZYtW8bOnTsBeOutt9i4cWO6v2wSAZg7dy7Hjh0D4NdffzV/yWllZUWnTp2YMWMGAOvXrycgICBDfc+bNw+Ahg0b8sMPP1CkSBFz39WrV2ft2rU4OztjGAbLli3LrCmJSBKxsbHMnz8fgN69e6dZ98yZM8meLjQMA39/f0aOHImbmxvffPNNsnYrVqygfPnyNGrUKFnZoEGDyJ8/P+vXrzf/9yTR4sWLWbBggbleUr6+vhQuXJhTp04xcuRIi7KRI0dy6tQpvLy86NGjR5pzOn78OOPHj6dPnz40aNDAoqxatWqcOHGCXbt2mY/NnDnTXCYi8jRTPCMiItnl0KFD3Lp1i8KFC1O6dGmOHTvGmDFj6N+/P8OGDWPdunXJHvpITHZKTH76L0dHRxwcHAAy9EBZ4vZa48ePN2/dlWj9+vVs27YNT09P2rRpY1G2ceNGABo0aMCdO3eYMWMGAwYMYODAgcyYMSPFFYxLlCgBYLESV9KfE79T2bt3L9OmTWPQoEHUqFEj3XMREXnaKKZ5fEyGNqHMVGFhYbi6uhIaGkrevHmzdSzL9idfik4kqcYhi7N7CJLD5Ws+MLuHIJJr3Lt3j3PnzlGyZMmn7oJSsl9GPn856Xr1SdegQQN27NiBt7e3eSuBpAzDoFSpUpw7d44ePXowd+7cdPedJ08eoqOjGTJkCBMmTEixTvXq1Tl06BDvvvsukydPTnffOekzophGHuT1F5/JtnOvWLGC9u3bky9fPq5evZrmf3+/++47hg4dSrVq1ShWrBiGYRAYGMj58+fx8PDgt99+SzHRac6cOfTs2ZPixYsTHBycrHzz5s20adOGe/fuUbFiRSpUqMCZM2c4fPgwcD9B69NPP03WbteuXTRt2pTIyEgqVapEpUqVCAoKIigoCCcnJ7Zs2ULt2rVTnY9hGLz00kucOXOGEydOJHsyfsOGDbRs2ZI8efLQpEkTrl27xv79+yldujRHjx7F0dEx1b5FJHsonpHslNHPX066XpWcJ6d9PhTTSFoedzzz448/0q9fP1588UW8vb356quvkm2TWLVqVVauXEmxYsUAmDVrFn379uWZZ57hxo0byfq8du0ahQoVAuCDDz7g66+/TtdYIiIi6NChAxs3bsTZ2Zl69eqRL18+Tp8+zcGDB6lXrx6zZ882bzUPEBMTQ548eTAMgxkzZjBy5MhkY3J2dubHH3+kc+fOFsfr1q3L/v37mT9/Pq1atWLt2rV0796dihUrcvToUWJjY6levTphYWH89ddfODk5pWseIpJzKKaR7JRV92i0QpeIiIiIiEgmi4yMNK9Mk/jU6X+ZTCbz00qbNm3KUP+JW7ul9vRraGgop06dAtBTpSJZxN/fH4CuXbs+8IuaevXq0aFDB27evMn69evZsGEDefPmZeTIkZw8efKhV61q0qQJR48excfHhzt37rBq1SouXLhAy5Yt2bhxY4rJXInjOXr0KD169CAkJIRff/2VkJAQevTowdGjR9NM5oL7q23t2rWL77//PsVtTpo3b87q1aupVKkSGzZs4NSpU3Tu3Jnff/9dyVwiIiIiItkkcevBw4cPM378eN555x1OnjxJaGgomzdvpmzZshw+fJhWrVoRGxsL3N9e3mQycfPmTVauXJmsz+nTp5vfh4WFpXssTk5OrFmzhg8++ICIiAg2btzIkiVLOHjwIAUKFKBx48bmlbMShYSEmBPQ3n33XTw9Pdm+fTthYWH8/fff+Pr6cvfuXbp168aOHTss2n7//fc4ODjQpUsX8ubNS9euXXFycuLHH38EYMKECRw7doxp06ZZJHNFRUUlS3oTERF5XLT5r4iIiIiISCY7ceKEeZuCSpUqpVovsezatWuEhITg5uaWrv7ffvttBg0axPbt2xkwYIB5eWvDMDh8+DDvvfced+/epU6dOnTr1u3RJyQiyaxZsybddWvWrMnixRlfIdnX1xdfX98065QtW5Y5c+ZkuO/SpUtnaGXApPr370///v3TrPPqq6/y6quvPlT/IiIiIiKS+RITk2JjY+nSpQtTpkwxlzVu3JjNmzdTrlw5goKCWLx4Md27d6dUqVJ069aNn3/+mV69enH37l1atGhBVFQUCxYs4Msvv8TW1pbY2FisrNK/jsjVq1dp27Ytf/75J59//jldunTBw8OD48ePM2LECMaMGcPKlSvZsWMHLi4uFuMHcHBwYMuWLTzzzP1VzsqVK8dPP/3E9evX+e233/Dz8yMgIMBcv0aNGgQFBTF37lwuXbpEsWLF8PHxoWjRopw5c4bPPvuMLl26mB/Kmzx5Ml999RWXLl3CwcGB1157jcmTJ6e69aSIiEhW0ApdIiIiIiIimezKlSvm9/99ojSppGVJ2zzIgAED+PDDD7GysmLq1Kl4eXnh4uJCnjx5qF69OqdPn2bYsGEEBARgbW2dZl/R0dGEhYVZvERERERERETkyZKYGAWk+IBGsWLFaNWqFQBbtmwxH582bRqvvfYa//77L927d8fd3Z2iRYsybNgw2rVrZ26T3ofUAHx8fDhw4ACfffYZH3/8MSVLlsTJyYmaNWuydu1aKleuzNGjR5kwYUKK42/fvr05mSupd955B4AdO3YQExNjUVa8eHFGjRrFzJkzGTFiBEWLFjX/LhwcHPjuu++A+6t5DRo0iOrVq7Ny5UpGjhzJr7/+SosWLcwP74mIiDwOSugSERERERHJZOHh4eb3aW0vlrQsaZsHsbKyYuzYsfj7++Ps7AzA3bt3zV9W3rt3j9DQUCIiIh7Y19ixY3F1dTW/Er/QFBERERERCQ8Px8/Pj8qVK+Ps7Iyrqys1a9bkm2++SZYskR7BwcGYTKZ0v3r27JkFsxJ5Oj377LMpvk+pztWrV83HnJycWLFiBbt372bUqFH07duXjz76iK1bt7JkyRKuX78OQOXKldM1jsuXL7N582YAunTpkqzc1taW119/HbBMLHN2djYncT1o/LGxsdy6deuBY5k7dy4BAQFMmDABDw8PAMaNG0fx4sVZtmwZbdu2Zfjw4fzvf//jwIEDFuMRERHJakroEhERERERyWVu3bpFo0aN8PX1pU6dOuzcuZM7d+5w9epVli9fzjPPPMO0adOoVasWly9fTrOv4cOHExoaan5dvHjxMc1CRERERERysvPnz1OlShXGjBlDUFAQhmEQHR1NYGAgH3zwAbVr1+bff//NUJ/W1tYULFgwzZerq6u5fs2aNTN7WiJPrWrVqmEymQBSTXZKPJ748FhSderUYcyYMcycOZNx48bh7e1NeHg4R44cwcbGBm9v73SN48KFC+b3efPmTbFO4n8HQkJCLI5Xr149XeNPbQ7/rTtkyBC8vb3NyaPXr1/n6tWr1KxZExsbG3Pd+vXrA3DkyJE0+xQREclMNg+uIiIiIiIiIhmRdBuAyMjIVOslLUva5kF8fHzYvn07DRs2ZOPGjeYvZF1dXWnXrh316tWjYsWKnD17lmHDhvHzzz+n2pe9vT329vbpPrdITnJnw+TsHoLkYPmaD8zuIYiIiORacXFxtG7dmuDgYAoVKsS8efNo3LgxCQkJLF26lL59+3L48GG6devGunXr0t1v0aJFuXbtWpp1Bg4cyJQpU3BwcKBr166POhUR+f88PT2pX78+O3bsYMuWLVStWtWiPDY2lt9//x2AF198MV19Tp06laioKLp06ULBggXT1aZIkSLm9/v27aNJkybJ6uzduxeAkiVLWhzv2LEjGzZsYOvWrSQkJGBlZbl2SeLKX+XKlUs1WSzR+++/T0REBDNmzDAfS/x+5b8rnif+nFguIiLyOGiFLhERERERkUxWuHBh8/u0VshKWpa0TVpOnDjB+vXrARgyZEiKXyZ6eHjQo0cPAJYvX45hGOnqW0REREREBO5vQ3bs2DEAfv31Vxo3bgzc3/69U6dO5gSI9evXExAQkGnnvXfvHgsWLACgQ4cO5MuXL9P6FhEYPXo0AGPHjjUnTcH9JM4hQ4Zw9uxZXFxcLLY7PXPmDDdv3rToxzAM/P39GTlyJG5ubnzzzTfJzrVixQrKly9Po0aNLI4XK1bMvPree++9R3BwsEX5/PnzWbJkCUCypM5u3bpRqlQpgoKCGDVqFAkJCeaybdu2MXHiRAAGDRqU5u9hy5Yt/Pzzz4wcOZIyZcqYj3t4eODl5cW2bds4c+YMAPHx8fj7+wP3VzkTERF5XJTQJSIiIpKJlDQh2UGfu5ynQoUK5qdEg4KCUq2XWObp6Ymbm1u6+j5+/Lj5falSpVKtl/iFZGRkJDdu3EhX3yIiIvJ003WlZAd97nKmuXPnAuDt7U2dOnWSlXfu3Nm8cs68efMy7bzLly83b+PYp0+fTOtXRO5r1KgRn332Gf/++y8vvfQS9erVo0OHDpQpU4bJkyfj4ODAokWLLFbbWrNmDYULF6ZWrVp07NiR119/nZIlS9K7d2/y58/P5s2bKVSoULJzhYaGcvLkSXNiVFL+/v64u7tz4sQJKlSogLe3Nx07dqRSpUp0794dwzDo1q0bb775pkU7Ozs7li9fjpubG1988QVly5bl9ddfp06dOjRu3JjIyEh8fHx4++23U/0dREVF8dZbb1G5cmWGDh2arHzkyJHcu3ePGjVq0L59e55//nk2bdpEvXr1eOWVVzLy6xaRbKBrS8kOWfW5U0KXiIiISCZITNyIj4/P5pHI0yjxc/ffZeYl+zg6OlKvXj0ANmzYkGIdwzDYuHEjAE2bNk1330n/nc+fP59qvevXr5vfOzs7p7t/ERERefoonpHspHgm54mMjGTXrl0AtGjRIsU6JpOJ5s2bA7Bp06ZMO/fs2bOB+w+oNGzYMNP6FZH/M2LECDZu3EiTJk34+++/WbNmDfHx8fj6+nLo0CFatWplUT8x6evmzZusX7+eDRs2kDdvXkaOHMnJkycfatWqSpUqERQUxEcffUTZsmU5cOAAK1eu5MaNGzRr1owlS5bw888/p7gqeZUqVQgKCmLAgAHEx8ezZs0a/v77bxo2bMiiRYuYM2dOmlsjfvrpp5w7d44ff/wRW1vbZOX9+vXjp59+okiRIqxdu5YbN27Qv39/1q1bpy0XRXIwxTSSnbIqprHJ1N5EREREnlK2trZYW1sTFRWlxAl57MLDw7G1tU3xSyjJPj4+PuzYsYNt27axb98+atWqZVG+dOlSzp49C2DeHjE9kn5ROm3atBRvsERERJifkq9SpQpOTk4PMwURERF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+      "text/plain": [
+       "<Figure size 3000x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(30, 6))\n",
+    "\n",
+    "age_groups = [[40, 60], [60, 80], [80, 90]] \n",
+    "x = 'heart_disease'\n",
+    "hue = 'gender'\n",
+    "palette_color = 'pastel'\n",
+    "for i, age_group in enumerate(age_groups):\n",
+    "    tempDf = df[(df['age'] >= age_group[0]) & (df['age'] < age_group[1])]\n",
+    "    \n",
+    "    data = tempDf[x].groupby(tempDf[hue]).value_counts(normalize=True).rename('percent').reset_index()\n",
+    "    \n",
+    "    # Vẽ biểu đồ cột\n",
+    "    ax = sns.barplot(data=data, x=x, y='percent', hue=hue, ax=axs[i], palette=palette_color)\n",
+    "    axs[i].set_xlabel(x, fontsize=16)\n",
+    "    axs[i].set_ylabel('percent', fontsize=16)\n",
+    "    axs[i].set_title(f'Age: {age_group[0]} - {age_group[1]}', fontsize=20)\n",
+    "    \n",
+    "    # Thêm số trên đầu cột\n",
+    "    for p in ax.patches:\n",
+    "        if p.get_height() > 0:\n",
+    "            ax.annotate(format(p.get_height(), '.2%'), (p.get_x() + p.get_width() / 2., p.get_height()), \n",
+    "                    ha='center', va='center', xytext=(0, 7), textcoords='offset points',fontsize =16)\n",
+    "    \n",
+    "    axs[i].tick_params(axis='both', labelsize=18)\n",
+    "      \n",
+    "    \n",
+    "    axs[i].legend(prop={'size': 16})\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Quan sát biểu đồ:</strong> <br>\n",
+    "<ul>\n",
+    "<li>Nhìn chung tỷ lệ nam, nữ mắc bệnh tim mạch đều tăng dần theo độ tuổi. Tại cả 3 biểu đồ, tỷ nam mắc bệnh tim nhiều hơn nữ.</li> \n",
+    "<li>Mốc tuổi 40 - 60, nam mắc bệnh tim mạch cao gấp 2.5 lần nữ giới. Mốc tuổi 60 - 80, nam mắc bệnh tim cao gấp 2 lần nữ. Mốc tuổi ngoài 80, nam giới mắc bệnh tim cao gấp 1.5 lần nữ giới. Có thể thấy tuy nam có tỷ lệ mắc bệnh tim mạch cao hơn phụ nữ, nhưng ở độ tuổi ngày càng cao, khoảng cách chênh lệch giữa nam nữ mắc bệnh tim mạch dần dần được thu hẹp. Điều này chứng minh phụ nữ sau thời kỳ mãn kinh (khoảng ngoài 50 tuổi) có tỉ lệ mắc bệnh tim mạch tăng lên nhanh chóng.</li> \n",
+    "</ul>\n",
+    "<strong>Kết luận: </strong>Có mối tương quan giữa độ tuổi và bệnh tim mạch. Độ tuổi càng cao thì nguy cơ mắc bệnh tim càng gia tăng, nam giới có tỉ lệ mắc bệnh tim mạch cao hơn nữ giới, ở độ tuổi sau mãn kinh tỷ lệ nữ giới mắc bệnh tim mạch gia tăng nhanh chóng. Điều này phù hợp với nghiên cứu của y khoa \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 32px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 60px;\n",
+    "  background-position: 0 100%;\">3.2.3.\tBệnh tim và hút thuốc lá: </h1>\n",
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<ul>\n",
+    "<li> <strong>Theo Thư viện Y khoa Quốc gia Hoa Kỳ:</strong> \n",
+    "hút thuốc lá là một trong những yếu tố nguy cơ hàng đầu mắc các bệnh tim mạch mãn tính. Hút thuốc làm tổn thương tim và mạch máu rất nhanh, nhưng tổn thương này sẽ được phục hồi nhanh chóng đối với hầu hết những người hút thuốc ngừng hút thuốc.</li>\n",
+    "<li>\n",
+    "<strong>Kiểm định tính xác thực: </strong>chia bộ dữ liệu thành 2 nhóm, nhóm bị bệnh tim và nhóm không bị bệnh tim, sau đó theo dõi sự phân bố của thuộc tính tình trạng hút thuốc trong 2 nhóm trên.</li>\n",
+    "\n",
+    "</ul>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "def draw(tempDf, x, hue):\n",
+    "    # Tính toán phần trăm và đếm số lượng\n",
+    "    data = tempDf[x].groupby(tempDf[hue]).value_counts(normalize=True).rename('percent').reset_index()\n",
+    "    \n",
+    "    # Vẽ biểu đồ cột\n",
+    "    ax = sns.barplot(data=data, x=x, y='percent', hue=hue, palette='pastel')\n",
+    "    \n",
+    "    # Thêm số trên đầu cột\n",
+    "    for p in ax.patches:\n",
+    "        if p.get_height() > 0:\n",
+    "            ax.annotate(format(p.get_height(), '.2%'), (p.get_x() + p.get_width() / 2., p.get_height()),\n",
+    "                        ha='center', va='center', xytext=(0, 7), textcoords='offset points', fontsize=16)\n",
+    "            \n",
+    "    ax.tick_params(axis='both', labelsize=16)\n",
+    "    \n",
+    "    # Đặt tên cho hue\n",
+    "    legend_labels = [str(label) for label in data[hue].unique()]\n",
+    "    ax.legend(title=hue, prop={'size': 16}, title_fontsize=16)\n",
+    "    \n",
+    "    ax.set_xlabel(x, fontsize=16)\n",
+    "    ax.set_ylabel('percent', fontsize=16)\n",
+    "    plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(26, 8))\n",
+    "draw(df, 'smoking_status', 'heart_disease')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Quan sát biểu đồ: </strong><br>\n",
+    "<ul>\n",
+    "<li>Hút thuốc trong nhóm không bị bệnh tim chiếm 15.19% và 22.18% ở nhóm bị bệnh tim. Con số chênh lệch đáng kể khi ở nhóm bị bệnh tim gấp 1.46 lần nhóm không bị bệnh tim. Chứng tỏ hút thuốc có nguy cơ bệnh tim cao.</li> \n",
+    "<li>Đã từng hút thuốc: chiếm 16.79% trong nhóm không bị bệnh tim và 28% trong nhóm bị bệnh tim. Có thể thấy đã từng hút thuốc trong nhóm bị bệnh tim cao gấp 1.67 nhóm không bị bệnh tim. Việc đã từng hút thuốc cũng có nguy cơ gây bệnh tim</li> \n",
+    "<li>Không hút thuốc: chiếm 37.17% trong nhóm không bị bệnh tim và 32.36% trong nhóm bị bệnh tim. Không hút thuốc làm giảm khả năng bị bệnh tim một chút.</li>\n",
+    "</ul>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected ij:\n",
+      "[[  47.8668942   819.1331058 ]\n",
+      " [ 101.47560731 1736.52439269]\n",
+      " [  42.84280265  733.15719735]\n",
+      " [  82.81469584 1417.18530416]]\n",
+      "Chi-Square value: 44.0267323134354\n",
+      "Degree of Freedom:  3\n",
+      "p-value: 0.000000001489578\n",
+      "Có mối tương quan giữa việc hút thuốc và bệnh tim.\n"
+     ]
+    }
+   ],
+   "source": [
+    "heart_formerlysmoked_count = df[(df['smoking_status'] == 'formerly smoked') & (df['heart_disease'] == 1)].shape[0]\n",
+    "noheart_formerlysmoked_count = df[(df['smoking_status'] == 'formerly smoked') & (df['heart_disease'] == 0)].shape[0]\n",
+    "heart_neversmoked_count = df[(df['smoking_status'] == 'never smoked') & (df['heart_disease'] == 1)].shape[0]\n",
+    "noheart_neversmoked_count = df[(df['smoking_status'] == 'never smoked') & (df['heart_disease'] == 0)].shape[0]\n",
+    "heart_smokes_count = df[(df['smoking_status'] == 'smokes') & (df['heart_disease'] == 1)].shape[0]\n",
+    "noheart_smokes_count = df[(df['smoking_status'] == 'smokes') & (df['heart_disease'] == 0)].shape[0]\n",
+    "heart_unknown_count = df[(df['smoking_status'] == 'Unknown') & (df['heart_disease'] == 1)].shape[0]\n",
+    "noheart_unknown_count = df[(df['smoking_status'] == 'Unknown') & (df['heart_disease'] == 0)].shape[0]\n",
+    "H0 = \"Không có mối tương quan giữa việc hút thuốc và bệnh tim.\"\n",
+    "H1 = \"Có mối tương quan giữa việc hút thuốc và bệnh tim.\"\n",
+    "\n",
+    "\n",
+    "formerly_smoked_arr = [heart_formerlysmoked_count, noheart_formerlysmoked_count]\n",
+    "never_smoked_arr = [heart_neversmoked_count, noheart_neversmoked_count]\n",
+    "smokes_arr = [heart_smokes_count, noheart_smokes_count]\n",
+    "unknown_arr = [heart_unknown_count, noheart_unknown_count]\n",
+    "\n",
+    "nij = []\n",
+    "nij.append(formerly_smoked_arr)\n",
+    "nij.append(never_smoked_arr)\n",
+    "nij.append(smokes_arr)\n",
+    "nij.append(unknown_arr)\n",
+    "nij = np.array(nij)\n",
+    "\n",
+    "total_sum = np.sum(nij)\n",
+    "row_sums = np.sum(nij, axis=1)\n",
+    "col_sums = np.sum(nij, axis=0)\n",
+    "expected_ij = np.outer(row_sums, col_sums) / total_sum\n",
+    "print(\"Expected ij:\")\n",
+    "print(expected_ij)\n",
+    "chi_square = np.sum((nij - expected_ij)**2 / expected_ij)\n",
+    "print(\"Chi-Square value:\", chi_square)\n",
+    "dof = (2 - 1)*(4 - 1)\n",
+    "print(\"Degree of Freedom: \", dof)\n",
+    "p_smoke = 1 - stats.chi2.cdf(chi_square,dof)\n",
+    "alpha = 0.05\n",
+    "print(\"p-value: {:.15f}\".format(p_smoke))\n",
+    "if p_smoke <= 0.05:\n",
+    "  print(H1)\n",
+    "else :\n",
+    "  print(H0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Kiểm định chi – square về mối tương quan giữa tuổi và bệnh tim:</strong><br>\n",
+    "<ul>\n",
+    "<li>\n",
+    "H0 : “Không có mối tương quan giữa các nhóm tuổi và bệnh tim mạch.”\n",
+    "</li>\n",
+    "<li>\n",
+    "H1 : “Có mối tương quan giữa các nhóm tuổi và bệnh tim mạch.”\n",
+    "</li>\n",
+    "</ul>\n",
+    "\n",
+    "</div>\n",
+    "<div style=\"display: flex;\n",
+    "            justify-content: center;\n",
+    "            align-items: center;\">\n",
+    "<img src=\"https://i.imgur.com/jPg1eXC.png\" alt=\"Mô tả hình ảnh\" style=\"weight: 500px; height:500px\">\n",
+    "</div>\n",
+    "<div class=\"symple-box yellow center \" style=\"background: #fffdf3; border-radius: 2px; border: 1px solid #f2dfa4; box-sizing: border-box; color: #c4690e; float: none; font-family: Arial, sans-serif; font-size: 22px; margin: 0px auto; outline: 0px; padding: 15px 20px; vertical-align: baseline;\"><strong style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Kết quả của kiểm định chi – square về mối tương quan giữa hút thuốc và bệnh tim</strong>:\n",
+    "<ul style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px 0px 20px 20px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "Chi-Square value: 44.0267323134354</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Degree of Freedom:  3</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">p-value: 0.000000001489578</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Có mối tương quan giữa việc hút thuốc và bệnh tim.</li>\n",
+    "</ul>\n",
+    "</div>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "\n",
+    "\n",
+    "<strong>Kết luận:</strong>  <br>\n",
+    "<ul>\n",
+    "<li>\n",
+    "Việc hút thuốc làm tăng khả năng bị bệnh tim, điều này đúng với nghiên cứu của y khoa.\n",
+    "</li>\n",
+    "<li>\n",
+    "Nhưng với nhóm đã từng hút thuốc lại có tỉ lệ mắc bệnh tim mạch cao hơn so với nhóm hút thuốc. Điều này không phù hợp với nghiên cứu của y khoa. Điều này có khả năng còn phụ thuộc vào thời gian hút thuốc trước đó và thời gian bỏ hút thuốc, nhưng các dữ liệu này không được thể hiện trong bộ dữ liệu nên không thể đưa ra kết luận chính xác.\n",
+    "</li>\n",
+    "</ul>\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 38px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 80px;\n",
+    "  background-position: 0 100%;\">3.3. Mối tương quan giữa cao huyết áp và một số đặc trưng khác:</h1>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 32px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 60px;\n",
+    "  background-position: 0 100%;\">3.3.1. Cao huyết áp và tuổi </h1>\n",
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<ul>\n",
+    "<li><strong>Theo nghiên cứu của Thư viện Y khoa Quốc gia Hoa Kỳ:</strong> cao huyết áp có tỉ lệ mắc tăng đáng kể khi tuổi ngày càng tăng. Điều này là do khi con người về già, thành động mạch bị lão hóa, giảm tính đàn hồi và trở nên cứng hơn, tăng tích lũy mỡ dẫn đến xơ mỡ động mạch, làm tăng khả năng bị cao huyết áp.</li>\n",
+    "<li><strong>Kiểm định tính xác thực: </strong>Chia bộ dữ liệu thành 2 nhóm bị cao huyết áp và không bị cao huyết áp, theo dõi sự phân bố của độ tuổi giữa hai nhóm này.\n",
+    "</li>\n",
+    "</ul>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x240 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x240 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_hypertension_1 = df[df['hypertension'] == 1]\n",
+    "df_hypertension_0 = df[df['hypertension'] == 0]\n",
+    "draw_box(df_hypertension_0, 'age', 'hypertension = 0')\n",
+    "draw_box(df_hypertension_1, 'age', 'hypertension = 1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Quan sát biểu đồ:</strong> <br>\n",
+    "<ul>\n",
+    "<li>Nhóm không bị bệnh tim: chỉ số tuổi tác Q1 = 23, Q2 = 42, Q3 = 59</li> \n",
+    "<li>Nhóm bị bệnh tim: chỉ số tuổi tác Q1 = 53, Q2 = 64, Q3 = 75. Có đến trên 75% người bị bệnh tim từ 53 tuổi trở lên, đây là người ở độ tuổi trung niên và người già</li> \n",
+    "<li>Các giá trị Q1, Q2, Q3 của nhóm bị bệnh tim cao lên hẳn so với nhóm không bị bệnh tim. Điều này cho thấy đa phần những người bị cao huyết áp thuộc nhóm người cao tuổi.</li>\n",
+    "</ul>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chi-Square value: 334.14515522475114\n",
+      "Degree of Freedom:  3\n",
+      "p-value: 0.000000000000000\n",
+      "Có mối tương quan giữa các nhóm tuổi và cao huyết áp\n"
+     ]
+    }
+   ],
+   "source": [
+    "child_df = df[(df['age'] >= 0) & (df['age'] <= 18)]\n",
+    "teenager_df = df[(df['age'] > 18) & (df['age'] <= 30)]\n",
+    "middle_df = df[(df['age'] > 30) & (df['age'] <= 45)]\n",
+    "old_df = df[df['age'] > 45]\n",
+    "child_hyp_count = child_df[child_df['hypertension'] == 1].shape[0]\n",
+    "child_no_hyp_count = child_df[child_df['hypertension'] == 0].shape[0]\n",
+    "teen_hyp_count = teenager_df[teenager_df['hypertension'] == 1].shape[0]\n",
+    "teen_no_hyp_count = teenager_df[teenager_df['hypertension'] == 0].shape[0]\n",
+    "mid_hyp_count = middle_df[middle_df['hypertension'] == 1].shape[0]\n",
+    "mid_no_hyp_count = middle_df[middle_df['hypertension'] == 0].shape[0]\n",
+    "old_hyp_count = old_df[old_df['hypertension'] == 1].shape[0]\n",
+    "old_no_hyp_count = old_df[old_df['hypertension'] == 0].shape[0]\n",
+    "\n",
+    "H0 = \"Không có mối tương quan giữa các nhóm tuổi và cao huyết áp\"\n",
+    "H1 = \"Có mối tương quan giữa các nhóm tuổi và cao huyết áp\"\n",
+    "\n",
+    "\n",
+    "child_arr = [child_hyp_count, child_no_hyp_count]\n",
+    "teen_arr = [teen_hyp_count, teen_no_hyp_count]\n",
+    "middle_arr = [mid_hyp_count, mid_no_hyp_count]\n",
+    "old_arr = [old_hyp_count, old_no_hyp_count]\n",
+    "\n",
+    "nij = []\n",
+    "nij.append(child_arr)\n",
+    "nij.append(teen_arr)\n",
+    "nij.append(middle_arr)\n",
+    "nij.append(old_arr )\n",
+    "nij = np.array(nij)\n",
+    "\n",
+    "total_sum = np.sum(nij)\n",
+    "row_sums = np.sum(nij, axis=1)\n",
+    "col_sums = np.sum(nij, axis=0)\n",
+    "expected_ij = np.outer(row_sums, col_sums) / total_sum\n",
+    "\n",
+    "chi_square = np.sum((nij - expected_ij)**2 / expected_ij)\n",
+    "print(\"Chi-Square value:\", chi_square)\n",
+    "dof = (2 - 1)*(4 - 1)\n",
+    "print(\"Degree of Freedom: \", dof)\n",
+    "p_smoke = 1 - stats.chi2.cdf(chi_square,dof)\n",
+    "alpha = 0.05\n",
+    "print(\"p-value: {:.15f}\".format(p_smoke))\n",
+    "if p_smoke <= 0.05:\n",
+    "  print(H1)\n",
+    "else :\n",
+    "  print(H0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Kiểm định chi – square với mức ý nghĩa 5%:</strong><br>\n",
+    "<ul>\n",
+    "<li>\n",
+    "H0 : “Không có mối tương quan giữa các nhóm tuổi và bệnh cao huyết áp.”\n",
+    "</li>\n",
+    "<li>\n",
+    "H1 : “Có mối tương quan giữa các nhóm tuổi và bệnh cao huyết áp.”\n",
+    "</li>\n",
+    "</ul>\n",
+    "\n",
+    "</div>\n",
+    "<div style=\"display: flex;\n",
+    "            justify-content: center;\n",
+    "            align-items: center;\">\n",
+    "<img src=\"https://i.imgur.com/afElHdm.png\" alt=\"Mô tả hình ảnh\" style=\"weight: 500px; height:500px\">\n",
+    "</div>\n",
+    "<div class=\"symple-box yellow center \" style=\"background: #fffdf3; border-radius: 2px; border: 1px solid #f2dfa4; box-sizing: border-box; color: #c4690e; float: none; font-family: Arial, sans-serif; font-size: 22px; margin: 0px auto; outline: 0px; padding: 15px 20px; vertical-align: baseline;\"><strong style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Kết quả của kiểm định chi – square về mối tương quan giữa tuổi và cao huyết áp</strong>:\n",
+    "<ul style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px 0px 20px 20px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "Chi-Square value: 334.14515522475114</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Degree of Freedom:  3</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">p-value: 0.000000000000000</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Có mối tương quan giữa các nhóm tuổi và cao huyết áp</li>\n",
+    "</ul>\n",
+    "</div>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "\n",
+    "<ul>\n",
+    "<li>\n",
+    "Do p-value bé hơn mức ý nghĩa, nên ta bác bỏ giả thuyết H0\n",
+    "</li>\n",
+    "<li>\n",
+    "Với mức ý nghĩa 5%, ta thấy có mối tương quan giữa các nhóm tuổi và bệnh cao huyết áp.\n",
+    "</li>\n",
+    "</ul>\n",
+    "<strong>Kết luận:</strong>  Tập dữ liệu thể hiện người cao tuổi có nguy cơ cao bị cao huyết áp, điều này đúng với nghiên cứu của y khoa.\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 32px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 60px;\n",
+    "  background-position: 0 100%;\">3.3.2. Bệnh tim và cao huyết áp: </h1>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<ul>\n",
+    "<li><strong>Theo nghiên cứu của Theo Thư viện Y khoa Quốc gia Hoa Kỳ:</strong> Người bị cao huyết áp có nguy cơ mắc bệnh tim mạch cao</li>\n",
+    "<li><strong>Kiểm định tính xác thực: </strong>Chia bộ dữ liệu thành 2 nhóm bị cao huyết áp và không bị cao huyết áp, quan sát sự phân bố của đặc trưng bệnh tim giữa hai nhóm này\n",
+    "</li>\n",
+    "</ul>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.figure(figsize=(26, 8))\n",
+    "draw(df, 'heart_disease', 'hypertension')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Quan sát biểu đồ:</strong> Ở nhóm người không bị cao huyết áp có 4.69% bị bệnh tim. Ở nhóm người bị cao huyết áp có 13.36% bị bệnh tim. Hai con số này giữa hai nhóm có sự chênh lệch đáng kể\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected ij:\n",
+      "[[  26.44549287  248.55450713]\n",
+      " [ 452.55450713 4253.44549287]]\n",
+      "Chi-Square value: 62.45225691271743\n",
+      "Degree of Freedom:  1\n",
+      "p-value: 0.000000000000003\n",
+      "Có mối tương quan giữa bệnh tim và cao huyết áp.\n"
+     ]
+    }
+   ],
+   "source": [
+    "heart_hyp_count = df[(df['heart_disease'] == 1) & (df['hypertension'] == 1)].shape[0]\n",
+    "heart_no_hyp_count = df[(df['heart_disease'] == 1) & (df['hypertension'] == 0)].shape[0]\n",
+    "no_heart_hyp_count = df[(df['heart_disease'] == 0) & (df['hypertension'] == 1)].shape[0]\n",
+    "no_heart_no_hyp_count = df[(df['heart_disease'] == 0) & (df['hypertension'] == 0)].shape[0]\n",
+    "H0 = \"Không có mối tương quan giữa bệnh tim và cao huyết áp.\"\n",
+    "H1 = \"Có mối tương quan giữa bệnh tim và cao huyết áp.\"\n",
+    "\n",
+    "\n",
+    "heart_arr = [heart_hyp_count, heart_no_hyp_count]\n",
+    "noheart_arr = [no_heart_hyp_count , no_heart_no_hyp_count]\n",
+    "\n",
+    "\n",
+    "nij = []\n",
+    "nij.append(heart_arr)\n",
+    "nij.append(noheart_arr)\n",
+    "\n",
+    "nij = np.array(nij)\n",
+    "\n",
+    "total_sum = np.sum(nij)\n",
+    "row_sums = np.sum(nij, axis=1)\n",
+    "col_sums = np.sum(nij, axis=0)\n",
+    "expected_ij = np.outer(row_sums, col_sums) / total_sum\n",
+    "print(\"Expected ij:\")\n",
+    "print(expected_ij)\n",
+    "chi_square = np.sum((nij - expected_ij)**2 / expected_ij)\n",
+    "print(\"Chi-Square value:\", chi_square)\n",
+    "dof = (2 - 1)*(2 - 1)\n",
+    "print(\"Degree of Freedom: \", dof)\n",
+    "p_smoke = 1 - stats.chi2.cdf(chi_square,dof)\n",
+    "alpha = 0.05\n",
+    "print(\"p-value: {:.15f}\".format(p_smoke))\n",
+    "if p_smoke <= 0.05:\n",
+    "  print(H1)\n",
+    "else :\n",
+    "  print(H0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Kiểm định chi – square với mức ý nghĩa 5%:</strong><br>\n",
+    "<ul>\n",
+    "<li>\n",
+    "H0 : “Không có mối tương quan giữa bệnh tim mạch và bệnh cao huyết áp.”\n",
+    "</li>\n",
+    "<li>\n",
+    "H1 : “Có mối tương quan giữa bệnh tim mạch và bệnh cao huyết áp.”\n",
+    "</li>\n",
+    "</ul>\n",
+    "\n",
+    "</div>\n",
+    "<div style=\"display: flex;\n",
+    "            justify-content: center;\n",
+    "            align-items: center;\">\n",
+    "<img src=\"https://i.imgur.com/1sb4R3t.png\" alt=\"Mô tả hình ảnh\" style=\"weight: 400px; height:400px\">\n",
+    "</div>\n",
+    "<div class=\"symple-box yellow center \" style=\"background: #fffdf3; border-radius: 2px; border: 1px solid #f2dfa4; box-sizing: border-box; color: #c4690e; float: none; font-family: Arial, sans-serif; font-size: 22px; margin: 0px auto; outline: 0px; padding: 15px 20px; vertical-align: baseline;\"><strong style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Kết quả của kiểm định chi – square về mối tương quan giữa bệnh tim và cao huyết áp</strong>:\n",
+    "<ul style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px 0px 20px 20px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "Chi-Square value: 62.45225691271743</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Degree of Freedom:  1</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">p-value: 0.000000000000003</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Có mối tương quan giữa bệnh tim và cao huyết áp.</li>\n",
+    "</ul>\n",
+    "</div>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "\n",
+    "<ul>\n",
+    "<li>\n",
+    "Do p-value bé hơn mức ý nghĩa, nên ta bác bỏ giả thuyết H0\n",
+    "</li>\n",
+    "<li>\n",
+    "Với mức ý nghĩa 5%, ta thấy có mối tương quan giữa bệnh tim mạch và bệnh cao huyết áp..\n",
+    "</li>\n",
+    "</ul>\n",
+    "<strong>Kết luận:</strong>  Có mối tương quan giữa cao huyết áp và bệnh tim mạch. Kết quả quan sát từ biểu đồ nhóm người bị cao huyết áp có nguy cơ bị tim mạch cao hơn. Điều này phù hợp với kết quả nghiên cứu của y khoa\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 38px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 80px;\n",
+    "  background-position: 0 100%;\">3.4 Mối tương quan giữa đột quỵ và các đặc trưng khác:</h1>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 32px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 60px;\n",
+    "  background-position: 0 100%;\" >3.4.1. Đột quỵ và giới tính:</h1>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Theo nghiên cứu của Thư viện Y khoa Quốc gia Hoa Kỳ:</strong> Nam giới có nguy cơ bị đột quỵ nhiều hơn phụ nữ\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 2600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(26, 8))\n",
+    "draw(df, 'stroke', 'gender')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "\n",
+    "<strong>Quan sát biểu đồ:</strong> tỉ lệ bị đột quỵ giữa nam và nữ gần như không có quá nhiều sự khác biệt\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected ij:\n",
+      "[[ 103.26279863 1970.73720137]\n",
+      " [ 144.73720137 2762.26279863]]\n",
+      "Chi-Square value: 0.3918784193181082\n",
+      "Degree of Freedom:  1\n",
+      "p-value: 0.531313673871251\n",
+      "Không có mối tương quan giữa giới tính và đột quỵ\n"
+     ]
+    }
+   ],
+   "source": [
+    "stroke_male_count = df[(df['gender'] == 'Male') & (df['stroke'] == 1)].shape[0]\n",
+    "no_stroke_male_count = df[(df['gender'] == 'Male') & (df['stroke'] == 0)].shape[0]\n",
+    "stroke_female_count = df[(df['gender'] == 'Female') & (df['stroke'] == 1)].shape[0]\n",
+    "no_stroke_female_count = df[(df['gender'] == 'Female') & (df['stroke'] == 0)].shape[0]\n",
+    "H0 = \"Không có mối tương quan giữa giới tính và đột quỵ\"\n",
+    "H1 = \"Có mối tương quan giữa giới tính và đột quỵ\"\n",
+    "male_arr = [stroke_male_count, no_stroke_male_count]\n",
+    "female_arr = [stroke_female_count, no_stroke_female_count]\n",
+    "nij = []\n",
+    "nij.append(male_arr)\n",
+    "nij.append(female_arr)\n",
+    "nij = np.array(nij)\n",
+    "total_sum = np.sum(nij)\n",
+    "row_sums = np.sum(nij, axis=1)\n",
+    "col_sums = np.sum(nij, axis=0)\n",
+    "expected_ij = np.outer(row_sums, col_sums) / total_sum\n",
+    "print(\"Expected ij:\")\n",
+    "print(expected_ij)\n",
+    "chi_square = np.sum((nij - expected_ij)**2 / expected_ij)\n",
+    "print(\"Chi-Square value:\", chi_square)\n",
+    "dof = (2 - 1)*(2 - 1)\n",
+    "print(\"Degree of Freedom: \", dof)\n",
+    "p_gender = 1 - stats.chi2.cdf(chi_square,dof)\n",
+    "alpha = 0.05\n",
+    "print(\"p-value: {:.15f}\".format(p_gender))\n",
+    "if p_gender <= 0.05:\n",
+    "  print(H1)\n",
+    "else :\n",
+    "  print(H0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Kiểm định chi – square với mức ý nghĩa 5%:</strong><br>\n",
+    "<ul>\n",
+    "<li>\n",
+    "H0 : “Không có mối tương quan giữa giới tính và bệnh đột quỵ.”\n",
+    "</li>\n",
+    "<li>\n",
+    "H1 : “Có mối tương quan giữa giới tính và bệnh đột quỵ.”\n",
+    "</li>\n",
+    "</ul>\n",
+    "\n",
+    "</div>\n",
+    "<div style=\"display: flex;\n",
+    "            justify-content: center;\n",
+    "            align-items: center;\">\n",
+    "<img src=\"https://i.imgur.com/AVKlET5.png\" alt=\"Mô tả hình ảnh\" style=\"weight: 400px; height:400px\">\n",
+    "</div>\n",
+    "<div class=\"symple-box yellow center \" style=\"background: #fffdf3; border-radius: 2px; border: 1px solid #f2dfa4; box-sizing: border-box; color: #c4690e; float: none; font-family: Arial, sans-serif; font-size: 22px; margin: 0px auto; outline: 0px; padding: 15px 20px; vertical-align: baseline;\"><strong style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Kết quả của kiểm định chi – square về mối tương quan giữa giới tính và bệnh đột quỵ</strong>:\n",
+    "<ul style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px 0px 20px 20px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "Chi-Square value: 0.3918784193181082</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Degree of Freedom:  1</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">p-value: 0.531313673871251</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Không có mối tương quan giữa giới tính và đột quỵ</li>\n",
+    "</ul>\n",
+    "</div>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "\n",
+    "<ul>\n",
+    "<li>\n",
+    "Do p-value lớn hơn mức ý nghĩa, nên ta không thể bác bỏ giả thuyết H0\n",
+    "</li>\n",
+    "<li>\n",
+    "Với mức ý nghĩa 5%, ta thấy không có mối tương quan giữa giới tính và bệnh đột quỵ.\n",
+    "</li>\n",
+    "</ul>\n",
+    "<strong>Kết luận:</strong>  Giới tính không có sự ảnh hưởng đến đột quỵ. Điều này không phù hợp với kết quả nghiên cứu\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 32px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 60px;\n",
+    "  background-position: 0 100%;\">3.4.2.Đột quỵ và tuổi tác </h1>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "\n",
+    "<strong>Theo nghiên cứu của Thư viện Y khoa Quốc gia Hoa Kỳ:</strong> Lão hóa có liên quan đến nhiều thay đổi đáng chú ý ở động mạch não trong và ngoài sọ người. Đột quỵ có thể xảy ra ở mọi lứa tuổi nhưng càng cao tuổi thì nguy cơ bị đột quỵ càng tăng và tăng rõ rệt sau 50 tuổi. \n",
+    "<br>\n",
+    "<strong>Kiểm định tính xác thực:</strong> chia bộ dữ liệu thành 2 phần, bị đột quỵ và không bị đột quỵ sau đó quan sát sự phân bố độ tuổi giữa 2 nhóm\n",
+    "\n",
+    "\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 2600x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plt.figure(figsize=(5,5))\n",
+    "fig, axs = plt.subplots(1, 2, figsize=(26, 5))\n",
+    "sns.histplot(data=df[df['stroke'] == 0], x='age', bins = 'auto', ax=axs[0], color = sns.color_palette(\"pastel\", 1)[0])\n",
+    "axs[0].set_title('Stroke: false', fontsize=16, y=1.05)\n",
+    "\n",
+    "sns.histplot(data=df[df['stroke'] == 1], x='age', bins = 'auto', ax=axs[1], color = sns.color_palette(\"pastel\", 1)[0])\n",
+    "axs[1].set_title('Stroke: true', fontsize=16, y=1.05)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:24px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Quan sát biểu đồ:</strong> Nhóm không bị đột quỵ, phân phối giữa các nhóm tuổi có độ dàn trải. Nhóm bị đột quỵ: Có 2 giá trị ngoại lai trong khoảng tuổi dưới 20 tuổi, 2 giá trị ngoại lai này có số lượng không đáng kể. Dữ liệu của biểu đồ có phân phối bị nghiêng về phía bên phải. Số lượng người mắc đột quỵ phân bố tăng dần theo số tuổi, trước 30 tuổi có rất ít trường hợp đột quỵ; sau 30 tuổi tỉ lệ bị đột quỵ tăng dần qua các năm và tăng mạnh nhất sau 50 tuổi.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected ij:\n",
+      "[[  43.81449508  836.18550492]\n",
+      " [  31.86508733  608.13491267]\n",
+      " [  50.68540454  967.31459546]\n",
+      " [ 121.63501305 2321.36498695]]\n",
+      "Chi-Square value: 223.0649448330483\n",
+      "Degree of Freedom:  3\n",
+      "p-value: 0.000000000000000\n",
+      "Có mối tương quan giữa các nhóm tuổi và đột quỵ\n"
+     ]
+    }
+   ],
+   "source": [
+    "old_stroke_count = old_df[old_df['stroke'] == 1].shape[0]\n",
+    "old_nostroke_count = old_df[old_df['stroke'] == 0].shape[0]\n",
+    "child_stroke_count = child_df[child_df['stroke'] == 1].shape[0]\n",
+    "child_nostroke_count = child_df[child_df['stroke'] == 0].shape[0]\n",
+    "teen_stroke_count = teenager_df[teenager_df['stroke'] == 1].shape[0]\n",
+    "teen_nostroke_count = teenager_df[teenager_df['stroke'] == 0].shape[0]\n",
+    "mid_stroke_count = middle_df[middle_df['stroke'] == 1].shape[0]\n",
+    "mid_nostroke_count = middle_df[middle_df['stroke'] == 0].shape[0]\n",
+    "H0 = \"Không có mối tương quan giữa các nhóm tuổi và đột quỵ\"\n",
+    "H1 = \"Có mối tương quan giữa các nhóm tuổi và đột quỵ\"\n",
+    "\n",
+    "child_arr = [child_stroke_count, child_nostroke_count]\n",
+    "teen_arr = [teen_stroke_count, teen_nostroke_count]\n",
+    "middle_arr = [mid_stroke_count, mid_nostroke_count]\n",
+    "old_arr = [old_stroke_count, old_nostroke_count]\n",
+    "\n",
+    "nij = []\n",
+    "nij.append(child_arr)\n",
+    "nij.append(teen_arr)\n",
+    "nij.append(middle_arr)\n",
+    "nij.append(old_arr )\n",
+    "nij = np.array(nij)\n",
+    "\n",
+    "total_sum = np.sum(nij)\n",
+    "row_sums = np.sum(nij, axis=1)\n",
+    "col_sums = np.sum(nij, axis=0)\n",
+    "expected_ij = np.outer(row_sums, col_sums) / total_sum\n",
+    "print(\"Expected ij:\")\n",
+    "print(expected_ij)\n",
+    "chi_square = np.sum((nij - expected_ij)**2 / expected_ij)\n",
+    "print(\"Chi-Square value:\", chi_square)\n",
+    "dof = (2 - 1)*(4 - 1)\n",
+    "print(\"Degree of Freedom: \", dof)\n",
+    "p_age = 1 - stats.chi2.cdf(chi_square,dof)\n",
+    "alpha = 0.05\n",
+    "print(\"p-value: {:.15f}\".format(p_age))\n",
+    "if p_age <= 0.05:\n",
+    "  print(H1)\n",
+    "else :\n",
+    "  print(H0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Kiểm định chi – square với mức ý nghĩa 5%:</strong><br>\n",
+    "<ul>\n",
+    "<li>\n",
+    "H0 : “Không có mối tương quan giữa độ tuổi và bệnh đột quỵ.”\n",
+    "</li>\n",
+    "<li>\n",
+    "H1 : “Có mối tương quan giữa độ tuổi và bệnh đột quỵ.”\n",
+    "</li>\n",
+    "</ul>\n",
+    "\n",
+    "</div>\n",
+    "<div style=\"display: flex;\n",
+    "            justify-content: center;\n",
+    "            align-items: center;\">\n",
+    "<img src=\"https://i.imgur.com/lDWHBuw.png\" alt=\"Mô tả hình ảnh\" style=\"weight: 500px; height:500px\">\n",
+    "</div>\n",
+    "<div class=\"symple-box yellow center \" style=\"background: #fffdf3; border-radius: 2px; border: 1px solid #f2dfa4; box-sizing: border-box; color: #c4690e; float: none; font-family: Arial, sans-serif; font-size: 22px; margin: 0px auto; outline: 0px; padding: 15px 20px; vertical-align: baseline;\"><strong style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Kết quả của kiểm định chi – square về mối tương quan giữa tuổi và đột quỵ</strong>:\n",
+    "<ul style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px 0px 20px 20px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "Chi-Square value: 223.0649448330483</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Degree of Freedom:  3</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">p-value: 0.000000000000000</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Có mối tương quan giữa các nhóm tuổi và đột quỵ</li>\n",
+    "</ul>\n",
+    "</div>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "\n",
+    "<ul>\n",
+    "<li>\n",
+    "Do p-value bé hơn mức ý nghĩa, nên ta bác bỏ giả thuyết H0\n",
+    "</li>\n",
+    "<li>\n",
+    "Với mức ý nghĩa 5%, ta thấy có mối tương quan giữa tuổi và bệnh đột quỵ.\n",
+    "</li>\n",
+    "</ul>\n",
+    "<strong>Kết luận:</strong>  Càng lớn tuổi nguy cơ mắc đột quỵ càng cao, điều này phù hợp với kết quả nghiên cứu của y khoa.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 32px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 60px;\n",
+    "  background-position: 0 100%;\">3.4.3. Đột quỵ và tình trạng hôn nhân </h1>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<ul>\n",
+    "<li>Ở bộ dữ liệu này độ tuổi bắt đầu xuất hiện người bị đột quỵ là từ 30 trở đi. Với từng khoảng tuổi từ 30 trở đi, chia thành 2 nhóm đã kết hôn và chưa kết hôn, sau đó quan sát sự phân bố của đột quỵ trên 2 nhóm này\n",
+    "\n",
+    "</li>\n",
+    "\n",
+    "</ul>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3000x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(30, 6))\n",
+    "\n",
+    "age_groups = [[30, 50], [50, 70], [70, 85]] \n",
+    "x = 'stroke'\n",
+    "hue = 'ever_married'\n",
+    "palette_color = 'pastel'\n",
+    "for i, age_group in enumerate(age_groups):\n",
+    "    tempDf = df[(df['age'] >= age_group[0]) & (df['age'] < age_group[1])]\n",
+    "    \n",
+    "    # draw(data, 'heart_disease', 'gender', 'age: ' + str(age_group[0]) + ' - ' + str(age_group[1]))\n",
+    "    \n",
+    "    data = tempDf[x].groupby(tempDf[hue]).value_counts(normalize=True).rename('percent').reset_index()\n",
+    "\n",
+    "    \n",
+    "    # Vẽ biểu đồ cộtkiii\n",
+    "    ax = sns.barplot(data=data, x=x, y='percent', hue=hue, ax = axs[i], palette = palette_color)\n",
+    "    axs[i].set_xlabel(x, fontsize=16)\n",
+    "    axs[i].set_ylabel('percent', fontsize=16)\n",
+    "    axs[i].set_title(f'Age: {age_group[0]} - {age_group[1]}', fontsize = 16)\n",
+    "\n",
+    "    # Thêm số trên đầu cột\n",
+    "    for p in ax.patches:\n",
+    "        if p.get_height() > 0:\n",
+    "            ax.annotate(format(p.get_height(), '.2%'), (p.get_x() + p.get_width() / 2., p.get_height()), \n",
+    "                    ha='center', va='center', xytext=(0, 7), textcoords='offset points',fontsize =16)\n",
+    "    \n",
+    "    axs[i].tick_params(axis='both', labelsize=18)\n",
+    "      \n",
+    "    \n",
+    "    axs[i].legend(title=hue, prop={'size': 16}, title_fontsize=16)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Quan sát biểu đồ:</strong> Ở mốc tuổi từ 30 - 50, tỉ lệ bị đột quỵ giữa người chưa kết hôn và người đã kết hôn là không đáng kể. Mốc tuổi từ 50 - 70, người chưa kết hôn có tỉ lệ bị đột quỵ cao hơn 1.3 lần so với người đã kết hôn. Mốc tuổi 70 - 80,  người chưa kết hôn có tỉ lệ bị đột quỵ cao gấp 1,31 lần so với người đã kết hôn. \n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected ij:\n",
+      "[[ 163.30857258 3116.69142742]\n",
+      " [  84.69142742 1616.30857258]]\n",
+      "Chi-Square value: 58.52750104665151\n",
+      "Degree of Freedom:  1\n",
+      "p-value: 0.000000000000020\n",
+      "Có mối tương quan giữa việc lập gia đình và đột quỵ\n"
+     ]
+    }
+   ],
+   "source": [
+    "stroke_married_count = df[(df['ever_married'] == 'Yes') & (df['stroke'] == 1)].shape[0]\n",
+    "nostroke_married_count = df[(df['ever_married'] == 'Yes') & (df['stroke'] == 0)].shape[0]\n",
+    "stroke_unmarried_count = df[(df['ever_married'] == 'No') & (df['stroke'] == 1)].shape[0]\n",
+    "nostroke_unmarried_count = df[(df['ever_married'] == 'No') & (df['stroke'] == 0)].shape[0]\n",
+    "H0 = \"Không có mối tương quan giữa việc lập gia đình và đột quỵ\"\n",
+    "H1 = \"Có mối tương quan giữa việc lập gia đình và đột quỵ\"\n",
+    "married_arr = [stroke_married_count , nostroke_married_count]\n",
+    "unmarried_arr = [stroke_unmarried_count , nostroke_unmarried_count]\n",
+    "nij = []\n",
+    "nij.append(married_arr)\n",
+    "nij.append(unmarried_arr)\n",
+    "nij = np.array(nij)\n",
+    "total_sum = np.sum(nij)\n",
+    "row_sums = np.sum(nij, axis=1)\n",
+    "col_sums = np.sum(nij, axis=0)\n",
+    "expected_ij = np.outer(row_sums, col_sums) / total_sum\n",
+    "print(\"Expected ij:\")\n",
+    "print(expected_ij)\n",
+    "chi_square = np.sum((nij - expected_ij)**2 / expected_ij)\n",
+    "print(\"Chi-Square value:\", chi_square)\n",
+    "dof = (2 - 1)*(2 - 1)\n",
+    "print(\"Degree of Freedom: \", dof)\n",
+    "p_married = 1 - stats.chi2.cdf(chi_square,dof)\n",
+    "alpha = 0.05\n",
+    "print(\"p-value: {:.15f}\".format(p_married))\n",
+    "if p_married <= 0.05:\n",
+    "  print(H1)\n",
+    "else :\n",
+    "  print(H0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Kiểm định chi – square với mức ý nghĩa 5%:</strong><br>\n",
+    "<ul>\n",
+    "<li>\n",
+    "H0 : “Không có mối tương quan giữa việc lập gia đình và bệnh đột quỵ.”\n",
+    "</li>\n",
+    "<li>\n",
+    "H1 : “Có mối tương quan giữa việc lập gia đình và bệnh đột quỵ.”\n",
+    "</li>\n",
+    "</ul>\n",
+    "\n",
+    "</div>\n",
+    "<div style=\"display: flex;\n",
+    "            justify-content: center;\n",
+    "            align-items: center;\">\n",
+    "<img src=\"https://i.imgur.com/nQ2BqvT.png\" alt=\"Mô tả hình ảnh\" style=\"weight: 400px; height:400px\">\n",
+    "</div>\n",
+    "<div class=\"symple-box yellow center \" style=\"background: #fffdf3; border-radius: 2px; border: 1px solid #f2dfa4; box-sizing: border-box; color: #c4690e; float: none; font-family: Arial, sans-serif; font-size: 22px; margin: 0px auto; outline: 0px; padding: 15px 20px; vertical-align: baseline;\"><strong style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Kết quả của kiểm định chi – square về mối tương quan giữa tình trạng hôn nhân và đột quỵ</strong>:\n",
+    "<ul style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px 0px 20px 20px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "Chi-Square value: 58.52750104665151</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Degree of Freedom:  1</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">p-value: 0.000000000000020</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Có mối tương quan giữa việc lập gia đình và đột quỵ</li>\n",
+    "</ul>\n",
+    "</div>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "\n",
+    "<ul>\n",
+    "<li>\n",
+    "Do p-value bé hơn mức ý nghĩa, nên ta bác bỏ giả thuyết H0\n",
+    "</li>\n",
+    "<li>\n",
+    "Với mức ý nghĩa 5%, ta thấy có mối tương quan giữa việc lập gia đình và bệnh đột quỵ\n",
+    "</li>\n",
+    "</ul>\n",
+    "<strong>Kết luận:</strong>  Kết hôn và đột quỵ có mối tương quan với nhau. Nhìn chung từ mốc 50 tuổi trở đi, nhóm người không kết hôn có khả năng tăng nguy cơ đột quỵ cao hơn một chút so với nhóm đã kết hôn\n",
+    "</div>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 32px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 60px;\n",
+    "  background-position: 0 100%;\">3.4.4. Đột quỵ và nơi ở </h1>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:24px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "Chia bộ dữ liệu thành thành 2 nhóm ở thành thị và nông thôn, quan sát tỉ lệ đột quỵ ở hai nhóm.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 2600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.figure(figsize=(26, 8))\n",
+    "draw(df, 'stroke', 'Residence_type')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:24px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "Tỉ lệ đột quỵ của thành thị là 5.33%, cao gấp 1.15 lần so với tỉ lệ đột quỵ của nông thôn. Sự khác biệt này là không quá rõ ràng.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected ij:\n",
+      "[[ 126.06625176 2405.93374824]\n",
+      " [ 121.93374824 2327.06625176]]\n",
+      "Chi-Square value: 1.3551156873012458\n",
+      "Degree of Freedom:  1\n",
+      "p-value:  0.24438577258460314\n",
+      "Không có mối tương quan giữa nơi ở và đột quỵ\n"
+     ]
+    }
+   ],
+   "source": [
+    "stroke_urban_count = df[(df['Residence_type'] == 'Urban') & (df['stroke'] == 1)].shape[0]\n",
+    "nostroke_urban_count = df[(df['Residence_type'] == 'Urban') & (df['stroke'] == 0)].shape[0]\n",
+    "stroke_rural_count = df[(df['Residence_type'] == 'Rural') & (df['stroke'] == 1)].shape[0]\n",
+    "nostroke_rural_count = df[(df['Residence_type'] == 'Rural') & (df['stroke'] == 0)].shape[0]\n",
+    "H0 = \"Không có mối tương quan giữa nơi ở và đột quỵ\"\n",
+    "H1 = \"Có mối tương quan giữa nơi ở và đột quỵ\"\n",
+    "urban_arr = [stroke_urban_count, nostroke_urban_count]\n",
+    "rural_arr = [stroke_rural_count, nostroke_rural_count]\n",
+    "nij = []\n",
+    "nij.append(urban_arr )\n",
+    "nij.append(rural_arr)\n",
+    "nij = np.array(nij)\n",
+    "total_sum = np.sum(nij)\n",
+    "row_sums = np.sum(nij, axis=1)\n",
+    "col_sums = np.sum(nij, axis=0)\n",
+    "expected_ij = np.outer(row_sums, col_sums) / total_sum\n",
+    "print(\"Expected ij:\")\n",
+    "print(expected_ij)\n",
+    "chi_square = np.sum((nij - expected_ij)**2 / expected_ij)\n",
+    "print(\"Chi-Square value:\", chi_square)\n",
+    "dof = (2 - 1)*(2 - 1)\n",
+    "print(\"Degree of Freedom: \", dof)\n",
+    "p_residence = 1 - stats.chi2.cdf(chi_square,dof)\n",
+    "alpha = 0.05\n",
+    "print(\"p-value: \", p_residence)\n",
+    "if p_residence <= 0.05:\n",
+    "  print(H1)\n",
+    "else :\n",
+    "  print(H0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Kiểm định Chi-square với mức ý nghĩa 5%:</strong><br>\n",
+    "<ul>\n",
+    "<li>\n",
+    "H0 : “Không có mối tương quan giữa nơi ở và bệnh đột quỵ.”\n",
+    "</li>\n",
+    "<li>\n",
+    "H1 : “Có mối tương quan giữa nơi ở và bệnh đột quỵ.”\n",
+    "</li>\n",
+    "</ul>\n",
+    "\n",
+    "</div>\n",
+    "<div style=\"display: flex;\n",
+    "            justify-content: center;\n",
+    "            align-items: center;\">\n",
+    "<img src=\"https://i.imgur.com/fExoluD.png\" alt=\"Mô tả hình ảnh\" style=\"weight: 400px; height:400px\">\n",
+    "</div>\n",
+    "<div class=\"symple-box yellow center \" style=\"background: #fffdf3; border-radius: 2px; border: 1px solid #f2dfa4; box-sizing: border-box; color: #c4690e; float: none; font-family: Arial, sans-serif; font-size: 22px; margin: 0px auto; outline: 0px; padding: 15px 20px; vertical-align: baseline;\"><strong style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Kết quả của kiểm định chi – square về mối tương quan giữa đột quỵ và nơi ở</strong>:\n",
+    "<ul style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px 0px 20px 20px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "Chi-Square value: 1.3551156873012458</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Degree of Freedom:  1</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">p-value:  0.24438577258460314</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Không có mối tương quan giữa nơi ở và đột quỵ</li>\n",
+    "</ul>\n",
+    "</div>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "\n",
+    "<ul>\n",
+    "<li>\n",
+    "Do p-value lớn hơn mức ý nghĩa, nên ta không thể bác bỏ giả thuyết H0\n",
+    "</li>\n",
+    "<li>\n",
+    "Với mức ý nghĩa 5%, ta thấy không có mối tương quan giữa nơi ở và bệnh đột quỵ\n",
+    "</li>\n",
+    "</ul>\n",
+    "<strong>Kết luận:</strong>  Nơi ở không có mối tương quan với bệnh đột quỵ, sự khác biệt giữa tỉ lệ người thành thị và nông thôn mắc bệnh đột quỵ là không đáng kể\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 32px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 60px;\n",
+    "  background-position: 0 100%;\">3.4.5. Đột quỵ và loại việc làm: </h1>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "Quan sát sự phân bố của loại việc làm trong nhóm người bị đột quỵ.\n",
+    "\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_13748\\2844643837.py:2: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  ax = sns.countplot(data=df[df['stroke'] == 1], x='work_type', palette='pastel')\n",
+      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_13748\\2844643837.py:3: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  ax.set_xticklabels(ax.get_xticklabels(), fontsize=16)  # Tùy chỉnh kích thước chữ trên trục x\n",
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 2600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(26, 6))\n",
+    "ax = sns.countplot(data=df[df['stroke'] == 1], x='work_type', palette='pastel')\n",
+    "ax.set_xticklabels(ax.get_xticklabels(), fontsize=16)  # Tùy chỉnh kích thước chữ trên trục x\n",
+    "ax.legend().remove()  # Loại bỏ huy hiệu (legend)\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:24px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Quan sát biểu đồ trên</strong> ta thấy cao nhất là nhóm việc làm riêng tư, thấp nhất là nhóm trẻ em.<br> \n",
+    "Để có thể quan sát rõ hơn sự khác biệt giữa các nhóm việc làm, ta sẽ quan sát thêm thuộc tính tuổi. Với những người bị đột quỵ, thực hiện chia thành các nhóm theo từng loại công việc, tiến hành quan sát sự phân bố theo độ tuổi.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "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>ever_married</th>\n",
+       "      <th>stroke</th>\n",
+       "      <th>percent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.772727</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>No</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.227273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.826531</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.173469</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  ever_married  stroke   percent\n",
+       "0           No       0  0.772727\n",
+       "1           No       1  0.227273\n",
+       "2          Yes       0  0.826531\n",
+       "3          Yes       1  0.173469"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_13748\\1485882269.py:12: UserWarning: You are merging on int and float columns where the float values are not equal to their int representation.\n",
+      "  data2 = all_ages.merge(data2, on='age', how='left').fillna(0)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x2000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "work_types = ['children', 'Private', 'Self-employed', 'Govt_job']\n",
+    "fig, axs = plt.subplots(4, 1, figsize=(10, 20)) # sharey=True\n",
+    "max_y = 0\n",
+    "data2 =pd.DataFrame()\n",
+    "for i, work_type in enumerate(work_types):\n",
+    "    tempDf = df[(df['work_type'] == work_type) & (df['stroke'] == 1)]\n",
+    "    data = tempDf['age'].value_counts().reset_index().sort_values(by='index')\n",
+    "    data2['age'] = data['index']\n",
+    "    data2['index'] = data['age']\n",
+    "    \n",
+    "    all_ages = pd.DataFrame({'age': range(int(df['age'].min()), int(df['age'].max()) + 1)})\n",
+    "    data2 = all_ages.merge(data2, on='age', how='left').fillna(0)\n",
+    "    \n",
+    "    \n",
+    "    ax = sns.lineplot(data2, x = 'age', y = 'index', ax = axs[i], color = sns.color_palette(\"pastel\")[1])\n",
+    "    ax.set_title(work_type, fontsize = 15)\n",
+    "    max_y = max(max_y, data2['index'].max())\n",
+    "    \n",
+    "fig.subplots_adjust(hspace=0.5) \n",
+    "for ax in axs:\n",
+    "    ax.set_ylim(0, max_y + 1)\n",
+    "    ax.set_xticks(range(int(df['age'].min()), int(df['age'].max()) + 1, 5))\n",
+    "    ax.set_xticklabels(ax.get_xticks(), fontsize=12)\n",
+    "    ax.grid(axis='y', linestyle='dotted', alpha=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Quan sát biểu đồ: </strong><br>\n",
+    "<ul>\n",
+    "<li>Một lần nữa chúng ta thấy , với độ tuổi ngày càng tăng thì nguy cơ bị đột quỵ càng cao. \n",
+    "</li>\n",
+    "<li>Nhóm trẻ em: chỉ có một trường hợp bị đột quỵ, điều này là dễ hiểu vì trẻ em nằm trong độ tuổi dưới 16, mà ta đã biết ở độ tuổi càng cao thì nguy cơ bị đột quỵ càng tăng, điều này lý giải tại sao nhóm trẻ em có ít nguy cơ bị đột quỵ.\n",
+    "\n",
+    "</li>\n",
+    "\n",
+    "<li>Nhóm nhân viên chính phủ: Nhóm này có nguy cơ bị đột quỵ ít hơn hai nhóm riêng tư và nghề tự do. Nhóm này bắt đầu xuất hiện người bị đột quỵ từ 48 tuổi và dao động về số người đột quỵ các năm sau đó là không quá lớn\n",
+    "\n",
+    "</li>\n",
+    "<li>Nhóm nghề tự do: Nhóm này bắt đầu xuất hiện người bị đột quỵ từ mốc 38 tuổi, dao động về số người đột quỵ tăng mạnh trong đoạn 75 đến 80 tuổi. Ở độ tuổi 78 tuổi, số người đột quỵ lên cao nhất đạt 9 người.\n",
+    "</li>\n",
+    "<li>Nhóm riêng tư: bắt đầu xuất hiện người đột quỵ sau 32 tuổi và tăng mạnh khi tiến gần đến 80 tuổi. Ở tuổi 79, số người bị đột quỵ đạt cao nhất lên đến 13 người.\n",
+    "\n",
+    "</li>\n",
+    "<li>Ta nhận thấy ngoài nhóm trẻ em ra, ở 3 loại việc làm còn lại, số tuổi càng cao thì số người bị đột quỵ càng tăng. Điều này có ảnh hưởng trực tiếp đến sự phân bố số người đột quỵ giữa các nhóm. Ta biết khi ở độ tuổi càng cao thì sẽ tiến dần đến độ tuổi nghỉ hưu, lúc này người nghỉ hưu thường sẽ làm các công việc trong nhóm nghề tự do và riêng tư dẫn đến nhóm này tăng mạnh số người đột quỵ. Hoặc cũng có khả năng người bị bệnh đột quỵ thì có tình trạng sức khỏe không đáp ứng được các yêu cầu làm việc trong nhóm nhân viên chính phủ. Hay nói cách khác, nhóm nhân viên chính phủ có số lượng người đột quỵ thấp hơn 2 nhóm nghề tự do và riêng tư là do để có thể vào làm nhân viên chính phủ phải đạt đủ tiêu chuẩn sức khỏe, nên nhóm này có nguy cơ bị đột quỵ thấp hơn.\n",
+    "\n",
+    "\n",
+    "</li>\n",
+    "</ul>\n",
+    "\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected ij:\n",
+      "[[ 142.39710901 2717.60289099]\n",
+      " [  40.03051596  763.96948404]\n",
+      " [  32.06424413  611.93575587]\n",
+      " [  33.5081309   639.4918691 ]]\n",
+      "Chi-Square value: 47.83177224258346\n",
+      "Degree of Freedom:  3\n",
+      "p-value: 0.000000000231245 \n",
+      "Có mối tương quan giữa công việc và đột quỵ\n"
+     ]
+    }
+   ],
+   "source": [
+    "stroke_private_count = df[(df['work_type'] == 'Private') & (df['stroke'] == 1)].shape[0]\n",
+    "nostroke_private_count = df[(df['work_type'] == 'Private') & (df['stroke'] == 0)].shape[0]\n",
+    "stroke_selfemployed_count = df[(df['work_type'] == 'Self-employed') & (df['stroke'] == 1)].shape[0]\n",
+    "nostroke_selfemployed_count = df[(df['work_type'] == 'Self-employed') & (df['stroke'] == 0)].shape[0]\n",
+    "stroke_govt_count = df[(df['work_type'] == 'Govt_job') & (df['stroke'] == 1)].shape[0]\n",
+    "nostroke_govt_count = df[(df['work_type'] == 'Govt_job') & (df['stroke'] == 0)].shape[0]\n",
+    "stroke_children_count = df[(df['work_type'] == 'children') & (df['stroke'] == 1)].shape[0]\n",
+    "nostroke_children_count = df[(df['work_type'] == 'children') & (df['stroke'] == 0)].shape[0]\n",
+    "H0 = \"Không có mối tương quan giữa công việc và đột quỵ\"\n",
+    "H1 = \"Có mối tương quan giữa công việc và đột quỵ\"\n",
+    "private_arr = [stroke_private_count, nostroke_private_count]\n",
+    "selfemployed_arr = [stroke_selfemployed_count, nostroke_selfemployed_count]\n",
+    "govt_arr = [stroke_govt_count, nostroke_govt_count]\n",
+    "children_arr = [stroke_children_count, nostroke_children_count]\n",
+    "\n",
+    "nij = []\n",
+    "nij.append(private_arr)\n",
+    "nij.append(selfemployed_arr)\n",
+    "nij.append(govt_arr)\n",
+    "nij.append(children_arr)\n",
+    "nij = np.array(nij)\n",
+    "\n",
+    "total_sum = np.sum(nij)\n",
+    "row_sums = np.sum(nij, axis=1)\n",
+    "col_sums = np.sum(nij, axis=0)\n",
+    "expected_ij = np.outer(row_sums, col_sums) / total_sum\n",
+    "print(\"Expected ij:\")\n",
+    "print(expected_ij)\n",
+    "chi_square = np.sum((nij - expected_ij)**2 / expected_ij)\n",
+    "print(\"Chi-Square value:\", chi_square)\n",
+    "dof = (2 - 1)*(4 - 1)\n",
+    "print(\"Degree of Freedom: \", dof)\n",
+    "p_job = 1 - stats.chi2.cdf(chi_square,dof)\n",
+    "alpha = 0.05\n",
+    "print(\"p-value: {:.15f} \".format(p_job))\n",
+    "if p_job <= 0.05:\n",
+    "  print(H1)\n",
+    "else :\n",
+    "  print(H0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Kiểm định Chi-square với mức ý nghĩa 5%:</strong><br>\n",
+    "<ul>\n",
+    "<li>\n",
+    "H0 : “Không có mối tương quan giữa công việc và bệnh đột quỵ.”\n",
+    "</li>\n",
+    "<li>\n",
+    "H1 : “Có mối tương quan giữa công việc và bệnh đột quỵ.”\n",
+    "</li>\n",
+    "</ul>\n",
+    "\n",
+    "</div>\n",
+    "<div style=\"display: flex;\n",
+    "            justify-content: center;\n",
+    "            align-items: center;\">\n",
+    "<img src=\"https://i.imgur.com/9r5ZpfY.png\" alt=\"Mô tả hình ảnh\" style=\"weight: 500px; height:500px\">\n",
+    "</div>\n",
+    "<div class=\"symple-box yellow center \" style=\"background: #fffdf3; border-radius: 2px; border: 1px solid #f2dfa4; box-sizing: border-box; color: #c4690e; float: none; font-family: Arial, sans-serif; font-size: 22px; margin: 0px auto; outline: 0px; padding: 15px 20px; vertical-align: baseline;\"><strong style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Kết quả của kiểm định chi – square về mối tương quan giữa đột quỵ và loại việc làm</strong>:\n",
+    "<ul style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px 0px 20px 20px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "Chi-Square value: 47.83177224258346</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Degree of Freedom:  3</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">p-value: 0.000000000231245 </li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Có mối tương quan giữa công việc và đột quỵ</li>\n",
+    "</ul>\n",
+    "</div>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "\n",
+    "<ul>\n",
+    "<li>\n",
+    "Do p-value bé hơn mức ý nghĩa, nên ta bác bỏ giả thuyết H0\n",
+    "</li>\n",
+    "<li>\n",
+    "Với mức ý nghĩa 5%, ta thấy có mối tương quan giữa loại công việc và bệnh đột quỵ.\n",
+    "</li>\n",
+    "</ul>\n",
+    "<strong>Kết luận:</strong>  Có mối tương quan giữa loại việc làm và bệnh đột quỵ. Nhóm việc làm riêng tư và nghề tự do có khả năng đột quỵ cao, nhóm nhân viên chính phủ có khả năng bị đột quỵ thấp hơn, và rất hiếm có người đột quỵ ở nhóm trẻ em.\n",
+    "</div>\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 32px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 60px;\n",
+    "  background-position: 0 100%;\">3.4.6. Đột quỵ và tình trạng hút thuốc </h1>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Theo nghiên cứu của Thư viện Y khoa Quốc gia Hoa Kỳ:</strong> Những người hút thuốc có nguy cơ đột quỵ cao hơn so với những người không hút thuốc. Người hút thuốc lá trong một khoảng thời gian dài dẫn đến tăng huyết áp, từ đó khiến các mạch máu đến não bị tổn thương ở thành mạch. Lúc này, các chất béo, canxi, chất lắng đọng sẽ dễ dàng bám vào thành mạch và tạo thành các mảng xơ vữa, cục máu đông gây tắc mạch, hẹp mạch. Về lâu dài, khi lượng máu không cung cấp đủ cho não sẽ gây ra đột quỵ. \n",
+    "\n",
+    "<br>\n",
+    "Chia bộ dữ liệu thành hai 2 nhóm đột quỵ và không đột quỵ, quan sát sự phân bố giữa các loại tình trạng hút thuốc giữa 2 nhóm trên.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(26, 8))\n",
+    "draw(df,'smoking_status','stroke')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:24px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Quan sát biểu đồ: </strong><br>\n",
+    "<ul>\n",
+    "<li>Nhóm không hút thuốc: tỉ lệ bị đột quỵ ít hơn. Sự khác biệt giữa tỉ lệ bị đột quỵ và không bị đột quỵ là không quá lớn.\n",
+    "\n",
+    "</li>\n",
+    "<li>Nhóm hút thuốc: tỉ lệ bị đột quỵ cao hơn so với không bị đột quỵ, tuy nhiên sự khác biệt là không quá đáng kể.\n",
+    "\n",
+    "</li>\n",
+    "\n",
+    "<li>Nhóm không biết: tỉ lệ bị không bị đột quỵ là 30.7%, cao hơn 1.62 lần so với bị đột quỵ.\n",
+    "</li>\n",
+    "<li>Nhóm đã từng hút thuốc: tỉ lệ bị đột quỵ là 28.23%, cao hơn 1.67 lần so với không bị đột quỵ.\n",
+    "</li>\n",
+    "\n",
+    "</ul>\n",
+    "\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected ij:\n",
+      "[[  43.16723549  823.83276451]\n",
+      " [  91.51254768 1746.48745232]\n",
+      " [  38.63641839  737.36358161]\n",
+      " [  74.68379843 1425.31620157]]\n",
+      "Chi-Square value: 28.733513415037887\n",
+      "Degree of Freedom:  3\n",
+      "p-value: 0.000002547567825\n",
+      "Có mối tương quan giữa việc hút thuốc và đột quỵ\n"
+     ]
+    }
+   ],
+   "source": [
+    "stroke_formerlysmoked_count = df[(df['smoking_status'] == 'formerly smoked') & (df['stroke'] == 1)].shape[0]\n",
+    "nostroke_formerlysmoked_count = df[(df['smoking_status'] == 'formerly smoked') & (df['stroke'] == 0)].shape[0]\n",
+    "stroke_neversmoked_count = df[(df['smoking_status'] == 'never smoked') & (df['stroke'] == 1)].shape[0]\n",
+    "nostroke_neversmoked_count = df[(df['smoking_status'] == 'never smoked') & (df['stroke'] == 0)].shape[0]\n",
+    "stroke_smokes_count = df[(df['smoking_status'] == 'smokes') & (df['stroke'] == 1)].shape[0]\n",
+    "nostroke_smokes_count = df[(df['smoking_status'] == 'smokes') & (df['stroke'] == 0)].shape[0]\n",
+    "stroke_unknown_count = df[(df['smoking_status'] == 'Unknown') & (df['stroke'] == 1)].shape[0]\n",
+    "nostroke_unknown_count = df[(df['smoking_status'] == 'Unknown') & (df['stroke'] == 0)].shape[0]\n",
+    "H0 = \"Không có mối tương quan giữa việc hút thuốc và đột quỵ\"\n",
+    "H1 = \"Có mối tương quan giữa việc hút thuốc và đột quỵ\"\n",
+    "\n",
+    "\n",
+    "formerly_smoked_arr = [stroke_formerlysmoked_count, nostroke_formerlysmoked_count]\n",
+    "never_smoked_arr = [stroke_neversmoked_count, nostroke_neversmoked_count]\n",
+    "smokes_arr = [stroke_smokes_count, nostroke_smokes_count]\n",
+    "unknown_arr = [stroke_unknown_count, nostroke_unknown_count]\n",
+    "\n",
+    "nij = []\n",
+    "nij.append(formerly_smoked_arr)\n",
+    "nij.append(never_smoked_arr)\n",
+    "nij.append(smokes_arr)\n",
+    "nij.append(unknown_arr)\n",
+    "nij = np.array(nij)\n",
+    "\n",
+    "total_sum = np.sum(nij)\n",
+    "row_sums = np.sum(nij, axis=1)\n",
+    "col_sums = np.sum(nij, axis=0)\n",
+    "expected_ij = np.outer(row_sums, col_sums) / total_sum\n",
+    "print(\"Expected ij:\")\n",
+    "print(expected_ij)\n",
+    "chi_square = np.sum((nij - expected_ij)**2 / expected_ij)\n",
+    "print(\"Chi-Square value:\", chi_square)\n",
+    "dof = (2 - 1)*(4 - 1)\n",
+    "print(\"Degree of Freedom: \", dof)\n",
+    "p_smoke = 1 - stats.chi2.cdf(chi_square,dof)\n",
+    "alpha = 0.05\n",
+    "print(\"p-value: {:.15f}\".format(p_smoke))\n",
+    "if p_smoke <= 0.05:\n",
+    "  print(H1)\n",
+    "else :\n",
+    "  print(H0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Kiểm định Chi-square với mức ý nghĩa 5%:</strong><br>\n",
+    "<ul>\n",
+    "<li>\n",
+    "H0 : “Không có mối tương quan giữa việc hút thuốc và bệnh đột quỵ.”\n",
+    "</li>\n",
+    "<li>\n",
+    "H1 : “Có mối tương quan giữa việc hút thuốc và bệnh đột quỵ.”\n",
+    "</li>\n",
+    "</ul>\n",
+    "\n",
+    "</div>\n",
+    "<div style=\"display: flex;\n",
+    "            justify-content: center;\n",
+    "            align-items: center;\">\n",
+    "<img src=\"https://i.imgur.com/3Wb9Kwv.png\" alt=\"Mô tả hình ảnh\" style=\"weight: 500px; height:500px\">\n",
+    "</div>\n",
+    "<div class=\"symple-box yellow center \" style=\"background: #fffdf3; border-radius: 2px; border: 1px solid #f2dfa4; box-sizing: border-box; color: #c4690e; float: none; font-family: Arial, sans-serif; font-size: 22px; margin: 0px auto; outline: 0px; padding: 15px 20px; vertical-align: baseline;\"><strong style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Kết quả của kiểm định chi – square về mối tương quan giữa đột quỵ và tình trạng hút thuốc</strong>:\n",
+    "<ul style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px 0px 20px 20px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "Chi-Square value: 28.733513415037887</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Degree of Freedom:  3</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">p-value: 0.000002547567825 </li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Có mối tương quan giữa việc hút thuốc và đột quỵ</li>\n",
+    "</ul>\n",
+    "</div>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "\n",
+    "<ul>\n",
+    "<li>\n",
+    "Do p-value bé hơn mức ý nghĩa, nên ta bác bỏ giả thuyết H0\n",
+    "</li>\n",
+    "<li>\n",
+    "Với mức ý nghĩa 5%, ta thấy có mối tương quan giữa việc hút thuốc và bệnh đột quỵ\n",
+    "</li>\n",
+    "</ul>\n",
+    "<strong>Kết luận:</strong>  \n",
+    "<ul>\n",
+    "<li>\n",
+    "Đột quỵ và tình trạng hút thuốc có mối tương quan với nhau.\n",
+    "</li>\n",
+    "<li>\n",
+    "Hút thuốc có khả năng làm tăng nguy cơ bị đột quỵ. Điều này phù hợp với nghiên cứu của y khoa.\n",
+    "</li>\n",
+    "<li>\n",
+    "Ngừng hút thuốc lá làm tăng nguy cơ bị đột quỵ cao hơn so với nhóm hút thuốc. Điều này trái lại với nghiên cứu của y khoa, khi đáng lý ra việc bỏ hút thuốc mang lại các tác động tích cực cho sức khỏe và giảm các nguy cơ mắc đột quỵ hơn so với nhóm hút thuốc. Điều này còn phụ thuộc vào thời gian hút thuốc và thời gian bỏ hút thuốc. Nhưng các dữ liệu này không được thể hiện trong bộ dữ liệu nên không thể đưa ra kết luận chính xác.\n",
+    "</li>\n",
+    "</ul>\n",
+    "</div>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 32px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 60px;\n",
+    "  background-position: 0 100%;\">3.4.6. Đột quỵ và cao huyết áp: </h1>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Theo nghiên cứu của Thư viện Y khoa Quốc gia Hoa Kỳ,</strong> cao huyết áp luôn gây những ảnh hưởng sâu sắc đến cấu trúc của mạch máu não. Tăng huyết áp thúc đẩy sự phát triển của mảng xơ vữa động mạch trên động mạch não và tiểu động mạch, có thể dẫn đến tắc động mạch và tổn thương do thiếu máu cục bộ. Đồng thời, tăng huyết áp cũng dẫn đến chứng xơ cứng mạch máu, dẫn đến tăng áp lực mạch, trở thành một yếu tố dự báo đột quỵ.\n",
+    "<br>\n",
+    "Chia bộ dữ liệu thành 2 nhóm bị cao huyết áp và không bị cao huyết áp, quan sát sự phân bố của người bị đột quỵ và không đột quỵ trên 2 nhóm trên.\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 2600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(26, 8))\n",
+    "draw(df,'stroke','hypertension')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Quan sát biểu đồ: </strong>Tỉ lệ bị đột quỵ của nhóm bị cao huyết áp là 13,78%, cao gấp 3.4 lần so với tỉ lệ bị đột quỵ của nhóm không bị cao huyết áp\n",
+    "\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected ij:\n",
+      "[[  23.8490263  455.1509737]\n",
+      " [ 224.1509737 4277.8490263]]\n",
+      "Chi-Square value: 86.74324378010591\n",
+      "Degree of Freedom:  1\n",
+      "p-value: 0.000000000000000\n",
+      "Có mối tương quan giữa cao huyết áp và đột quỵ\n"
+     ]
+    }
+   ],
+   "source": [
+    "stroke_htension_count = df[(df['hypertension'] == 1) & (df['stroke'] == 1)].shape[0]\n",
+    "nostroke_htension_count = df[(df['hypertension'] == 1) & (df['stroke'] == 0)].shape[0]\n",
+    "stroke_no_htension_count = df[(df['hypertension'] == 0) & (df['stroke'] == 1)].shape[0]\n",
+    "nostroke_no_htension_count = df[(df['hypertension'] == 0) & (df['stroke'] == 0)].shape[0]\n",
+    "H0 = \"Không có mối tương quan giữa cao huyết áp và đột quỵ\"\n",
+    "H1 = \"Có mối tương quan giữa cao huyết áp và đột quỵ\"\n",
+    "hypertension_arr = [stroke_htension_count,nostroke_htension_count]\n",
+    "no_hypertension_arr = [stroke_no_htension_count, nostroke_no_htension_count]\n",
+    "nij = []\n",
+    "nij.append(hypertension_arr)\n",
+    "nij.append(no_hypertension_arr)\n",
+    "nij = np.array(nij)\n",
+    "total_sum = np.sum(nij)\n",
+    "row_sums = np.sum(nij, axis=1)\n",
+    "col_sums = np.sum(nij, axis=0)\n",
+    "expected_ij = np.outer(row_sums, col_sums) / total_sum\n",
+    "print(\"Expected ij:\")\n",
+    "print(expected_ij)\n",
+    "chi_square = np.sum((nij - expected_ij)**2 / expected_ij)\n",
+    "print(\"Chi-Square value:\", chi_square)\n",
+    "dof = (2 - 1)*(2 - 1)\n",
+    "print(\"Degree of Freedom: \", dof)\n",
+    "p_hypertension = 1 - stats.chi2.cdf(chi_square,dof)\n",
+    "alpha = 0.05\n",
+    "print(\"p-value: {:.15f}\".format(p_hypertension))\n",
+    "if p_hypertension <= 0.05:\n",
+    "  print(H1)\n",
+    "else :\n",
+    "  print(H0)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Kiểm định Chi-square với mức ý nghĩa 5%:</strong><br>\n",
+    "<ul>\n",
+    "<li>\n",
+    "H0 : “Không có mối tương quan giữa bệnh cao huyết áp và bệnh đột quỵ.”\n",
+    "</li>\n",
+    "<li>\n",
+    "H1 : “Có mối tương quan giữa giới tính và bệnh cao huyết áp và bệnh đột quỵ.”\n",
+    "</li>\n",
+    "</ul>\n",
+    "\n",
+    "</div>\n",
+    "<div style=\"display: flex;\n",
+    "            justify-content: center;\n",
+    "            align-items: center;\">\n",
+    "<img src=\"https://i.imgur.com/ldoqCL7.png\" alt=\"Mô tả hình ảnh\" style=\"weight: 400px; height:400px\">\n",
+    "</div>\n",
+    "<div class=\"symple-box yellow center \" style=\"background: #fffdf3; border-radius: 2px; border: 1px solid #f2dfa4; box-sizing: border-box; color: #c4690e; float: none; font-family: Arial, sans-serif; font-size: 22px; margin: 0px auto; outline: 0px; padding: 15px 20px; vertical-align: baseline;\"><strong style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Kết quả kiểm định chi – square về mối tương quan giữa đột quỵ và cao huyết áp</strong>:\n",
+    "<ul style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px 0px 20px 20px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "Chi-Square value: 86.74324378010591</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Degree of Freedom:  1</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">p-value: 0.000000000000000 </li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Có mối tương quan giữa cao huyết áp và đột quỵ</li>\n",
+    "</ul>\n",
+    "</div>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "\n",
+    "<ul>\n",
+    "<li>\n",
+    "Do p-value bé hơn mức ý nghĩa, nên ta bác bỏ giả thuyết H0\n",
+    "</li>\n",
+    "<li>\n",
+    "Với mức ý nghĩa 5%, ta thấy có mối tương quan giữa bệnh cao huyết áp và bệnh đột quỵ.\n",
+    "</li>\n",
+    "</ul>\n",
+    "<strong>Kết luận:</strong>:  Cao huyết áp có mối tương quan với đột quỵ, cao huyết áp làm tăng khả năng mắc đột quỵ, điều này đúng với nghiên cứu của y khoa\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 32px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 60px;\n",
+    "  background-position: 0 100%;\">3.4.8. Đột quỵ và bệnh tim: </h1>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Theo nghiên cứu của Thư viện Y khoa Quốc gia Hoa Kỳ,</strong> bị bệnh tim mạch làm tăng nguy cơ đột quỵ. Bệnh tim mạch có thể góp phần vào nguy cơ phát triển đột quỵ. Một số điều kiện tim mạch, chẳng hạn như bệnh động mạch vành, có thể dẫn đến sự tắc nghẽn của mạch máu. Khi một khu vực của não không nhận đủ máu, có thể xảy ra đột quỵ. <br>\n",
+    "Chia bộ dữ liệu thành 2 nhóm bị bệnh tim và không bị bệnh tim, quan sát sự phân bố của người bị đột quỵ và không bị đột quỵ trên hai nhóm này\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 2600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(26, 8))\n",
+    "draw(df,'stroke','heart_disease')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Quan sát biểu đồ: </strong> tỉ lệ người bị đột quỵ của nhóm bị bệnh tim là 17.09%, cao gấp 4 lần so với tỉ lệ bị đột quỵ của nhóm không bị bệnh tim.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected ij:\n",
+      "[[  13.69202971  261.30797029]\n",
+      " [ 234.30797029 4471.69202971]]\n",
+      "Chi-Square value: 90.25539274279595\n",
+      "Degree of Freedom:  1\n",
+      "p-value:  0.0\n",
+      "Có mối tương quan giữa tim mạch và đột quỵ\n"
+     ]
+    }
+   ],
+   "source": [
+    "stroke_heart_count = df[(df['heart_disease'] == 1) & (df['stroke'] == 1)].shape[0]\n",
+    "nostroke_heart_count = df[(df['heart_disease'] == 1) & (df['stroke'] == 0)].shape[0]\n",
+    "stroke_no_heart_count = df[(df['heart_disease'] == 0) & (df['stroke'] == 1)].shape[0]\n",
+    "nostroke_no_heart_count = df[(df['heart_disease'] == 0) & (df['stroke'] == 0)].shape[0]\n",
+    "H0 = \"Không có mối tương quan giữa tim mạch và đột quỵ\"\n",
+    "H1 = \"Có mối tương quan giữa tim mạch và đột quỵ\"\n",
+    "heart_desease_arr = [stroke_heart_count,nostroke_heart_count]\n",
+    "no_heart_desease_arr = [stroke_no_heart_count,nostroke_no_heart_count]\n",
+    "nij = []\n",
+    "nij.append(heart_desease_arr)\n",
+    "nij.append(no_heart_desease_arr)\n",
+    "nij = np.array(nij)\n",
+    "total_sum = np.sum(nij)\n",
+    "row_sums = np.sum(nij, axis=1)\n",
+    "col_sums = np.sum(nij, axis=0)\n",
+    "expected_ij = np.outer(row_sums, col_sums) / total_sum\n",
+    "print(\"Expected ij:\")\n",
+    "print(expected_ij)\n",
+    "chi_square = np.sum((nij - expected_ij)**2 / expected_ij)\n",
+    "print(\"Chi-Square value:\", chi_square)\n",
+    "dof = (2 - 1)*(2 - 1)\n",
+    "print(\"Degree of Freedom: \", dof)\n",
+    "p_heart = 1 - stats.chi2.cdf(chi_square,dof)\n",
+    "alpha = 0.05\n",
+    "print(\"p-value: \", p_heart)\n",
+    "if p_heart <= 0.05:\n",
+    "  print(H1)\n",
+    "else :\n",
+    "  print(H0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Kiểm định Chi-square với mức ý nghĩa 5%:</strong><br>\n",
+    "<ul>\n",
+    "<li>\n",
+    "H0 : “Không có mối tương quan giữa bệnh tim mạch và bệnh đột quỵ.”\n",
+    "</li>\n",
+    "<li>\n",
+    "H1 : “Có mối tương quan giữa bệnh tim mạch và bệnh đột quỵ.”\n",
+    "</li>\n",
+    "</ul>\n",
+    "\n",
+    "</div>\n",
+    "<div style=\"display: flex;\n",
+    "            justify-content: center;\n",
+    "            align-items: center;\">\n",
+    "<img src=\"https://i.imgur.com/pUyd5kE.png\" alt=\"Mô tả hình ảnh\" style=\"weight: 400px; height:400px\">\n",
+    "</div>\n",
+    "<div class=\"symple-box yellow center \" style=\"background: #fffdf3; border-radius: 2px; border: 1px solid #f2dfa4; box-sizing: border-box; color: #c4690e; float: none; font-family: Arial, sans-serif; font-size: 22px; margin: 0px auto; outline: 0px; padding: 15px 20px; vertical-align: baseline;\"><strong style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Kết quả kiểm định chi – square về mối tương quan giữa đột quỵ và bệnh tim mạch</strong>:\n",
+    "<ul style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px 0px 20px 20px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "Chi-Square value: 90.25539274279595</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Degree of Freedom:  1</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">p-value:  0.0</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Có mối tương quan giữa tim mạch và đột quỵ</li>\n",
+    "</ul>\n",
+    "</div>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "\n",
+    "<ul>\n",
+    "<li>\n",
+    "Do p-value bé hơn mức ý nghĩa, nên ta bác bỏ giả thuyết H0\n",
+    "</li>\n",
+    "<li>\n",
+    "Với mức ý nghĩa 5%, ta thấy có mối tương quan giữa bệnh tim mạch và bệnh đột quỵ.\n",
+    "</li>\n",
+    "</ul>\n",
+    "<strong>Kết luận:</strong> Bệnh tim mạch có mối tương quan với đột quỵ. Bệnh tim có khả năng làm tăng nguy cơ bị đột quỵ. Điều này phù hợp với nghiên cứu của y khoa.\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 32px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 60px;\n",
+    "  background-position: 0 100%;\">3.4.9. Đột quỵ và chỉ số bmi </h1>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Theo nghiên cứu của Thư viện Y khoa Quốc gia Hoa Kỳ,</strong> người bị thừa cân và béo phì làm tăng nguy cơ mắc bệnh đột quỵ hơn so với người có chỉ số bmi bình thường. <br>\n",
+    "Chia bộ dữ liệu thành 2 nhóm đột quỵ và không bị đột quỵ, quan sát sự phân bố của chỉ số bmi.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x240 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x240 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "draw_box(df[df['stroke'] == 0], 'bmi', 'Stroke = 0')\n",
+    "draw_box(df[df['stroke'] == 1], 'bmi', 'Stroke = 1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Quan sát biểu đồ: </strong><br>\n",
+    "<ul>\n",
+    "\n",
+    "\n",
+    "<li>Chỉ số min của biểu đồ hộp bmi của nhóm bị đột quỵ xấp xỉ 18, cao hơn nhiều so với chỉ số min của nhóm bị đột quỵ. Tuy nhiên chỉ số max của nhóm bị đột quỵ lại thấp hơn so với  chỉ số max của nhóm không bị đột quỵ.\n",
+    "\n",
+    "</li>\n",
+    "<li>Nhóm không bị đột quỵ có chỉ số Q1 = 23.5 và Q2 = 28, trong khi nhóm bị đột quỵ chỉ số Q1 = 27 và Q2 = 29.4, cao hơn so với nhóm không bị đột quỵ. Nhóm bị đột quỵ có trên 75% thuộc nhóm thừa cân và béo phì (bmi ≥ 25) và có xấp xỉ 50% thuộc nhóm béo phì (bmi  ≥ 30).\n",
+    "</li>\n",
+    "\n",
+    "</ul>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected ij:\n",
+      "[[  16.53001405  315.46998595]\n",
+      " [  62.78417988 1198.21582012]\n",
+      " [  74.68379843 1425.31620157]\n",
+      " [  94.00200763 1793.99799237]]\n",
+      "Chi-Square value: 37.603837899779755\n",
+      "Degree of Freedom:  3\n",
+      "p-value: 0.000000034285203\n",
+      "Có mối tương quan giữa BMI và đột quỵ\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_13748\\4208331033.py:5: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  thin_stroke_count = thin_df[df['stroke'] == 1].shape[0]\n",
+      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_13748\\4208331033.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  thin_nostroke_count = thin_df[df['stroke'] == 0].shape[0]\n",
+      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_13748\\4208331033.py:7: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  normal_stroke_count = normal_df[df['stroke'] == 1].shape[0]\n",
+      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_13748\\4208331033.py:8: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  normal_nostroke_count = normal_df[df['stroke'] == 0].shape[0]\n",
+      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_13748\\4208331033.py:9: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  ovw_stroke_count = overweight_df[df['stroke'] == 1].shape[0]\n",
+      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_13748\\4208331033.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  ovw_nostroke_count = overweight_df[df['stroke'] == 0].shape[0]\n",
+      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_13748\\4208331033.py:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  obese_stroke_count = obese_df[df['stroke'] == 1].shape[0]\n",
+      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_13748\\4208331033.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  obese_nostroke_count = obese_df[df['stroke'] == 0].shape[0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "thin_df = df[(df['bmi'] >= 0) & (df['bmi'] <= 18.5)]\n",
+    "normal_df = df[(df['bmi'] > 18.5) & (df['bmi'] <= 25)]\n",
+    "overweight_df = df[(df['bmi'] > 25) & (df['bmi'] <= 30)]\n",
+    "obese_df = df[df['bmi'] > 30]\n",
+    "thin_stroke_count = thin_df[df['stroke'] == 1].shape[0]\n",
+    "thin_nostroke_count = thin_df[df['stroke'] == 0].shape[0]\n",
+    "normal_stroke_count = normal_df[df['stroke'] == 1].shape[0]\n",
+    "normal_nostroke_count = normal_df[df['stroke'] == 0].shape[0]\n",
+    "ovw_stroke_count = overweight_df[df['stroke'] == 1].shape[0]\n",
+    "ovw_nostroke_count = overweight_df[df['stroke'] == 0].shape[0]\n",
+    "obese_stroke_count = obese_df[df['stroke'] == 1].shape[0]\n",
+    "obese_nostroke_count = obese_df[df['stroke'] == 0].shape[0]\n",
+    "\n",
+    "H0 = \"Không có mối tương quan giữa BMI và đột quỵ\"\n",
+    "H1 = \"Có mối tương quan giữa BMI và đột quỵ\"\n",
+    "\n",
+    "\n",
+    "thin_arr = [thin_stroke_count, thin_nostroke_count]\n",
+    "normal_arr = [normal_stroke_count, normal_nostroke_count]\n",
+    "ovw_arr = [ovw_stroke_count, ovw_nostroke_count]\n",
+    "obese_arr = [obese_stroke_count, obese_nostroke_count]\n",
+    "\n",
+    "nij = []\n",
+    "nij.append(thin_arr)\n",
+    "nij.append(normal_arr)\n",
+    "nij.append(ovw_arr)\n",
+    "nij.append(obese_arr )\n",
+    "nij = np.array(nij)\n",
+    "\n",
+    "total_sum = np.sum(nij)\n",
+    "row_sums = np.sum(nij, axis=1)\n",
+    "col_sums = np.sum(nij, axis=0)\n",
+    "expected_ij = np.outer(row_sums, col_sums) / total_sum\n",
+    "print(\"Expected ij:\")\n",
+    "print(expected_ij)\n",
+    "chi_square = np.sum((nij - expected_ij)**2 / expected_ij)\n",
+    "print(\"Chi-Square value:\", chi_square)\n",
+    "dof = (2 - 1)*(4 - 1)\n",
+    "print(\"Degree of Freedom: \", dof)\n",
+    "p_bmi = 1 - stats.chi2.cdf(chi_square,dof)\n",
+    "alpha = 0.05\n",
+    "print(\"p-value: {:.15f}\".format(p_bmi))\n",
+    "if p_bmi <= 0.05:\n",
+    "  print(H1)\n",
+    "else :\n",
+    "  print(H0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Kiểm định Chi-square với mức ý nghĩa 5%:</strong><br>\n",
+    "<ul>\n",
+    "<li>\n",
+    "H0 : “Không có mối tương quan giữa chỉ số BMI và bệnh đột quỵ.”\n",
+    "</li>\n",
+    "<li>\n",
+    "H1 : “Có mối tương quan giữa chỉ số BMI và bệnh đột quỵ.”\n",
+    "</li>\n",
+    "</ul>\n",
+    "\n",
+    "</div>\n",
+    "<div style=\"display: flex;\n",
+    "            justify-content: center;\n",
+    "            align-items: center;\">\n",
+    "<img src=\"https://i.imgur.com/w2YeNO4.png\" alt=\"Mô tả hình ảnh\" style=\"weight: 500px; height:500px\">\n",
+    "</div>\n",
+    "<div class=\"symple-box yellow center \" style=\"background: #fffdf3; border-radius: 2px; border: 1px solid #f2dfa4; box-sizing: border-box; color: #c4690e; float: none; font-family: Arial, sans-serif; font-size: 22px; margin: 0px auto; outline: 0px; padding: 15px 20px; vertical-align: baseline;\"><strong style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Kết quả kiểm định Chi – square về mối tương quan giữa đột quỵ và chỉ số bmi</strong>:\n",
+    "<ul style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px 0px 20px 20px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "Chi-Square value: 37.603837899779755</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Degree of Freedom:  3</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">p-value: 0.000000034285203</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Có mối tương quan giữa BMI và đột quỵ</li>\n",
+    "</ul>\n",
+    "</div>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "\n",
+    "\n",
+    "<strong>Kết luận:</strong>  Nếu chỉ số bmi thuộc nhóm thừa cân và béo phì thì sẽ làm tăng nguy cơ mắc bệnh đột quỵ. Điều này phù hợp với nghiên cứu của y khoa.\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 32px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 60px;\n",
+    "  background-position: 0 100%;\">3.4.10. Đột quỵ và chỉ số đường huyết trung bình: </h1>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Theo nghiên cứu của Thư viện Y khoa Quốc gia Hoa Kỳ,</strong> người mắc bệnh tiểu đường thì có nguy cơ mắc bệnh đột quỵ cao gấp 1.5 - 2 lần so với người không mắc bệnh tiểu đường, nguy cơ tăng dần theo thời gian mắc bệnh tiểu đường <br>\n",
+    "Chia bộ dữ liệu thành 2 nhóm bị đột quỵ và không bị đột quỵ, quan sát sự phân bố của chỉ số đường huyết trung bình trên hai nhóm này\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x240 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x240 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "draw_box(df[df['stroke'] == 0], 'avg_glucose_level', 'Stroke = 0')\n",
+    "draw_box(df[df['stroke'] == 1], 'avg_glucose_level', 'Stroke = 1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Quan sát biểu đồ:</strong> <br>\n",
+    "<ul>\n",
+    "<li>Nhóm không bị đột quỵ: max của chỉ số đường huyết trung bình thấp hơn 175, có trên 75% thuộc nhóm đường huyết dành cho người bình thường (đường huyết < 140), chỉ có một phần nhỏ thuộc nhóm tiền tiểu đường (140 ≤ đường huyết < 200 ). Nếu ta không quan tâm đến các điểm ngoại lệ của biểu đồ này thì không xuất hiện người bị béo phì thuộc nhóm này.\n",
+    "</li>\n",
+    "<li>Nhóm bị đột quỵ: chỉ số Q2, Q3 và max của biểu đồ cao hơn rất nhiều so với nhóm không bị đột quỵ. Có xấp xỉ 25% người bị béo phì trong nhóm này (đường huyết > 200).\n",
+    "</li>\n",
+    "\n",
+    "</ul>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected ij:\n",
+      "[[ 208.61674363 3981.38325637]\n",
+      " [  18.72073881  357.27926119]\n",
+      " [  20.66251757  394.33748243]]\n",
+      "Chi-Square value: 92.80248397931284\n",
+      "Degree of Freedom:  2\n",
+      "p-value: 0.0\n",
+      "Có mối tương quan giữa lượng đường trung bình và đột quỵ.\n"
+     ]
+    }
+   ],
+   "source": [
+    "normal_glu_df = df[(df['avg_glucose_level'] >= 0) & (df['avg_glucose_level'] < 140)]\n",
+    "prediact_glu_df = df[(df['avg_glucose_level'] >= 140) & (df['avg_glucose_level'] <= 200)]\n",
+    "diact_glu_df = df[df['avg_glucose_level'] > 200]\n",
+    "normal_glu_stroke_count = normal_glu_df[normal_glu_df['stroke'] == 1].shape[0]\n",
+    "normal_glu_nostroke_count = normal_glu_df[normal_glu_df['stroke'] == 0].shape[0]\n",
+    "prediact_glu_stroke_count = prediact_glu_df[prediact_glu_df['stroke'] == 1].shape[0]\n",
+    "prediact_glu_nostroke_count = prediact_glu_df[prediact_glu_df['stroke'] == 0].shape[0]\n",
+    "diact_glu_stroke_count = diact_glu_df[diact_glu_df['stroke'] == 1].shape[0]\n",
+    "diact_glu_nostroke_count = diact_glu_df[diact_glu_df['stroke'] == 0].shape[0]\n",
+    "\n",
+    "\n",
+    "H0 = \"Không có mối tương quan giữa lượng đường trung bình và đột quỵ.\"\n",
+    "H1 = \"Có mối tương quan giữa lượng đường trung bình và đột quỵ.\"\n",
+    "\n",
+    "\n",
+    "normal_glu_arr = [normal_glu_stroke_count, normal_glu_nostroke_count]\n",
+    "prediact_glu_arr = [prediact_glu_stroke_count, prediact_glu_nostroke_count]\n",
+    "diact_glu_arr = [diact_glu_stroke_count, diact_glu_nostroke_count]\n",
+    "\n",
+    "\n",
+    "nij = []\n",
+    "nij.append(normal_glu_arr)\n",
+    "nij.append(prediact_glu_arr)\n",
+    "nij.append(diact_glu_arr)\n",
+    "nij = np.array(nij)\n",
+    "\n",
+    "total_sum = np.sum(nij)\n",
+    "row_sums = np.sum(nij, axis=1)\n",
+    "col_sums = np.sum(nij, axis=0)\n",
+    "expected_ij = np.outer(row_sums, col_sums) / total_sum\n",
+    "print(\"Expected ij:\")\n",
+    "print(expected_ij)\n",
+    "chi_square = np.sum((nij - expected_ij)**2 / expected_ij)\n",
+    "print(\"Chi-Square value:\", chi_square)\n",
+    "dof = (2 - 1)*(3 - 1)\n",
+    "print(\"Degree of Freedom: \", dof)\n",
+    "p_bmi = 1 - stats.chi2.cdf(chi_square,dof)\n",
+    "alpha = 0.05\n",
+    "print(\"p-value: {:.1f}\".format(p_bmi))\n",
+    "if p_bmi <= 0.05:\n",
+    "  print(H1)\n",
+    "else :\n",
+    "  print(H0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Kiểm định Chi-square với mức ý nghĩa 5%:</strong><br>\n",
+    "<ul>\n",
+    "<li>\n",
+    "H0 : “Không có mối tương quan giữa lượng đường huyết trung bình và bệnh đột quỵ.”\n",
+    "</li>\n",
+    "<li>\n",
+    "H1 : “Có mối tương quan giữa lượng đường huyết trung bình và bệnh đột quỵ.”\n",
+    "</li>\n",
+    "</ul>\n",
+    "\n",
+    "</div>\n",
+    "<div style=\"display: flex;\n",
+    "            justify-content: center;\n",
+    "            align-items: center;\">\n",
+    "<img src=\"https://i.imgur.com/1p2711p.png\" alt=\"Mô tả hình ảnh\" style=\"weight: 500px; height:500px\">\n",
+    "</div>\n",
+    "<div class=\"symple-box yellow center \" style=\"background: #fffdf3; border-radius: 2px; border: 1px solid #f2dfa4; box-sizing: border-box; color: #c4690e; float: none; font-family: Arial, sans-serif; font-size: 22px; margin: 0px auto; outline: 0px; padding: 15px 20px; vertical-align: baseline;\"><strong style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Kết quả kiểm định Chi-square về mối tương quan giữa đột quỵ và chỉ số đường huyết trung bình</strong>:\n",
+    "<ul style=\"background: transparent; border: 0px; box-sizing: initial; margin: 0px 0px 20px 20px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">\n",
+    "Chi-Square value: 92.80248397931284</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Degree of Freedom:  2</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">p-value: 0.0</li>\n",
+    "<li style=\"background: transparent; border: 0px; box-sizing: initial; list-style: disc; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;\">Có mối tương quan giữa lượng đường trung bình và đột quỵ.</li>\n",
+    "</ul>\n",
+    "</div>\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "\n",
+    "\n",
+    "<strong>Kết luận:</strong>  chỉ số đường huyết trung bình có ảnh hưởng nhiều đến đột quỵ. Nếu chỉ số đường huyết thuộc nhóm bị tiểu đường sẽ có nguy cơ cao mắc bệnh đột quỵ. Điều này phù hợp với nghiên cứu của y khoa\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background: rgb(255, 233, 233); border-radius: 2px; border: 1px solid rgb(251, 196, 196); box-sizing: border-box; color: #de5959; float: none; font-family: Arial, sans-serif; font-size: 22px; margin: 0px auto; outline: 0px; padding: 15px 20px; vertical-align: baseline;\"><strong>Đánh giá độ tin cậy của bộ dữ liệu:</strong> Dựa trên các phân tích về mối tương quan giữa các đặc trưng của bộ dữ liệu phía trên, bộ dữ liệu đã thể hiện được phần lớn các nghiên cứu của y khoa. Tuy nhiên vẫn còn một số ít các nghiên cứu của y khoa mà bộ dữ liệu không thể hiện được hoặc không đủ tiêu chí đánh giá do bộ dữ liệu không đủ các thông tin cần có để xem xét. Kết luận: Bộ dữ liệu có độ tin cậy cao. </div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "</div>\n",
+    "<div style=\"display: flex;\n",
+    "            justify-content: center;\n",
+    "            align-items: center;\">\n",
+    "<img src=\"https://i.imgur.com/2vdCUtH.png\" alt=\"Mô tả hình ảnh\" style=\"weight: 500px; height:500px\">\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "</div>\n",
+    "<div style=\"display: flex;\n",
+    "            justify-content: center;\n",
+    "            align-items: center;\">\n",
+    "<img src=\"https://i.imgur.com/SWP9FMU.png\" alt=\"Mô tả hình ảnh\" style=\"weight: 500px; height:500px\">\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 42px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 120px;\n",
+    "  background-position: 0 100%;\"> 4. Xây dựng mô hình </h1>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 38px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 80px;\n",
+    "  background-position: 0 100%;\"> 4.1 Tiền xử lý dữ liệu</h1>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<strong>Mã hóa dữ liệu: </strong>Ta sẽ biến các trường dữ liệu là kiểu object thành kiểu dữ liệu số hết. Đối với trường dữ liệu smokin_status có thư tự nên ta chuyển never smoke: 0, Unknown: 1,formerly smoked: 2,smokes: 3<br>\n",
+    "<strong>Xử lí bộ dữ liệu mất cân bằng: </strong>Do tỉ lệ số người bị đột quỵ và không bị đột quỵ trên nhau quá lớn do đó ta sẽ sử dụng phương pháp SMOTE để xử lí dữ liệu mất cân bằng<br>\n",
+    "<strong>Chuẩn hóa dữ liệu:</strong> Ta dùng MinMaxScaler để chuẩn hóa dữ liệu đối với các trường dữ liệu age, avg_glucose_level, bmi,smoking_status_encoded để giúp tăng tính ổn định khi xây dựng, huấn luyện mô hình\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def one_hot_encode(data, column):\n",
+    "    # Tạo một DataFrame tạm thời để lưu trữ kết quả\n",
+    "    encoded_data = pd.get_dummies(data[column], prefix=column)\n",
+    "    \n",
+    "    # Ghép kết quả với DataFrame gốc\n",
+    "    data = pd.concat([data, encoded_data], axis=1)\n",
+    "    \n",
+    "    # Xóa trường dữ liệu gốc\n",
+    "    data.drop(column, axis=1, inplace=True)\n",
+    "    \n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "df = one_hot_encode(df, 'work_type')\n",
+    "custom_order = [['never smoked','Unknown', 'formerly smoked', 'smokes']]\n",
+    "encoder = OrdinalEncoder()\n",
+    "\n",
+    "\n",
+    "cat_cols = ['Residence_type', 'ever_married', 'gender']\n",
+    "df[cat_cols] = encoder.fit_transform(df[cat_cols])\n",
+    "\n",
+    "\n",
+    "# Áp dụng Ordinal Encoding với danh sách thứ tự tùy chỉnh\n",
+    "encoder = OrdinalEncoder(categories=custom_order)\n",
+    "encoded_data = encoder.fit_transform(df[['smoking_status']])\n",
+    "\n",
+    "# Chuyển đổi ma trận kết quả thành DataFrame\n",
+    "df_encoded = pd.DataFrame(encoded_data, columns=['smoking_status_encoded'])\n",
+    "\n",
+    "# Kết hợp DataFrame gốc và DataFrame đã được biến đổi\n",
+    "df_final = pd.concat([df, df_encoded], axis=1)\n",
+    "df_final.drop('smoking_status',axis=1,inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "smote = SMOTE()\n",
+    "X = df_final.drop('stroke',axis=1)\n",
+    "y = df_final['stroke']\n",
+    "X_resampled, y_resampled = smote.fit_resample(X, y)\n",
+    "\n",
+    "# Tạo DataFrame mới từ mẫu đã cân bằng\n",
+    "df_resampled = pd.concat([pd.DataFrame(X_resampled, columns=X.columns),\n",
+    "                          pd.DataFrame(y_resampled, columns=['stroke'])], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "X = df_resampled.drop('stroke',axis=1)\n",
+    "y = df_resampled['stroke']\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "# Áp dụng chuẩn hóa z-score cho các cột dữ liệu\n",
+    "scaler = MinMaxScaler()\n",
+    "scaled_train_data = scaler.fit_transform(X_train)\n",
+    "scaled_test_data = scaler.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 38px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 80px;\n",
+    "  background-position: 0 100%;\"> 4.2 Huấn luyện</h1>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<ul><li>Ta dùng các mô hình học máy để huấn luyện mô hình dự đoán nguy cơ đột quỵ thông qua các dữ liệu lâm sàn.<br> \n",
+    "</li>\n",
+    "<li>\n",
+    "Các mô hình ta dùng để đánh giá bao gồm Logistic regression, Gradient boosting classifier, Support vector machine, XGBoost và cuối cùng là thuật toán học máy Stacking lấy XGBoost, Gradient boosting, Support vector machine, làm công cụ ước tính  và dùng Logistic regression làm công cụ ước tính cuối cùng\n",
+    "</li>\n",
+    "<div style=\"display: flex;\n",
+    "            justify-content: center;\n",
+    "            align-items: center;\">\n",
+    "<i>Sơ đồ của thuật toán học máy Stacking</i>\n",
+    "</div>\n",
+    "<ul>\n",
+    "</div>\n",
+    "\n",
+    "<div style=\"display: flex;\n",
+    "            justify-content: center;\n",
+    "            align-items: center;\">\n",
+    "<img src=\"https://i.imgur.com/OZl2Ix4.png\" alt=\"Mô tả hình ảnh\" style=\"weight: 500px; height:500px\">\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_LGT = LogisticRegression(max_iter = 100)\n",
+    "model_LGT.fit(scaled_train_data , y_train)\n",
+    "y_pred = model_LGT.predict(scaled_test_data)\n",
+    "\n",
+    "accuracy_lgt = accuracy_score(y_test, y_pred)\n",
+    "recall_lgt = recall_score(y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graBoost = GradientBoostingClassifier(  learning_rate=0.4, n_estimators=300, min_samples_split=77, random_state=42)\n",
+    "graBoost.fit(scaled_train_data , y_train)\n",
+    "y_pred = graBoost.predict(scaled_test_data)\n",
+    "\n",
+    "accuracy_gra = accuracy_score(y_test, y_pred)\n",
+    "recall_gra = recall_score(y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xgBoost = XGBClassifier(  objective=\"binary:logistic\", random_state=42, n_estimators = 500)\n",
+    "xgBoost.fit(scaled_train_data,y_train )\n",
+    "y_pred = xgBoost.predict(scaled_test_data)\n",
+    "\n",
+    "accuracy_xgb = accuracy_score(y_test, y_pred)\n",
+    "recall_xgb = recall_score(y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "svm = SVC( kernel = 'rbf', random_state =42,C=300)\n",
+    "svm.fit(scaled_train_data,y_train )\n",
+    "y_pred = svm.predict(scaled_test_data)\n",
+    "\n",
+    "accuracy_svm = accuracy_score(y_test, y_pred)\n",
+    "recall_svm = recall_score(y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lgt = LogisticRegression(max_iter = 100)\n",
+    "svc = SVC( kernel = 'rbf', random_state =42,C=300)\n",
+    "grb = GradientBoostingClassifier(  learning_rate=0.3, n_estimators=500, min_samples_split=7, \n",
+    "random_state=42)\n",
+    "xgb =  XGBClassifier( n_estimators = 500)\n",
+    "clf = [('grb',grb),('xgb',xgb),('svc',svc)]\n",
+    "\n",
+    "\n",
+    "\n",
+    "classifier = StackingClassifier(estimators =clf , final_estimator=lgt)\n",
+    "classifier.fit(scaled_train_data,y_train )\n",
+    "y_pred = classifier.predict(scaled_test_data)\n",
+    "\n",
+    "accuracy_sta = accuracy_score(y_test, y_pred)\n",
+    "recall_sta = recall_score(y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 38px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 80px;\n",
+    "  background-position: 0 100%;\"> 4.3\t Đánh giá </h1>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "Ta sẽ đánh giá độ chính xác, độ tốt của mô hình thông qua các thang đo accuracy, recall. Vì đây là bài toán dự đoán bệnh do đó ta sẽ ưu tiên thang đo recall hơn precision nên ta không sử dụng thang đo presicion để đánh giá ở đây </div>\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "recall = [recall_lgt, recall_gra, recall_xgb, recall_svm, recall_sta]\n",
+    "\n",
+    "accuracy = [accuracy_lgt, accuracy_gra, accuracy_xgb, accuracy_svm, accuracy_sta]\n",
+    "ml = ['Logistic', 'GradientBoosting', 'XGBoost', 'Support Vector Machine', 'Stacking']\n",
+    "plt.figure(figsize=(15, 6))\n",
+    "# Vẽ biểu đồ\n",
+    "plt.plot(ml, recall, 'o-', label='recall')\n",
+    "plt.plot(ml, accuracy, 'o-', label='accuracy')\n",
+    "\n",
+    "# Đặt tiêu đề và nhãn trục\n",
+    "\n",
+    "\n",
+    "# Hiển thị chú thích\n",
+    "plt.legend()\n",
+    "\n",
+    "# Ghi số cua trục y lên từng điểm\n",
+    "for i, j in enumerate(recall):\n",
+    "    plt.text(ml[i], j, str(round(j, 4)),fontsize = 14)\n",
+    "\n",
+    "for i, j in enumerate(accuracy):\n",
+    "    plt.text(ml[i], j, str(round(j, 4)),fontsize = 14)\n",
+    "\n",
+    "plt.xticks(fontsize=14)\n",
+    "plt.yticks(fontsize=14)\n",
+    "# Hiển thị biểu đồ\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"display: inline-block;\n",
+    "  font: bold 4.5em/1.5 Bebas, sans-serif;\n",
+    "  color: #5CA17C; /*non-webkit fallback*/\n",
+    "  font-size: 38px;\n",
+    "  text-transform: uppercase;\n",
+    "   background-color:#66689c;\n",
+    "  background-size: auto 250%;\n",
+    "  transition: background-position 0.5s;\n",
+    "    -webkit-background-clip: text;\n",
+    "  -webkit-text-fill-color: transparent;\n",
+    "  line-height: 80px;\n",
+    "  background-position: 0 100%;\"> TÀI LIỆU THAM KHẢO</h1>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<div class=\"box_luuy\" style=\"background: #66689c; box-shadow: rgba(0, 0, 0, 0.1) 3px 4px 4px; clear: both; line-height: 2; color: white; font-size:22px ; letter-spacing: -0.75px; margin: 5px 0px; padding: 20px 25px; transition: 0.5s;\">\n",
+    "<ol type=\"1\">\n",
+    "<li>\n",
+    "Obesity and insulin resistance: <a style=\"color: white\"href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC380258/.\">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC380258/.</a>\n",
+    "</li>\n",
+    "<li>\n",
+    "Correlation of Body Mass Index (BMI) with Saliva and Blood Glucose Levels in Diabetic and Non-Diabetic Patients: <a style=\"color: white\"href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10485520/. \">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10485520/. </a> \n",
+    "</li>\n",
+    "<li>\n",
+    "Association of Body Mass Index With Lifetime Risk of Cardiovascular Disease and Compression of Morbidity: <a style=\"color: white\"href=\"https://pubmed.ncbi.nlm.nih.gov/29490333/.  \">https://pubmed.ncbi.nlm.nih.gov/29490333/.  </a>\n",
+    "</li>\n",
+    "<li>\n",
+    "Heart Health and Aging: <a style=\"color: white\"href=\" https://www.nia.nih.gov/health/heart-health/heart-health-and-aging.  \"> https://www.nia.nih.gov/health/heart-health/heart-health-and-aging.  </a>\n",
+    "</li>\n",
+    "<li>\n",
+    "Gender differences in coronary heart disease: <a style=\"color: white\"href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3018605/.  \"> https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3018605/. </a>\n",
+    "</li>\n",
+    "<li>\n",
+    "Cardiovascular risk of smoking and benefits of smoking cessation: <a style=\"color: white\"href=\" https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7399440/.\">  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7399440/.</a>\n",
+    "</li>\n",
+    "<li>\n",
+    "Hypertension and Aging: <a style=\"color: white\"href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4768730/.\"> https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4768730/.</a>\n",
+    "</li>\n",
+    "<li>\n",
+    "Hypertension and Coronary Heart Disease: <a style=\"color: white\"href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4768730/.\"> https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6169140/.</a>\n",
+    "</li>\n",
+    "<li>\n",
+    "Stroke and gender: <a style=\"color: white\"href=\"https://pubmed.ncbi.nlm.nih.gov/11252851/.\">https://pubmed.ncbi.nlm.nih.gov/11252851/.</a>\n",
+    "</li>\n",
+    "<li>\n",
+    "Aging and ischemic stroke: <a style=\"color: white\"href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6535078/. \">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6535078/. </a>\n",
+    "</li>\n",
+    "<li>\n",
+    "The relationship between smoking and stroke, Smoking and stroke: the more you smoke the more you stroke: <a style=\"color: white\"href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2928253/. \">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2928253/. </a>\n",
+    "</li>\n",
+    "<li>\n",
+    "Hypertension and Stroke: Update on Treatment: <a style=\"color: white\"href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6659031/. \">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6659031/. </a>\n",
+    "</li>\n",
+    "<li>\n",
+    "Large Burden of Stroke Incidence in People with Cardiac Disease: A Linked Data Cohort Study: <a style=\"color: white\"href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9945299/. \">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9945299/. </a>\n",
+    "</li> \n",
+    "</ol>\n",
+    " </div>"
+   ]
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