diff --git "a/dataAnalysis_notebook.ipynb" "b/dataAnalysis_notebook.ipynb"
new file mode 100644--- /dev/null
+++ "b/dataAnalysis_notebook.ipynb"
@@ -0,0 +1,2415 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.gridspec as gridspec\n",
+    "import seaborn as sns\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore', category=FutureWarning)\n",
+    "\n",
+    "sns.set(style=\"whitegrid\")\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Đọc dữ liệu\n",
+    "train = pd.read_csv('train.csv')\n",
+    "test = pd.read_csv('test.csv')\n",
+    "data_dict = pd.read_csv('data_dictionary.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>id</th>\n",
+       "      <th>Basic_Demos-Enroll_Season</th>\n",
+       "      <th>Basic_Demos-Age</th>\n",
+       "      <th>Basic_Demos-Sex</th>\n",
+       "      <th>CGAS-Season</th>\n",
+       "      <th>CGAS-CGAS_Score</th>\n",
+       "      <th>Physical-Season</th>\n",
+       "      <th>Physical-BMI</th>\n",
+       "      <th>Physical-Height</th>\n",
+       "      <th>Physical-Weight</th>\n",
+       "      <th>...</th>\n",
+       "      <th>PCIAT-PCIAT_18</th>\n",
+       "      <th>PCIAT-PCIAT_19</th>\n",
+       "      <th>PCIAT-PCIAT_20</th>\n",
+       "      <th>PCIAT-PCIAT_Total</th>\n",
+       "      <th>SDS-Season</th>\n",
+       "      <th>SDS-SDS_Total_Raw</th>\n",
+       "      <th>SDS-SDS_Total_T</th>\n",
+       "      <th>PreInt_EduHx-Season</th>\n",
+       "      <th>PreInt_EduHx-computerinternet_hoursday</th>\n",
+       "      <th>sii</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>00008ff9</td>\n",
+       "      <td>Fall</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Winter</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>Fall</td>\n",
+       "      <td>16.877316</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>50.8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Fall</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>000fd460</td>\n",
+       "      <td>Summer</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Fall</td>\n",
+       "      <td>14.035590</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Fall</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>Summer</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>00105258</td>\n",
+       "      <td>Summer</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Fall</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>Fall</td>\n",
+       "      <td>16.648696</td>\n",
+       "      <td>56.5</td>\n",
+       "      <td>75.6</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>Fall</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>Summer</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>00115b9f</td>\n",
+       "      <td>Winter</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Fall</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>Summer</td>\n",
+       "      <td>18.292347</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>81.6</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>Summer</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>Winter</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0016bb22</td>\n",
+       "      <td>Spring</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Summer</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 82 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         id Basic_Demos-Enroll_Season  Basic_Demos-Age  Basic_Demos-Sex  \\\n",
+       "0  00008ff9                      Fall                5                0   \n",
+       "1  000fd460                    Summer                9                0   \n",
+       "2  00105258                    Summer               10                1   \n",
+       "3  00115b9f                    Winter                9                0   \n",
+       "4  0016bb22                    Spring               18                1   \n",
+       "\n",
+       "  CGAS-Season  CGAS-CGAS_Score Physical-Season  Physical-BMI  Physical-Height  \\\n",
+       "0      Winter             51.0            Fall     16.877316             46.0   \n",
+       "1         NaN              NaN            Fall     14.035590             48.0   \n",
+       "2        Fall             71.0            Fall     16.648696             56.5   \n",
+       "3        Fall             71.0          Summer     18.292347             56.0   \n",
+       "4      Summer              NaN             NaN           NaN              NaN   \n",
+       "\n",
+       "   Physical-Weight  ...  PCIAT-PCIAT_18  PCIAT-PCIAT_19  PCIAT-PCIAT_20  \\\n",
+       "0             50.8  ...             4.0             2.0             4.0   \n",
+       "1             46.0  ...             0.0             0.0             0.0   \n",
+       "2             75.6  ...             2.0             1.0             1.0   \n",
+       "3             81.6  ...             3.0             4.0             1.0   \n",
+       "4              NaN  ...             NaN             NaN             NaN   \n",
+       "\n",
+       "   PCIAT-PCIAT_Total SDS-Season  SDS-SDS_Total_Raw  SDS-SDS_Total_T  \\\n",
+       "0               55.0        NaN                NaN              NaN   \n",
+       "1                0.0       Fall               46.0             64.0   \n",
+       "2               28.0       Fall               38.0             54.0   \n",
+       "3               44.0     Summer               31.0             45.0   \n",
+       "4                NaN        NaN                NaN              NaN   \n",
+       "\n",
+       "   PreInt_EduHx-Season PreInt_EduHx-computerinternet_hoursday  sii  \n",
+       "0                 Fall                                    3.0  2.0  \n",
+       "1               Summer                                    0.0  0.0  \n",
+       "2               Summer                                    2.0  0.0  \n",
+       "3               Winter                                    0.0  1.0  \n",
+       "4                  NaN                                    NaN  NaN  \n",
+       "\n",
+       "[5 rows x 82 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(train.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>id</th>\n",
+       "      <th>Basic_Demos-Enroll_Season</th>\n",
+       "      <th>Basic_Demos-Age</th>\n",
+       "      <th>Basic_Demos-Sex</th>\n",
+       "      <th>CGAS-Season</th>\n",
+       "      <th>CGAS-CGAS_Score</th>\n",
+       "      <th>Physical-Season</th>\n",
+       "      <th>Physical-BMI</th>\n",
+       "      <th>Physical-Height</th>\n",
+       "      <th>Physical-Weight</th>\n",
+       "      <th>...</th>\n",
+       "      <th>BIA-BIA_TBW</th>\n",
+       "      <th>PAQ_A-Season</th>\n",
+       "      <th>PAQ_A-PAQ_A_Total</th>\n",
+       "      <th>PAQ_C-Season</th>\n",
+       "      <th>PAQ_C-PAQ_C_Total</th>\n",
+       "      <th>SDS-Season</th>\n",
+       "      <th>SDS-SDS_Total_Raw</th>\n",
+       "      <th>SDS-SDS_Total_T</th>\n",
+       "      <th>PreInt_EduHx-Season</th>\n",
+       "      <th>PreInt_EduHx-computerinternet_hoursday</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>00008ff9</td>\n",
+       "      <td>Fall</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Winter</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>Fall</td>\n",
+       "      <td>16.877316</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>50.8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.6909</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Fall</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>000fd460</td>\n",
+       "      <td>Summer</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Fall</td>\n",
+       "      <td>14.035590</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>27.0552</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Fall</td>\n",
+       "      <td>2.340</td>\n",
+       "      <td>Fall</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>Summer</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>00105258</td>\n",
+       "      <td>Summer</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Fall</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>Fall</td>\n",
+       "      <td>16.648696</td>\n",
+       "      <td>56.5</td>\n",
+       "      <td>75.6</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Summer</td>\n",
+       "      <td>2.170</td>\n",
+       "      <td>Fall</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>Summer</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>00115b9f</td>\n",
+       "      <td>Winter</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Fall</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>Summer</td>\n",
+       "      <td>18.292347</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>81.6</td>\n",
+       "      <td>...</td>\n",
+       "      <td>45.9966</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Winter</td>\n",
+       "      <td>2.451</td>\n",
+       "      <td>Summer</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>Winter</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0016bb22</td>\n",
+       "      <td>Spring</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Summer</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Summer</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 59 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         id Basic_Demos-Enroll_Season  Basic_Demos-Age  Basic_Demos-Sex  \\\n",
+       "0  00008ff9                      Fall                5                0   \n",
+       "1  000fd460                    Summer                9                0   \n",
+       "2  00105258                    Summer               10                1   \n",
+       "3  00115b9f                    Winter                9                0   \n",
+       "4  0016bb22                    Spring               18                1   \n",
+       "\n",
+       "  CGAS-Season  CGAS-CGAS_Score Physical-Season  Physical-BMI  Physical-Height  \\\n",
+       "0      Winter             51.0            Fall     16.877316             46.0   \n",
+       "1         NaN              NaN            Fall     14.035590             48.0   \n",
+       "2        Fall             71.0            Fall     16.648696             56.5   \n",
+       "3        Fall             71.0          Summer     18.292347             56.0   \n",
+       "4      Summer              NaN             NaN           NaN              NaN   \n",
+       "\n",
+       "   Physical-Weight  ...  BIA-BIA_TBW  PAQ_A-Season  PAQ_A-PAQ_A_Total  \\\n",
+       "0             50.8  ...      32.6909           NaN                NaN   \n",
+       "1             46.0  ...      27.0552           NaN                NaN   \n",
+       "2             75.6  ...          NaN           NaN                NaN   \n",
+       "3             81.6  ...      45.9966           NaN                NaN   \n",
+       "4              NaN  ...          NaN        Summer               1.04   \n",
+       "\n",
+       "   PAQ_C-Season PAQ_C-PAQ_C_Total  SDS-Season  SDS-SDS_Total_Raw  \\\n",
+       "0           NaN               NaN         NaN                NaN   \n",
+       "1          Fall             2.340        Fall               46.0   \n",
+       "2        Summer             2.170        Fall               38.0   \n",
+       "3        Winter             2.451      Summer               31.0   \n",
+       "4           NaN               NaN         NaN                NaN   \n",
+       "\n",
+       "   SDS-SDS_Total_T PreInt_EduHx-Season  PreInt_EduHx-computerinternet_hoursday  \n",
+       "0              NaN                Fall                                     3.0  \n",
+       "1             64.0              Summer                                     0.0  \n",
+       "2             54.0              Summer                                     2.0  \n",
+       "3             45.0              Winter                                     0.0  \n",
+       "4              NaN                 NaN                                     NaN  \n",
+       "\n",
+       "[5 rows x 59 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(test.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>Instrument</th>\n",
+       "      <th>Field</th>\n",
+       "      <th>Description</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Values</th>\n",
+       "      <th>Value Labels</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Identifier</td>\n",
+       "      <td>id</td>\n",
+       "      <td>Participant's ID</td>\n",
+       "      <td>str</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Demographics</td>\n",
+       "      <td>Basic_Demos-Enroll_Season</td>\n",
+       "      <td>Season of enrollment</td>\n",
+       "      <td>str</td>\n",
+       "      <td>Spring, Summer, Fall, Winter</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Demographics</td>\n",
+       "      <td>Basic_Demos-Age</td>\n",
+       "      <td>Age of participant</td>\n",
+       "      <td>float</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Demographics</td>\n",
+       "      <td>Basic_Demos-Sex</td>\n",
+       "      <td>Sex of participant</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1</td>\n",
+       "      <td>0=Male, 1=Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Children's Global Assessment Scale</td>\n",
+       "      <td>CGAS-Season</td>\n",
+       "      <td>Season of participation</td>\n",
+       "      <td>str</td>\n",
+       "      <td>Spring, Summer, Fall, Winter</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           Instrument                      Field  \\\n",
+       "0                          Identifier                         id   \n",
+       "1                        Demographics  Basic_Demos-Enroll_Season   \n",
+       "2                        Demographics            Basic_Demos-Age   \n",
+       "3                        Demographics            Basic_Demos-Sex   \n",
+       "4  Children's Global Assessment Scale                CGAS-Season   \n",
+       "\n",
+       "               Description             Type                        Values  \\\n",
+       "0         Participant's ID              str                           NaN   \n",
+       "1     Season of enrollment              str  Spring, Summer, Fall, Winter   \n",
+       "2       Age of participant            float                           NaN   \n",
+       "3       Sex of participant  categorical int                           0,1   \n",
+       "4  Season of participation              str  Spring, Summer, Fall, Winter   \n",
+       "\n",
+       "       Value Labels  \n",
+       "0               NaN  \n",
+       "1               NaN  \n",
+       "2               NaN  \n",
+       "3  0=Male, 1=Female  \n",
+       "4               NaN  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_dict.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_stats(data, columns):\n",
+    "    if isinstance(columns, str):\n",
+    "        columns = [columns]\n",
+    "\n",
+    "    stats = []\n",
+    "    for col in columns:\n",
+    "        if data[col].dtype in ['object', 'category']:\n",
+    "            counts = data[col].value_counts(dropna=False, sort=False)\n",
+    "            percents = data[col].value_counts(normalize=True, dropna=False, sort=False) * 100\n",
+    "            formatted = counts.astype(str) + ' (' + percents.round(2).astype(str) + '%)'\n",
+    "            stats_col = pd.DataFrame({'count (%)': formatted})\n",
+    "            stats.append(stats_col)\n",
+    "        else:\n",
+    "            stats_col = data[col].describe().to_frame().transpose()\n",
+    "            stats_col['missing'] = data[col].isnull().sum()\n",
+    "            stats_col.index.name = col\n",
+    "            stats.append(stats_col)\n",
+    "\n",
+    "    return pd.concat(stats, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>Instrument</th>\n",
+       "      <th>Field</th>\n",
+       "      <th>Description</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Values</th>\n",
+       "      <th>Value Labels</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-Season</td>\n",
+       "      <td>Season of participation</td>\n",
+       "      <td>str</td>\n",
+       "      <td>Spring, Summer, Fall, Winter</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_01</td>\n",
+       "      <td>How often does your child disobey time limits ...</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1,2,3,4,5</td>\n",
+       "      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_02</td>\n",
+       "      <td>How often does your child neglect household ch...</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1,2,3,4,5</td>\n",
+       "      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_03</td>\n",
+       "      <td>How often does your child prefer to spend time...</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1,2,3,4,5</td>\n",
+       "      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_04</td>\n",
+       "      <td>How often does your child form new relationshi...</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1,2,3,4,5</td>\n",
+       "      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_05</td>\n",
+       "      <td>How often do you complain about the amount of ...</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1,2,3,4,5</td>\n",
+       "      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_06</td>\n",
+       "      <td>How often do your child's grades suffer becaus...</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1,2,3,4,5</td>\n",
+       "      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_07</td>\n",
+       "      <td>How often does your child check his or her e-m...</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1,2,3,4,5</td>\n",
+       "      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_08</td>\n",
+       "      <td>How often does your child seem withdrawn from ...</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1,2,3,4,5</td>\n",
+       "      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_09</td>\n",
+       "      <td>How often does your child become defensive or ...</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1,2,3,4,5</td>\n",
+       "      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_10</td>\n",
+       "      <td>How often have you caught your child sneaking ...</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1,2,3,4,5</td>\n",
+       "      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_11</td>\n",
+       "      <td>How often does your child spend time along in ...</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1,2,3,4,5</td>\n",
+       "      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_12</td>\n",
+       "      <td>How often does your child receive strange phon...</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1,2,3,4,5</td>\n",
+       "      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_13</td>\n",
+       "      <td>How often does your child snap, yell, or act a...</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1,2,3,4,5</td>\n",
+       "      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_14</td>\n",
+       "      <td>How often does your child seem more tired and ...</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1,2,3,4,5</td>\n",
+       "      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_15</td>\n",
+       "      <td>How often does your child seem preoccupied wit...</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1,2,3,4,5</td>\n",
+       "      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_16</td>\n",
+       "      <td>How often does your child throw tantrums with ...</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1,2,3,4,5</td>\n",
+       "      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_17</td>\n",
+       "      <td>How often does your child choose to spend time...</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1,2,3,4,5</td>\n",
+       "      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_18</td>\n",
+       "      <td>How often does your child become angry or bell...</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1,2,3,4,5</td>\n",
+       "      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_19</td>\n",
+       "      <td>How often does your child choose to spend more...</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1,2,3,4,5</td>\n",
+       "      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_20</td>\n",
+       "      <td>How often does your child feel depressed, mood...</td>\n",
+       "      <td>categorical int</td>\n",
+       "      <td>0,1,2,3,4,5</td>\n",
+       "      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>Parent-Child Internet Addiction Test</td>\n",
+       "      <td>PCIAT-PCIAT_Total</td>\n",
+       "      <td>Total Score</td>\n",
+       "      <td>int</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Severity Impairment Index: 0-30=None; 31-49=Mi...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              Instrument              Field  \\\n",
+       "54  Parent-Child Internet Addiction Test       PCIAT-Season   \n",
+       "55  Parent-Child Internet Addiction Test     PCIAT-PCIAT_01   \n",
+       "56  Parent-Child Internet Addiction Test     PCIAT-PCIAT_02   \n",
+       "57  Parent-Child Internet Addiction Test     PCIAT-PCIAT_03   \n",
+       "58  Parent-Child Internet Addiction Test     PCIAT-PCIAT_04   \n",
+       "59  Parent-Child Internet Addiction Test     PCIAT-PCIAT_05   \n",
+       "60  Parent-Child Internet Addiction Test     PCIAT-PCIAT_06   \n",
+       "61  Parent-Child Internet Addiction Test     PCIAT-PCIAT_07   \n",
+       "62  Parent-Child Internet Addiction Test     PCIAT-PCIAT_08   \n",
+       "63  Parent-Child Internet Addiction Test     PCIAT-PCIAT_09   \n",
+       "64  Parent-Child Internet Addiction Test     PCIAT-PCIAT_10   \n",
+       "65  Parent-Child Internet Addiction Test     PCIAT-PCIAT_11   \n",
+       "66  Parent-Child Internet Addiction Test     PCIAT-PCIAT_12   \n",
+       "67  Parent-Child Internet Addiction Test     PCIAT-PCIAT_13   \n",
+       "68  Parent-Child Internet Addiction Test     PCIAT-PCIAT_14   \n",
+       "69  Parent-Child Internet Addiction Test     PCIAT-PCIAT_15   \n",
+       "70  Parent-Child Internet Addiction Test     PCIAT-PCIAT_16   \n",
+       "71  Parent-Child Internet Addiction Test     PCIAT-PCIAT_17   \n",
+       "72  Parent-Child Internet Addiction Test     PCIAT-PCIAT_18   \n",
+       "73  Parent-Child Internet Addiction Test     PCIAT-PCIAT_19   \n",
+       "74  Parent-Child Internet Addiction Test     PCIAT-PCIAT_20   \n",
+       "75  Parent-Child Internet Addiction Test  PCIAT-PCIAT_Total   \n",
+       "\n",
+       "                                          Description             Type  \\\n",
+       "54                            Season of participation              str   \n",
+       "55  How often does your child disobey time limits ...  categorical int   \n",
+       "56  How often does your child neglect household ch...  categorical int   \n",
+       "57  How often does your child prefer to spend time...  categorical int   \n",
+       "58  How often does your child form new relationshi...  categorical int   \n",
+       "59  How often do you complain about the amount of ...  categorical int   \n",
+       "60  How often do your child's grades suffer becaus...  categorical int   \n",
+       "61  How often does your child check his or her e-m...  categorical int   \n",
+       "62  How often does your child seem withdrawn from ...  categorical int   \n",
+       "63  How often does your child become defensive or ...  categorical int   \n",
+       "64  How often have you caught your child sneaking ...  categorical int   \n",
+       "65  How often does your child spend time along in ...  categorical int   \n",
+       "66  How often does your child receive strange phon...  categorical int   \n",
+       "67  How often does your child snap, yell, or act a...  categorical int   \n",
+       "68  How often does your child seem more tired and ...  categorical int   \n",
+       "69  How often does your child seem preoccupied wit...  categorical int   \n",
+       "70  How often does your child throw tantrums with ...  categorical int   \n",
+       "71  How often does your child choose to spend time...  categorical int   \n",
+       "72  How often does your child become angry or bell...  categorical int   \n",
+       "73  How often does your child choose to spend more...  categorical int   \n",
+       "74  How often does your child feel depressed, mood...  categorical int   \n",
+       "75                                        Total Score              int   \n",
+       "\n",
+       "                          Values  \\\n",
+       "54  Spring, Summer, Fall, Winter   \n",
+       "55                   0,1,2,3,4,5   \n",
+       "56                   0,1,2,3,4,5   \n",
+       "57                   0,1,2,3,4,5   \n",
+       "58                   0,1,2,3,4,5   \n",
+       "59                   0,1,2,3,4,5   \n",
+       "60                   0,1,2,3,4,5   \n",
+       "61                   0,1,2,3,4,5   \n",
+       "62                   0,1,2,3,4,5   \n",
+       "63                   0,1,2,3,4,5   \n",
+       "64                   0,1,2,3,4,5   \n",
+       "65                   0,1,2,3,4,5   \n",
+       "66                   0,1,2,3,4,5   \n",
+       "67                   0,1,2,3,4,5   \n",
+       "68                   0,1,2,3,4,5   \n",
+       "69                   0,1,2,3,4,5   \n",
+       "70                   0,1,2,3,4,5   \n",
+       "71                   0,1,2,3,4,5   \n",
+       "72                   0,1,2,3,4,5   \n",
+       "73                   0,1,2,3,4,5   \n",
+       "74                   0,1,2,3,4,5   \n",
+       "75                           NaN   \n",
+       "\n",
+       "                                         Value Labels  \n",
+       "54                                                NaN  \n",
+       "55  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n",
+       "56  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n",
+       "57  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n",
+       "58  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n",
+       "59  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n",
+       "60  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n",
+       "61  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n",
+       "62  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n",
+       "63  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n",
+       "64  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n",
+       "65  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n",
+       "66  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n",
+       "67  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n",
+       "68  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n",
+       "69  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n",
+       "70  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n",
+       "71  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n",
+       "72  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n",
+       "73  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n",
+       "74  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n",
+       "75  Severity Impairment Index: 0-30=None; 31-49=Mi...  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_cols = set(train.columns)\n",
+    "test_cols = set(test.columns)\n",
+    "columns_not_in_test = sorted(list(train_cols - test_cols))\n",
+    "data_dict[data_dict['Field'].isin(columns_not_in_test)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_92df0_row0_col18, #T_92df0_row1_col0, #T_92df0_row1_col1, #T_92df0_row1_col2, #T_92df0_row1_col3, #T_92df0_row1_col4, #T_92df0_row1_col5, #T_92df0_row1_col6, #T_92df0_row1_col7, #T_92df0_row1_col8, #T_92df0_row1_col9, #T_92df0_row1_col10, #T_92df0_row1_col11, #T_92df0_row1_col12, #T_92df0_row1_col13, #T_92df0_row1_col14, #T_92df0_row1_col15, #T_92df0_row1_col16, #T_92df0_row1_col17, #T_92df0_row1_col18, #T_92df0_row1_col19, #T_92df0_row2_col4, #T_92df0_row3_col16, #T_92df0_row4_col18 {\n",
+       "  background-color: #FFC0CB;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_92df0\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_92df0_level0_col0\" class=\"col_heading level0 col0\" >PCIAT-PCIAT_01</th>\n",
+       "      <th id=\"T_92df0_level0_col1\" class=\"col_heading level0 col1\" >PCIAT-PCIAT_02</th>\n",
+       "      <th id=\"T_92df0_level0_col2\" class=\"col_heading level0 col2\" >PCIAT-PCIAT_03</th>\n",
+       "      <th id=\"T_92df0_level0_col3\" class=\"col_heading level0 col3\" >PCIAT-PCIAT_04</th>\n",
+       "      <th id=\"T_92df0_level0_col4\" class=\"col_heading level0 col4\" >PCIAT-PCIAT_05</th>\n",
+       "      <th id=\"T_92df0_level0_col5\" class=\"col_heading level0 col5\" >PCIAT-PCIAT_06</th>\n",
+       "      <th id=\"T_92df0_level0_col6\" class=\"col_heading level0 col6\" >PCIAT-PCIAT_07</th>\n",
+       "      <th id=\"T_92df0_level0_col7\" class=\"col_heading level0 col7\" >PCIAT-PCIAT_08</th>\n",
+       "      <th id=\"T_92df0_level0_col8\" class=\"col_heading level0 col8\" >PCIAT-PCIAT_09</th>\n",
+       "      <th id=\"T_92df0_level0_col9\" class=\"col_heading level0 col9\" >PCIAT-PCIAT_10</th>\n",
+       "      <th id=\"T_92df0_level0_col10\" class=\"col_heading level0 col10\" >PCIAT-PCIAT_11</th>\n",
+       "      <th id=\"T_92df0_level0_col11\" class=\"col_heading level0 col11\" >PCIAT-PCIAT_12</th>\n",
+       "      <th id=\"T_92df0_level0_col12\" class=\"col_heading level0 col12\" >PCIAT-PCIAT_13</th>\n",
+       "      <th id=\"T_92df0_level0_col13\" class=\"col_heading level0 col13\" >PCIAT-PCIAT_14</th>\n",
+       "      <th id=\"T_92df0_level0_col14\" class=\"col_heading level0 col14\" >PCIAT-PCIAT_15</th>\n",
+       "      <th id=\"T_92df0_level0_col15\" class=\"col_heading level0 col15\" >PCIAT-PCIAT_16</th>\n",
+       "      <th id=\"T_92df0_level0_col16\" class=\"col_heading level0 col16\" >PCIAT-PCIAT_17</th>\n",
+       "      <th id=\"T_92df0_level0_col17\" class=\"col_heading level0 col17\" >PCIAT-PCIAT_18</th>\n",
+       "      <th id=\"T_92df0_level0_col18\" class=\"col_heading level0 col18\" >PCIAT-PCIAT_19</th>\n",
+       "      <th id=\"T_92df0_level0_col19\" class=\"col_heading level0 col19\" >PCIAT-PCIAT_20</th>\n",
+       "      <th id=\"T_92df0_level0_col20\" class=\"col_heading level0 col20\" >PCIAT-PCIAT_Total</th>\n",
+       "      <th id=\"T_92df0_level0_col21\" class=\"col_heading level0 col21\" >PCIAT-Season</th>\n",
+       "      <th id=\"T_92df0_level0_col22\" class=\"col_heading level0 col22\" >sii</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_92df0_level0_row0\" class=\"row_heading level0 row0\" >24</th>\n",
+       "      <td id=\"T_92df0_row0_col0\" class=\"data row0 col0\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row0_col1\" class=\"data row0 col1\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row0_col2\" class=\"data row0 col2\" >3.000000</td>\n",
+       "      <td id=\"T_92df0_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row0_col4\" class=\"data row0 col4\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row0_col5\" class=\"data row0 col5\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row0_col6\" class=\"data row0 col6\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row0_col7\" class=\"data row0 col7\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row0_col8\" class=\"data row0 col8\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row0_col9\" class=\"data row0 col9\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row0_col10\" class=\"data row0 col10\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row0_col11\" class=\"data row0 col11\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row0_col12\" class=\"data row0 col12\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row0_col13\" class=\"data row0 col13\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row0_col14\" class=\"data row0 col14\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row0_col15\" class=\"data row0 col15\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row0_col16\" class=\"data row0 col16\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row0_col17\" class=\"data row0 col17\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row0_col18\" class=\"data row0 col18\" >nan</td>\n",
+       "      <td id=\"T_92df0_row0_col19\" class=\"data row0 col19\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row0_col20\" class=\"data row0 col20\" >30.000000</td>\n",
+       "      <td id=\"T_92df0_row0_col21\" class=\"data row0 col21\" >Summer</td>\n",
+       "      <td id=\"T_92df0_row0_col22\" class=\"data row0 col22\" >0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_92df0_level0_row1\" class=\"row_heading level0 row1\" >93</th>\n",
+       "      <td id=\"T_92df0_row1_col0\" class=\"data row1 col0\" >nan</td>\n",
+       "      <td id=\"T_92df0_row1_col1\" class=\"data row1 col1\" >nan</td>\n",
+       "      <td id=\"T_92df0_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
+       "      <td id=\"T_92df0_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
+       "      <td id=\"T_92df0_row1_col4\" class=\"data row1 col4\" >nan</td>\n",
+       "      <td id=\"T_92df0_row1_col5\" class=\"data row1 col5\" >nan</td>\n",
+       "      <td id=\"T_92df0_row1_col6\" class=\"data row1 col6\" >nan</td>\n",
+       "      <td id=\"T_92df0_row1_col7\" class=\"data row1 col7\" >nan</td>\n",
+       "      <td id=\"T_92df0_row1_col8\" class=\"data row1 col8\" >nan</td>\n",
+       "      <td id=\"T_92df0_row1_col9\" class=\"data row1 col9\" >nan</td>\n",
+       "      <td id=\"T_92df0_row1_col10\" class=\"data row1 col10\" >nan</td>\n",
+       "      <td id=\"T_92df0_row1_col11\" class=\"data row1 col11\" >nan</td>\n",
+       "      <td id=\"T_92df0_row1_col12\" class=\"data row1 col12\" >nan</td>\n",
+       "      <td id=\"T_92df0_row1_col13\" class=\"data row1 col13\" >nan</td>\n",
+       "      <td id=\"T_92df0_row1_col14\" class=\"data row1 col14\" >nan</td>\n",
+       "      <td id=\"T_92df0_row1_col15\" class=\"data row1 col15\" >nan</td>\n",
+       "      <td id=\"T_92df0_row1_col16\" class=\"data row1 col16\" >nan</td>\n",
+       "      <td id=\"T_92df0_row1_col17\" class=\"data row1 col17\" >nan</td>\n",
+       "      <td id=\"T_92df0_row1_col18\" class=\"data row1 col18\" >nan</td>\n",
+       "      <td id=\"T_92df0_row1_col19\" class=\"data row1 col19\" >nan</td>\n",
+       "      <td id=\"T_92df0_row1_col20\" class=\"data row1 col20\" >0.000000</td>\n",
+       "      <td id=\"T_92df0_row1_col21\" class=\"data row1 col21\" >Fall</td>\n",
+       "      <td id=\"T_92df0_row1_col22\" class=\"data row1 col22\" >0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_92df0_level0_row2\" class=\"row_heading level0 row2\" >104</th>\n",
+       "      <td id=\"T_92df0_row2_col0\" class=\"data row2 col0\" >5.000000</td>\n",
+       "      <td id=\"T_92df0_row2_col1\" class=\"data row2 col1\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row2_col2\" class=\"data row2 col2\" >4.000000</td>\n",
+       "      <td id=\"T_92df0_row2_col3\" class=\"data row2 col3\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row2_col4\" class=\"data row2 col4\" >nan</td>\n",
+       "      <td id=\"T_92df0_row2_col5\" class=\"data row2 col5\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row2_col6\" class=\"data row2 col6\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row2_col7\" class=\"data row2 col7\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row2_col8\" class=\"data row2 col8\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row2_col9\" class=\"data row2 col9\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row2_col10\" class=\"data row2 col10\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row2_col11\" class=\"data row2 col11\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row2_col12\" class=\"data row2 col12\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row2_col13\" class=\"data row2 col13\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row2_col14\" class=\"data row2 col14\" >3.000000</td>\n",
+       "      <td id=\"T_92df0_row2_col15\" class=\"data row2 col15\" >3.000000</td>\n",
+       "      <td id=\"T_92df0_row2_col16\" class=\"data row2 col16\" >3.000000</td>\n",
+       "      <td id=\"T_92df0_row2_col17\" class=\"data row2 col17\" >3.000000</td>\n",
+       "      <td id=\"T_92df0_row2_col18\" class=\"data row2 col18\" >3.000000</td>\n",
+       "      <td id=\"T_92df0_row2_col19\" class=\"data row2 col19\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row2_col20\" class=\"data row2 col20\" >45.000000</td>\n",
+       "      <td id=\"T_92df0_row2_col21\" class=\"data row2 col21\" >Fall</td>\n",
+       "      <td id=\"T_92df0_row2_col22\" class=\"data row2 col22\" >1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_92df0_level0_row3\" class=\"row_heading level0 row3\" >141</th>\n",
+       "      <td id=\"T_92df0_row3_col0\" class=\"data row3 col0\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row3_col1\" class=\"data row3 col1\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row3_col2\" class=\"data row3 col2\" >4.000000</td>\n",
+       "      <td id=\"T_92df0_row3_col3\" class=\"data row3 col3\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row3_col4\" class=\"data row3 col4\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row3_col5\" class=\"data row3 col5\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row3_col6\" class=\"data row3 col6\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row3_col7\" class=\"data row3 col7\" >3.000000</td>\n",
+       "      <td id=\"T_92df0_row3_col8\" class=\"data row3 col8\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row3_col9\" class=\"data row3 col9\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row3_col10\" class=\"data row3 col10\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row3_col11\" class=\"data row3 col11\" >0.000000</td>\n",
+       "      <td id=\"T_92df0_row3_col12\" class=\"data row3 col12\" >0.000000</td>\n",
+       "      <td id=\"T_92df0_row3_col13\" class=\"data row3 col13\" >0.000000</td>\n",
+       "      <td id=\"T_92df0_row3_col14\" class=\"data row3 col14\" >3.000000</td>\n",
+       "      <td id=\"T_92df0_row3_col15\" class=\"data row3 col15\" >0.000000</td>\n",
+       "      <td id=\"T_92df0_row3_col16\" class=\"data row3 col16\" >nan</td>\n",
+       "      <td id=\"T_92df0_row3_col17\" class=\"data row3 col17\" >0.000000</td>\n",
+       "      <td id=\"T_92df0_row3_col18\" class=\"data row3 col18\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row3_col19\" class=\"data row3 col19\" >0.000000</td>\n",
+       "      <td id=\"T_92df0_row3_col20\" class=\"data row3 col20\" >26.000000</td>\n",
+       "      <td id=\"T_92df0_row3_col21\" class=\"data row3 col21\" >Winter</td>\n",
+       "      <td id=\"T_92df0_row3_col22\" class=\"data row3 col22\" >0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_92df0_level0_row4\" class=\"row_heading level0 row4\" >142</th>\n",
+       "      <td id=\"T_92df0_row4_col0\" class=\"data row4 col0\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row4_col1\" class=\"data row4 col1\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row4_col2\" class=\"data row4 col2\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row4_col3\" class=\"data row4 col3\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row4_col4\" class=\"data row4 col4\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row4_col5\" class=\"data row4 col5\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row4_col6\" class=\"data row4 col6\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row4_col7\" class=\"data row4 col7\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row4_col8\" class=\"data row4 col8\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row4_col9\" class=\"data row4 col9\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row4_col10\" class=\"data row4 col10\" >3.000000</td>\n",
+       "      <td id=\"T_92df0_row4_col11\" class=\"data row4 col11\" >0.000000</td>\n",
+       "      <td id=\"T_92df0_row4_col12\" class=\"data row4 col12\" >2.000000</td>\n",
+       "      <td id=\"T_92df0_row4_col13\" class=\"data row4 col13\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row4_col14\" class=\"data row4 col14\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row4_col15\" class=\"data row4 col15\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row4_col16\" class=\"data row4 col16\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row4_col17\" class=\"data row4 col17\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row4_col18\" class=\"data row4 col18\" >nan</td>\n",
+       "      <td id=\"T_92df0_row4_col19\" class=\"data row4 col19\" >1.000000</td>\n",
+       "      <td id=\"T_92df0_row4_col20\" class=\"data row4 col20\" >26.000000</td>\n",
+       "      <td id=\"T_92df0_row4_col21\" class=\"data row4 col21\" >Spring</td>\n",
+       "      <td id=\"T_92df0_row4_col22\" class=\"data row4 col22\" >0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x1b8f53abdf0>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_with_sii = train[train['sii'].notna()][columns_not_in_test]\n",
+    "train_with_sii[train_with_sii.isna().any(axis=1)].head().style.applymap(\n",
+    "    lambda x: 'background-color: #FFC0CB' if pd.isna(x) else ''\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PCIAT_cols = [f'PCIAT-PCIAT_{i+1:02d}' for i in range(20)]\n",
+    "recalc_total_score = train_with_sii[PCIAT_cols].sum(\n",
+    "    axis=1, skipna=True\n",
+    ")\n",
+    "(recalc_total_score == train_with_sii['PCIAT-PCIAT_Total']).all()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def recalculate_sii(row):\n",
+    "    if pd.isna(row['PCIAT-PCIAT_Total']):\n",
+    "        return np.nan\n",
+    "    max_possible = row['PCIAT-PCIAT_Total'] + row[PCIAT_cols].isna().sum() * 5\n",
+    "    if row['PCIAT-PCIAT_Total'] <= 30 and max_possible <= 30:\n",
+    "        return 0\n",
+    "    elif 31 <= row['PCIAT-PCIAT_Total'] <= 49 and max_possible <= 49:\n",
+    "        return 1\n",
+    "    elif 50 <= row['PCIAT-PCIAT_Total'] <= 79 and max_possible <= 79:\n",
+    "        return 2\n",
+    "    elif row['PCIAT-PCIAT_Total'] >= 80 and max_possible >= 80:\n",
+    "        return 3\n",
+    "    return np.nan\n",
+    "\n",
+    "train['recalc_sii'] = train.apply(recalculate_sii, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_b5224_row0_col18, #T_b5224_row0_col22, #T_b5224_row1_col0, #T_b5224_row1_col1, #T_b5224_row1_col2, #T_b5224_row1_col3, #T_b5224_row1_col4, #T_b5224_row1_col5, #T_b5224_row1_col6, #T_b5224_row1_col7, #T_b5224_row1_col8, #T_b5224_row1_col9, #T_b5224_row1_col10, #T_b5224_row1_col11, #T_b5224_row1_col12, #T_b5224_row1_col13, #T_b5224_row1_col14, #T_b5224_row1_col15, #T_b5224_row1_col16, #T_b5224_row1_col17, #T_b5224_row1_col18, #T_b5224_row1_col19, #T_b5224_row1_col22, #T_b5224_row2_col4, #T_b5224_row2_col22, #T_b5224_row3_col16, #T_b5224_row3_col22, #T_b5224_row4_col18, #T_b5224_row4_col22, #T_b5224_row5_col15, #T_b5224_row5_col22, #T_b5224_row6_col7, #T_b5224_row6_col8, #T_b5224_row6_col9, #T_b5224_row6_col18, #T_b5224_row6_col19, #T_b5224_row6_col22, #T_b5224_row7_col13, #T_b5224_row7_col14, #T_b5224_row7_col22, #T_b5224_row8_col7, #T_b5224_row8_col22, #T_b5224_row9_col11, #T_b5224_row9_col22, #T_b5224_row10_col0, #T_b5224_row10_col1, #T_b5224_row10_col2, #T_b5224_row10_col3, #T_b5224_row10_col4, #T_b5224_row10_col5, #T_b5224_row10_col6, #T_b5224_row10_col7, #T_b5224_row10_col8, #T_b5224_row10_col9, #T_b5224_row10_col22, #T_b5224_row11_col0, #T_b5224_row11_col3, #T_b5224_row11_col16, #T_b5224_row11_col22, #T_b5224_row12_col14, #T_b5224_row12_col17, #T_b5224_row12_col22, #T_b5224_row13_col17, #T_b5224_row13_col18, #T_b5224_row13_col22, #T_b5224_row14_col15, #T_b5224_row14_col22, #T_b5224_row15_col4, #T_b5224_row15_col22, #T_b5224_row16_col6, #T_b5224_row16_col8, #T_b5224_row16_col22 {\n",
+       "  background-color: #FFC0CB;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_b5224\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_b5224_level0_col0\" class=\"col_heading level0 col0\" >PCIAT-PCIAT_01</th>\n",
+       "      <th id=\"T_b5224_level0_col1\" class=\"col_heading level0 col1\" >PCIAT-PCIAT_02</th>\n",
+       "      <th id=\"T_b5224_level0_col2\" class=\"col_heading level0 col2\" >PCIAT-PCIAT_03</th>\n",
+       "      <th id=\"T_b5224_level0_col3\" class=\"col_heading level0 col3\" >PCIAT-PCIAT_04</th>\n",
+       "      <th id=\"T_b5224_level0_col4\" class=\"col_heading level0 col4\" >PCIAT-PCIAT_05</th>\n",
+       "      <th id=\"T_b5224_level0_col5\" class=\"col_heading level0 col5\" >PCIAT-PCIAT_06</th>\n",
+       "      <th id=\"T_b5224_level0_col6\" class=\"col_heading level0 col6\" >PCIAT-PCIAT_07</th>\n",
+       "      <th id=\"T_b5224_level0_col7\" class=\"col_heading level0 col7\" >PCIAT-PCIAT_08</th>\n",
+       "      <th id=\"T_b5224_level0_col8\" class=\"col_heading level0 col8\" >PCIAT-PCIAT_09</th>\n",
+       "      <th id=\"T_b5224_level0_col9\" class=\"col_heading level0 col9\" >PCIAT-PCIAT_10</th>\n",
+       "      <th id=\"T_b5224_level0_col10\" class=\"col_heading level0 col10\" >PCIAT-PCIAT_11</th>\n",
+       "      <th id=\"T_b5224_level0_col11\" class=\"col_heading level0 col11\" >PCIAT-PCIAT_12</th>\n",
+       "      <th id=\"T_b5224_level0_col12\" class=\"col_heading level0 col12\" >PCIAT-PCIAT_13</th>\n",
+       "      <th id=\"T_b5224_level0_col13\" class=\"col_heading level0 col13\" >PCIAT-PCIAT_14</th>\n",
+       "      <th id=\"T_b5224_level0_col14\" class=\"col_heading level0 col14\" >PCIAT-PCIAT_15</th>\n",
+       "      <th id=\"T_b5224_level0_col15\" class=\"col_heading level0 col15\" >PCIAT-PCIAT_16</th>\n",
+       "      <th id=\"T_b5224_level0_col16\" class=\"col_heading level0 col16\" >PCIAT-PCIAT_17</th>\n",
+       "      <th id=\"T_b5224_level0_col17\" class=\"col_heading level0 col17\" >PCIAT-PCIAT_18</th>\n",
+       "      <th id=\"T_b5224_level0_col18\" class=\"col_heading level0 col18\" >PCIAT-PCIAT_19</th>\n",
+       "      <th id=\"T_b5224_level0_col19\" class=\"col_heading level0 col19\" >PCIAT-PCIAT_20</th>\n",
+       "      <th id=\"T_b5224_level0_col20\" class=\"col_heading level0 col20\" >PCIAT-PCIAT_Total</th>\n",
+       "      <th id=\"T_b5224_level0_col21\" class=\"col_heading level0 col21\" >sii</th>\n",
+       "      <th id=\"T_b5224_level0_col22\" class=\"col_heading level0 col22\" >recalc_sii</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b5224_level0_row0\" class=\"row_heading level0 row0\" >24</th>\n",
+       "      <td id=\"T_b5224_row0_col0\" class=\"data row0 col0\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col1\" class=\"data row0 col1\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col2\" class=\"data row0 col2\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col4\" class=\"data row0 col4\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col5\" class=\"data row0 col5\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col6\" class=\"data row0 col6\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col7\" class=\"data row0 col7\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col8\" class=\"data row0 col8\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col9\" class=\"data row0 col9\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col10\" class=\"data row0 col10\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col11\" class=\"data row0 col11\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col12\" class=\"data row0 col12\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col13\" class=\"data row0 col13\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col14\" class=\"data row0 col14\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col15\" class=\"data row0 col15\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col16\" class=\"data row0 col16\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col17\" class=\"data row0 col17\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col18\" class=\"data row0 col18\" >nan</td>\n",
+       "      <td id=\"T_b5224_row0_col19\" class=\"data row0 col19\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col20\" class=\"data row0 col20\" >30.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col21\" class=\"data row0 col21\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row0_col22\" class=\"data row0 col22\" >nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b5224_level0_row1\" class=\"row_heading level0 row1\" >93</th>\n",
+       "      <td id=\"T_b5224_row1_col0\" class=\"data row1 col0\" >nan</td>\n",
+       "      <td id=\"T_b5224_row1_col1\" class=\"data row1 col1\" >nan</td>\n",
+       "      <td id=\"T_b5224_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
+       "      <td id=\"T_b5224_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
+       "      <td id=\"T_b5224_row1_col4\" class=\"data row1 col4\" >nan</td>\n",
+       "      <td id=\"T_b5224_row1_col5\" class=\"data row1 col5\" >nan</td>\n",
+       "      <td id=\"T_b5224_row1_col6\" class=\"data row1 col6\" >nan</td>\n",
+       "      <td id=\"T_b5224_row1_col7\" class=\"data row1 col7\" >nan</td>\n",
+       "      <td id=\"T_b5224_row1_col8\" class=\"data row1 col8\" >nan</td>\n",
+       "      <td id=\"T_b5224_row1_col9\" class=\"data row1 col9\" >nan</td>\n",
+       "      <td id=\"T_b5224_row1_col10\" class=\"data row1 col10\" >nan</td>\n",
+       "      <td id=\"T_b5224_row1_col11\" class=\"data row1 col11\" >nan</td>\n",
+       "      <td id=\"T_b5224_row1_col12\" class=\"data row1 col12\" >nan</td>\n",
+       "      <td id=\"T_b5224_row1_col13\" class=\"data row1 col13\" >nan</td>\n",
+       "      <td id=\"T_b5224_row1_col14\" class=\"data row1 col14\" >nan</td>\n",
+       "      <td id=\"T_b5224_row1_col15\" class=\"data row1 col15\" >nan</td>\n",
+       "      <td id=\"T_b5224_row1_col16\" class=\"data row1 col16\" >nan</td>\n",
+       "      <td id=\"T_b5224_row1_col17\" class=\"data row1 col17\" >nan</td>\n",
+       "      <td id=\"T_b5224_row1_col18\" class=\"data row1 col18\" >nan</td>\n",
+       "      <td id=\"T_b5224_row1_col19\" class=\"data row1 col19\" >nan</td>\n",
+       "      <td id=\"T_b5224_row1_col20\" class=\"data row1 col20\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row1_col21\" class=\"data row1 col21\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row1_col22\" class=\"data row1 col22\" >nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b5224_level0_row2\" class=\"row_heading level0 row2\" >104</th>\n",
+       "      <td id=\"T_b5224_row2_col0\" class=\"data row2 col0\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col1\" class=\"data row2 col1\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col2\" class=\"data row2 col2\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col3\" class=\"data row2 col3\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col4\" class=\"data row2 col4\" >nan</td>\n",
+       "      <td id=\"T_b5224_row2_col5\" class=\"data row2 col5\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col6\" class=\"data row2 col6\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col7\" class=\"data row2 col7\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col8\" class=\"data row2 col8\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col9\" class=\"data row2 col9\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col10\" class=\"data row2 col10\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col11\" class=\"data row2 col11\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col12\" class=\"data row2 col12\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col13\" class=\"data row2 col13\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col14\" class=\"data row2 col14\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col15\" class=\"data row2 col15\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col16\" class=\"data row2 col16\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col17\" class=\"data row2 col17\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col18\" class=\"data row2 col18\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col19\" class=\"data row2 col19\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col20\" class=\"data row2 col20\" >45.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col21\" class=\"data row2 col21\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row2_col22\" class=\"data row2 col22\" >nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b5224_level0_row3\" class=\"row_heading level0 row3\" >141</th>\n",
+       "      <td id=\"T_b5224_row3_col0\" class=\"data row3 col0\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col1\" class=\"data row3 col1\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col2\" class=\"data row3 col2\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col3\" class=\"data row3 col3\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col4\" class=\"data row3 col4\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col5\" class=\"data row3 col5\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col6\" class=\"data row3 col6\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col7\" class=\"data row3 col7\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col8\" class=\"data row3 col8\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col9\" class=\"data row3 col9\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col10\" class=\"data row3 col10\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col11\" class=\"data row3 col11\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col12\" class=\"data row3 col12\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col13\" class=\"data row3 col13\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col14\" class=\"data row3 col14\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col15\" class=\"data row3 col15\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col16\" class=\"data row3 col16\" >nan</td>\n",
+       "      <td id=\"T_b5224_row3_col17\" class=\"data row3 col17\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col18\" class=\"data row3 col18\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col19\" class=\"data row3 col19\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col20\" class=\"data row3 col20\" >26.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col21\" class=\"data row3 col21\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row3_col22\" class=\"data row3 col22\" >nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b5224_level0_row4\" class=\"row_heading level0 row4\" >142</th>\n",
+       "      <td id=\"T_b5224_row4_col0\" class=\"data row4 col0\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col1\" class=\"data row4 col1\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col2\" class=\"data row4 col2\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col3\" class=\"data row4 col3\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col4\" class=\"data row4 col4\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col5\" class=\"data row4 col5\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col6\" class=\"data row4 col6\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col7\" class=\"data row4 col7\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col8\" class=\"data row4 col8\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col9\" class=\"data row4 col9\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col10\" class=\"data row4 col10\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col11\" class=\"data row4 col11\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col12\" class=\"data row4 col12\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col13\" class=\"data row4 col13\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col14\" class=\"data row4 col14\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col15\" class=\"data row4 col15\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col16\" class=\"data row4 col16\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col17\" class=\"data row4 col17\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col18\" class=\"data row4 col18\" >nan</td>\n",
+       "      <td id=\"T_b5224_row4_col19\" class=\"data row4 col19\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col20\" class=\"data row4 col20\" >26.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col21\" class=\"data row4 col21\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row4_col22\" class=\"data row4 col22\" >nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b5224_level0_row5\" class=\"row_heading level0 row5\" >270</th>\n",
+       "      <td id=\"T_b5224_row5_col0\" class=\"data row5 col0\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col1\" class=\"data row5 col1\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col2\" class=\"data row5 col2\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col3\" class=\"data row5 col3\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col4\" class=\"data row5 col4\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col5\" class=\"data row5 col5\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col6\" class=\"data row5 col6\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col7\" class=\"data row5 col7\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col8\" class=\"data row5 col8\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col9\" class=\"data row5 col9\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col10\" class=\"data row5 col10\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col11\" class=\"data row5 col11\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col12\" class=\"data row5 col12\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col13\" class=\"data row5 col13\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col14\" class=\"data row5 col14\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col15\" class=\"data row5 col15\" >nan</td>\n",
+       "      <td id=\"T_b5224_row5_col16\" class=\"data row5 col16\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col17\" class=\"data row5 col17\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col18\" class=\"data row5 col18\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col19\" class=\"data row5 col19\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col20\" class=\"data row5 col20\" >48.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col21\" class=\"data row5 col21\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row5_col22\" class=\"data row5 col22\" >nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b5224_level0_row6\" class=\"row_heading level0 row6\" >368</th>\n",
+       "      <td id=\"T_b5224_row6_col0\" class=\"data row6 col0\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row6_col1\" class=\"data row6 col1\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row6_col2\" class=\"data row6 col2\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row6_col3\" class=\"data row6 col3\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row6_col4\" class=\"data row6 col4\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row6_col5\" class=\"data row6 col5\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row6_col6\" class=\"data row6 col6\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row6_col7\" class=\"data row6 col7\" >nan</td>\n",
+       "      <td id=\"T_b5224_row6_col8\" class=\"data row6 col8\" >nan</td>\n",
+       "      <td id=\"T_b5224_row6_col9\" class=\"data row6 col9\" >nan</td>\n",
+       "      <td id=\"T_b5224_row6_col10\" class=\"data row6 col10\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row6_col11\" class=\"data row6 col11\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row6_col12\" class=\"data row6 col12\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row6_col13\" class=\"data row6 col13\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row6_col14\" class=\"data row6 col14\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row6_col15\" class=\"data row6 col15\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row6_col16\" class=\"data row6 col16\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row6_col17\" class=\"data row6 col17\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row6_col18\" class=\"data row6 col18\" >nan</td>\n",
+       "      <td id=\"T_b5224_row6_col19\" class=\"data row6 col19\" >nan</td>\n",
+       "      <td id=\"T_b5224_row6_col20\" class=\"data row6 col20\" >31.000000</td>\n",
+       "      <td id=\"T_b5224_row6_col21\" class=\"data row6 col21\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row6_col22\" class=\"data row6 col22\" >nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b5224_level0_row7\" class=\"row_heading level0 row7\" >592</th>\n",
+       "      <td id=\"T_b5224_row7_col0\" class=\"data row7 col0\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row7_col1\" class=\"data row7 col1\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row7_col2\" class=\"data row7 col2\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row7_col3\" class=\"data row7 col3\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row7_col4\" class=\"data row7 col4\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row7_col5\" class=\"data row7 col5\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row7_col6\" class=\"data row7 col6\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row7_col8\" class=\"data row7 col8\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row7_col9\" class=\"data row7 col9\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row7_col10\" class=\"data row7 col10\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row7_col11\" class=\"data row7 col11\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row7_col12\" class=\"data row7 col12\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row7_col13\" class=\"data row7 col13\" >nan</td>\n",
+       "      <td id=\"T_b5224_row7_col14\" class=\"data row7 col14\" >nan</td>\n",
+       "      <td id=\"T_b5224_row7_col15\" class=\"data row7 col15\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row7_col16\" class=\"data row7 col16\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row7_col17\" class=\"data row7 col17\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row7_col18\" class=\"data row7 col18\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row7_col19\" class=\"data row7 col19\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row7_col20\" class=\"data row7 col20\" >21.000000</td>\n",
+       "      <td id=\"T_b5224_row7_col21\" class=\"data row7 col21\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row7_col22\" class=\"data row7 col22\" >nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b5224_level0_row8\" class=\"row_heading level0 row8\" >724</th>\n",
+       "      <td id=\"T_b5224_row8_col0\" class=\"data row8 col0\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col1\" class=\"data row8 col1\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col2\" class=\"data row8 col2\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col3\" class=\"data row8 col3\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col4\" class=\"data row8 col4\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col5\" class=\"data row8 col5\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col6\" class=\"data row8 col6\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col7\" class=\"data row8 col7\" >nan</td>\n",
+       "      <td id=\"T_b5224_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col9\" class=\"data row8 col9\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col10\" class=\"data row8 col10\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col11\" class=\"data row8 col11\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col12\" class=\"data row8 col12\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col13\" class=\"data row8 col13\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col14\" class=\"data row8 col14\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col15\" class=\"data row8 col15\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col16\" class=\"data row8 col16\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col17\" class=\"data row8 col17\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col18\" class=\"data row8 col18\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col19\" class=\"data row8 col19\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col20\" class=\"data row8 col20\" >29.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col21\" class=\"data row8 col21\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row8_col22\" class=\"data row8 col22\" >nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b5224_level0_row9\" class=\"row_heading level0 row9\" >877</th>\n",
+       "      <td id=\"T_b5224_row9_col0\" class=\"data row9 col0\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col1\" class=\"data row9 col1\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col2\" class=\"data row9 col2\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col3\" class=\"data row9 col3\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col4\" class=\"data row9 col4\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col5\" class=\"data row9 col5\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col6\" class=\"data row9 col6\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col7\" class=\"data row9 col7\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col8\" class=\"data row9 col8\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col9\" class=\"data row9 col9\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col10\" class=\"data row9 col10\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col11\" class=\"data row9 col11\" >nan</td>\n",
+       "      <td id=\"T_b5224_row9_col12\" class=\"data row9 col12\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col13\" class=\"data row9 col13\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col14\" class=\"data row9 col14\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col15\" class=\"data row9 col15\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col16\" class=\"data row9 col16\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col17\" class=\"data row9 col17\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col18\" class=\"data row9 col18\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col19\" class=\"data row9 col19\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col20\" class=\"data row9 col20\" >78.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col21\" class=\"data row9 col21\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row9_col22\" class=\"data row9 col22\" >nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b5224_level0_row10\" class=\"row_heading level0 row10\" >1706</th>\n",
+       "      <td id=\"T_b5224_row10_col0\" class=\"data row10 col0\" >nan</td>\n",
+       "      <td id=\"T_b5224_row10_col1\" class=\"data row10 col1\" >nan</td>\n",
+       "      <td id=\"T_b5224_row10_col2\" class=\"data row10 col2\" >nan</td>\n",
+       "      <td id=\"T_b5224_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
+       "      <td id=\"T_b5224_row10_col4\" class=\"data row10 col4\" >nan</td>\n",
+       "      <td id=\"T_b5224_row10_col5\" class=\"data row10 col5\" >nan</td>\n",
+       "      <td id=\"T_b5224_row10_col6\" class=\"data row10 col6\" >nan</td>\n",
+       "      <td id=\"T_b5224_row10_col7\" class=\"data row10 col7\" >nan</td>\n",
+       "      <td id=\"T_b5224_row10_col8\" class=\"data row10 col8\" >nan</td>\n",
+       "      <td id=\"T_b5224_row10_col9\" class=\"data row10 col9\" >nan</td>\n",
+       "      <td id=\"T_b5224_row10_col10\" class=\"data row10 col10\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row10_col11\" class=\"data row10 col11\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row10_col12\" class=\"data row10 col12\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row10_col13\" class=\"data row10 col13\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row10_col14\" class=\"data row10 col14\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row10_col15\" class=\"data row10 col15\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row10_col16\" class=\"data row10 col16\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row10_col17\" class=\"data row10 col17\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row10_col18\" class=\"data row10 col18\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row10_col19\" class=\"data row10 col19\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row10_col20\" class=\"data row10 col20\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row10_col21\" class=\"data row10 col21\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row10_col22\" class=\"data row10 col22\" >nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b5224_level0_row11\" class=\"row_heading level0 row11\" >1911</th>\n",
+       "      <td id=\"T_b5224_row11_col0\" class=\"data row11 col0\" >nan</td>\n",
+       "      <td id=\"T_b5224_row11_col1\" class=\"data row11 col1\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row11_col2\" class=\"data row11 col2\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row11_col3\" class=\"data row11 col3\" >nan</td>\n",
+       "      <td id=\"T_b5224_row11_col4\" class=\"data row11 col4\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row11_col5\" class=\"data row11 col5\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row11_col6\" class=\"data row11 col6\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row11_col7\" class=\"data row11 col7\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row11_col8\" class=\"data row11 col8\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row11_col9\" class=\"data row11 col9\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row11_col10\" class=\"data row11 col10\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row11_col11\" class=\"data row11 col11\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row11_col12\" class=\"data row11 col12\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row11_col13\" class=\"data row11 col13\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row11_col14\" class=\"data row11 col14\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row11_col15\" class=\"data row11 col15\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row11_col16\" class=\"data row11 col16\" >nan</td>\n",
+       "      <td id=\"T_b5224_row11_col17\" class=\"data row11 col17\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row11_col18\" class=\"data row11 col18\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row11_col19\" class=\"data row11 col19\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row11_col20\" class=\"data row11 col20\" >38.000000</td>\n",
+       "      <td id=\"T_b5224_row11_col21\" class=\"data row11 col21\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row11_col22\" class=\"data row11 col22\" >nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b5224_level0_row12\" class=\"row_heading level0 row12\" >2285</th>\n",
+       "      <td id=\"T_b5224_row12_col0\" class=\"data row12 col0\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row12_col1\" class=\"data row12 col1\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row12_col2\" class=\"data row12 col2\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row12_col3\" class=\"data row12 col3\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row12_col4\" class=\"data row12 col4\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row12_col5\" class=\"data row12 col5\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row12_col6\" class=\"data row12 col6\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row12_col7\" class=\"data row12 col7\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row12_col8\" class=\"data row12 col8\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row12_col9\" class=\"data row12 col9\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row12_col10\" class=\"data row12 col10\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row12_col11\" class=\"data row12 col11\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row12_col12\" class=\"data row12 col12\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row12_col13\" class=\"data row12 col13\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row12_col14\" class=\"data row12 col14\" >nan</td>\n",
+       "      <td id=\"T_b5224_row12_col15\" class=\"data row12 col15\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row12_col16\" class=\"data row12 col16\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row12_col17\" class=\"data row12 col17\" >nan</td>\n",
+       "      <td id=\"T_b5224_row12_col18\" class=\"data row12 col18\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row12_col19\" class=\"data row12 col19\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row12_col20\" class=\"data row12 col20\" >27.000000</td>\n",
+       "      <td id=\"T_b5224_row12_col21\" class=\"data row12 col21\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row12_col22\" class=\"data row12 col22\" >nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b5224_level0_row13\" class=\"row_heading level0 row13\" >3037</th>\n",
+       "      <td id=\"T_b5224_row13_col0\" class=\"data row13 col0\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row13_col1\" class=\"data row13 col1\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row13_col2\" class=\"data row13 col2\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row13_col3\" class=\"data row13 col3\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row13_col4\" class=\"data row13 col4\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row13_col5\" class=\"data row13 col5\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row13_col6\" class=\"data row13 col6\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row13_col7\" class=\"data row13 col7\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row13_col8\" class=\"data row13 col8\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row13_col9\" class=\"data row13 col9\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row13_col10\" class=\"data row13 col10\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row13_col11\" class=\"data row13 col11\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row13_col12\" class=\"data row13 col12\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row13_col13\" class=\"data row13 col13\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row13_col14\" class=\"data row13 col14\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row13_col15\" class=\"data row13 col15\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row13_col16\" class=\"data row13 col16\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row13_col17\" class=\"data row13 col17\" >nan</td>\n",
+       "      <td id=\"T_b5224_row13_col18\" class=\"data row13 col18\" >nan</td>\n",
+       "      <td id=\"T_b5224_row13_col19\" class=\"data row13 col19\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row13_col20\" class=\"data row13 col20\" >45.000000</td>\n",
+       "      <td id=\"T_b5224_row13_col21\" class=\"data row13 col21\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row13_col22\" class=\"data row13 col22\" >nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b5224_level0_row14\" class=\"row_heading level0 row14\" >3500</th>\n",
+       "      <td id=\"T_b5224_row14_col0\" class=\"data row14 col0\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col1\" class=\"data row14 col1\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col2\" class=\"data row14 col2\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col3\" class=\"data row14 col3\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col4\" class=\"data row14 col4\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col5\" class=\"data row14 col5\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col6\" class=\"data row14 col6\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col7\" class=\"data row14 col7\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col8\" class=\"data row14 col8\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col9\" class=\"data row14 col9\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col10\" class=\"data row14 col10\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col11\" class=\"data row14 col11\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col12\" class=\"data row14 col12\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col13\" class=\"data row14 col13\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col14\" class=\"data row14 col14\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col15\" class=\"data row14 col15\" >nan</td>\n",
+       "      <td id=\"T_b5224_row14_col16\" class=\"data row14 col16\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col17\" class=\"data row14 col17\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col18\" class=\"data row14 col18\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col19\" class=\"data row14 col19\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col20\" class=\"data row14 col20\" >47.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col21\" class=\"data row14 col21\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row14_col22\" class=\"data row14 col22\" >nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b5224_level0_row15\" class=\"row_heading level0 row15\" >3672</th>\n",
+       "      <td id=\"T_b5224_row15_col0\" class=\"data row15 col0\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col1\" class=\"data row15 col1\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col2\" class=\"data row15 col2\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col3\" class=\"data row15 col3\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col4\" class=\"data row15 col4\" >nan</td>\n",
+       "      <td id=\"T_b5224_row15_col5\" class=\"data row15 col5\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col6\" class=\"data row15 col6\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col7\" class=\"data row15 col7\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col8\" class=\"data row15 col8\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col9\" class=\"data row15 col9\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col10\" class=\"data row15 col10\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col11\" class=\"data row15 col11\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col12\" class=\"data row15 col12\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col13\" class=\"data row15 col13\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col14\" class=\"data row15 col14\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col15\" class=\"data row15 col15\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col16\" class=\"data row15 col16\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col17\" class=\"data row15 col17\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col18\" class=\"data row15 col18\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col19\" class=\"data row15 col19\" >3.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col20\" class=\"data row15 col20\" >49.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col21\" class=\"data row15 col21\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row15_col22\" class=\"data row15 col22\" >nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b5224_level0_row16\" class=\"row_heading level0 row16\" >3757</th>\n",
+       "      <td id=\"T_b5224_row16_col0\" class=\"data row16 col0\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row16_col1\" class=\"data row16 col1\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row16_col2\" class=\"data row16 col2\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row16_col3\" class=\"data row16 col3\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row16_col4\" class=\"data row16 col4\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row16_col5\" class=\"data row16 col5\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row16_col6\" class=\"data row16 col6\" >nan</td>\n",
+       "      <td id=\"T_b5224_row16_col7\" class=\"data row16 col7\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row16_col8\" class=\"data row16 col8\" >nan</td>\n",
+       "      <td id=\"T_b5224_row16_col9\" class=\"data row16 col9\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row16_col10\" class=\"data row16 col10\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row16_col11\" class=\"data row16 col11\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row16_col12\" class=\"data row16 col12\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row16_col13\" class=\"data row16 col13\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row16_col14\" class=\"data row16 col14\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row16_col15\" class=\"data row16 col15\" >5.000000</td>\n",
+       "      <td id=\"T_b5224_row16_col16\" class=\"data row16 col16\" >2.000000</td>\n",
+       "      <td id=\"T_b5224_row16_col17\" class=\"data row16 col17\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row16_col18\" class=\"data row16 col18\" >0.000000</td>\n",
+       "      <td id=\"T_b5224_row16_col19\" class=\"data row16 col19\" >4.000000</td>\n",
+       "      <td id=\"T_b5224_row16_col20\" class=\"data row16 col20\" >42.000000</td>\n",
+       "      <td id=\"T_b5224_row16_col21\" class=\"data row16 col21\" >1.000000</td>\n",
+       "      <td id=\"T_b5224_row16_col22\" class=\"data row16 col22\" >nan</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x1b89560ddb0>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mismatch_rows = train[\n",
+    "    (train['recalc_sii'] != train['sii']) & train['sii'].notna()\n",
+    "]\n",
+    "\n",
+    "mismatch_rows[PCIAT_cols + [\n",
+    "    'PCIAT-PCIAT_Total', 'sii', 'recalc_sii'\n",
+    "]].style.applymap(\n",
+    "    lambda x: 'background-color: #FFC0CB' if pd.isna(x) else ''\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train['sii'] = train['recalc_sii']\n",
+    "train['complete_resp_total'] = train['PCIAT-PCIAT_Total'].where(\n",
+    "    train[PCIAT_cols].notna().all(axis=1), np.nan\n",
+    ")\n",
+    "\n",
+    "sii_map = {0: '0 (None)', 1: '1 (Mild)', 2: '2 (Moderate)', 3: '3 (Severe)'}\n",
+    "train['sii'] = train['sii'].map(sii_map).fillna('Missing')\n",
+    "\n",
+    "sii_order = ['Missing', '0 (None)', '1 (Mild)', '2 (Moderate)', '3 (Severe)']\n",
+    "train['sii'] = pd.Categorical(train['sii'], categories=sii_order, ordered=True)\n",
+    "\n",
+    "train.drop(columns='recalc_sii', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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DBw+MunXrGiVLljTOnDljUbdq1SrD09PTGD58uLls+vTphqenp7Fv375E+3zUunXrjIoVK5pj9PT0NIoXL2689957xtq1ay3mMSwszKhcubLRtGlTIzIy0qKfGTNmGJ6ensb3339vGIZhnD592vD09DQ+/vhji3aTJ082PD09jcDAQMMwDOP77783PD09jYULF1q0Cw0NNRo0aGBUrlzZCAsLMwzj/+ZxyJAh8c7D09PTaN26tfnnhObx8OHDhqenpzF+/HiLY8ePH294eXkZ//33X5KvVUKfkWHDhhmenp7G6NGjLdo2atTI8PT0NCZMmGBR3rRpU+O1114zv35x7+M33njDuHXrlrnd1atXjTJlyhg1a9Y0z0Hv3r2NkiVLGkFBQRZ97ty50/D09DTGjRtnLkvs8/L+++/HO69atWoZnp6expYtW8xlN2/eNIoXL254enoa27dvN5ffuXPHeO2114w2bdqYy8aMGWN4e3sbf/zxh0W/p06dMkqWLGl07do1Xlzz5s2zaDtr1izD09PTWLNmjbnsad7LSc3NqFGjLNpu2LDB4vMcGxtr1KpVy6hatapx8+ZNc7u7d+8adevWtej3aT4DhmEYx44dM0qUKGE0btzYuHfvntGgQQOjTJkyxtmzZ594TnXr1jV8fX3jlb/zzjvGW2+9ZVF2//5945133rF4Hyb2Hli7dm28PufOnWt4enoa+/fvf2JcAIDnG9f8T44rs13zx43/zz//GDdv3jT/uXDhgrFu3TqjUqVKRqlSpYzTp08bhmEYf/31l+Hp6Wn079//iX0/Lu41HDZsWLy6Fi1aGJ6ensaxY8fMZZ07d453Hfo4T09Po1atWk8dy5NcvHgx3vvCMAzj4MGDhqenp9G+fXsjKirKXB4ZGWm8//778a6ZqlevbhFfTEyMUblyZePdd9+N9/7+/PPPDU9PT+OHH34wlz3+ewyA/8O2BkAioqOjZTKZEq338PBQWFiYJkyYoH/++UeGYcjW1lYrVqww337zJJUqVUr2E1r9/PyULVs288/u7u569913dfXqVf3999/J6uNp7dmzR3fv3jV/e/6o3r17y8nJKd5t5gUKFIj31M+41Zs3btxIdKzjx4/rwoULatiwYbxvplu3bq38+fPrxx9/tLid+mm0aNFCv/76q2bNmqXWrVurWLFiio2N1V9//aVRo0bpgw8+MG8/sGfPHoWEhKh+/foKDQ3VrVu3zH/itguIe3BRsWLFVLp0af3vf/+z2L5gy5YtKlGihPmhAd9//70kqX79+hb9RUZGql69egoJCbHY/0pSgvteJUe5cuX0yiuvaOvWreZb5aKjo/Xdd9+pYsWK8R6W9DQe3y4hbq4ef3pt4cKF9eDBg3hz3r59e4un53p4eOjdd9/VlStXzLc+zZw5Uzt37rR4im5MTIz51sHQ0NAnxlmlSpUEy+3t7VWvXj3zz25ubnJzc5Ojo6Nq165tLs+WLZvc3Nx07do1SQ9ve/vhhx/0yiuv6OWXX7aYw1y5cqlMmTLas2eP7t+/bzFe48aNLX6O+ywktFL1WT1prMDAQF2+fFnvvPOOxQqGrFmzqlWrVhbHPs1nQJJKlCih/v3768SJE2revLn+/fdfjR071rxiNzGxsbG6ePGixYrjOB4eHjp37pxmzZqls2fPSpJcXFz03Xff6ZNPPknuy2IhbpwLFy6k6HgAQObDNX/muuaXHj7srWrVquY/devW1UcffSR3d3ctWrRIxYoVkyTzw62eZazHnT59WkePHlXhwoVVokQJc3nDhg0l6YmritPTDz/8IOnhHD96l56Dg4P69u0rSfHm/VG2trbatWuXli1bZvH+DgsLM/eXnOt2AGxrACQoJiZG9+7dU548eRJt07dvXwUGBmrFihVasWKF3NzcVKVKFdWpU0f169dP1pMsX3rppWTHFHcR8ajChQtLephoKFOmTLL7Sq7//vsv0bGdnZ1VsGBBc5s4uXPnjtc27tadpC58khrLZDLp1Vdf1c6dOxUSEvJUr9vjMderV8+cnLt+/bq+//57zZs3T7t379bq1avVvn17nTt3TtLDW9CmT5+eYF+P3g7WokULjR49Wr/++qvq1q2r33//XdeuXVP37t3NbeL6TOops4/fYvb4xfHTaN68uaZMmaLffvtNtWrV0q+//qqQkBA1b948xX1K8ec37kLs8TmJ2zbh8b1tPT094/UZl8D777//5OPjIxsbG925c0eLFy/WyZMndfnyZV28eNGcaE7OfrmJvUeyZcsW71YyW1tb5cqVK94vZjY2NjIMQ9LDLQpu376t27dvq2rVqomOGxQUZPGLxuNxxI39tHv+JseTxor7jMX9u/Goxz93T/sZkB5uVbFjxw798ccfqlu3brKeBnz79m3FxsbK1dU1Xt3IkSPVq1cvzZ49W7Nnz5a7u7veeOMN1atXTzVr1kzyF+nExD1M5NatW099LAAg8+Ga/6HMds0/ZcoU87Emk0lOTk4qUKBAvJjjFgKk5pfmcQ8Ce/3113Xp0iVz+WuvvSY7OzsdOnRIZ86cSfD801tSc/Hqq69KksU5JMTBwUGHDx/WDz/8oHPnzuny5cu6evWq+Ro67r8AkkZyFkhAYGCgoqOjLfbrfNxLL72ktWvX6u+//9avv/6qffv2afv27dq6davKlCmjFStWJLhP6KOeZt/PhNrG/c/uSReFKf02+En/M33w4EG8RFdKEibJEXcOT7tH05kzZ7R+/XpVr1493krU3Llzq2PHjvL29la7du20d+9etW/f3nzecQ++SkiWLFnMf3/77bf12WefafPmzapbt642bdokR0dH8zfk0sMEmbOzs+bOnZtorEWKFLH4ObkrLBLSpEkTzZgxQ5s3b1atWrW0adMmZcmS5an3KntcYu+15M57QvMXN7dxfX/99dcaN26cXnrpJVWuXFlly5aVl5eX3N3dzQ/ueJLEXruUxh+X4Cxbtqz69euXaDsPDw+Ln1Oyt29KJXesxx/qIcVPFj/tZ0B6uG/bv//+K+nhU5CvXbtmsfo5IXGve0LJ6rjV34cPH9auXbvMe9lt2LBB9erVs3gQRXLFjZOe8wIAyLi45rfsP6l+M/o1/6PKlSunAgUKPLFdrly59Morr+jYsWOKiIiw2FP3UTExMWrfvr1ee+01jRo1KtFzj46O1rfffitJWrVqlVatWpVgu9WrV2vUqFHJPJu0k9S8x10zPWkeBg0apO+++07FihVTmTJl5Ovrq+LFi+vcuXMaN25cqsYLZGYkZ4EExP1PNbFElmEYOn36tCIiIuTj46NSpUqpT58+Cg0N1ZAhQ/Tzzz9r9+7dqlWrVqrF9Pi31ZLMiZC4b9PjElJxt9fHSem3wYUKFZL0MMH5uPDwcF2+fDnB25FTIu5W+4TGMgxD//77r1xdXS1u80qOe/fuafHixbp8+XKi2wTEbT0Q99CouIs5R0fHeMeEhoZq9+7dFt+8u7q6qn79+tq6dauuX7+un376SXXq1FH27NnNbQoUKKBz587p1VdfjfetfWBgoIKDg83jp4aXXnpJNWrU0K+//qpbt25p165datSoUaqOkRLnzp2L95rGvY+LFCmiyMhITZ48WQULFtTGjRstVlQePnw4XWN9lJubm1xcXHT79u0E30d79uyRjY1Nkg+TsLa4fyfiXu9HPX6b/9N+BgzD0LBhw3Tv3j2NGjVKn332mYYNG6YlS5Yk+ctbzpw5ZW9vr5CQEIvymJgYnTp1SnZ2dqpYsaIqVqwoSbp586Z69Oihbdu26dSpUwmuxE5K3IrZhFb7AABePFzzP5QZrvlT6p133tGsWbO0du1atW/fPsE2O3bs0OHDh+Xs7Jzkdc3OnTt18+ZNlSxZUj179oxXf+XKFU2YMEGbN2/WoEGDrH5d/ui8V6pUyaLu9OnTkqR8+fIlevyhQ4f03Xff6a233tKMGTMsXpsjR46kfsBAJsbSEeAxBw4c0Ndff61ixYoleqFmMpnUp08f9ezZU/fu3TOXu7q6mpMFcRdNid3e/bTWrVtncQF26dIlbd68WYULF9Zrr70mSeZbsuL27oyzadOmeP0lJ6433nhDrq6uWrFihW7evGlRN2/ePEVGRj7zSsw43t7eKliwoLZs2WLeXzLO2rVrdfnyZYu9QpOrTJky8vT01Pbt2/Xdd98l2GbFihWSHj5JVZKqVaumLFmyaPny5fFuf/7yyy/14Ycf6tdff7Uob9GihaKiojRu3DiFh4fH2z4g7nX64osvLMpDQ0PVv39/9e7dO8EVjU+S1Dy2aNFCYWFh+vzzzxUZGalmzZo9df+p7auvvlJERIT550uXLunbb7+Vp6envLy8FBERobCwMOXLl88iMRsTE6PFixdLSt19wZLL1tZWderU0blz57R582aLuhMnTqh79+6aMGFCsm5tTKhvKW22OnhU8eLFVaRIEW3ZssXiFrXw8HCtXLnSou3TfgYCAgK0d+9ede3aVe3atVPHjh21d+9eBQQEPDGu/Pnz68qVKxZlMTEx8vPz0+DBgxUdHW0uz5Url/kXiZSsLI/biiE5q2kAAJkb1/z/JzNc86dU586d5eHhIX9/f+3duzde/dmzZzVmzBjZ2dnpww8/TLKvuC0NOnXqpDp16sT70759e5UoUUJ3795N9PeS9BQ3p3PmzDFvHyY9XAEcd7ffo/Nua2tr8T66ffu2pIdbIDyamL1165a++eYbSbLoF0DiWDmLF9aRI0fMF1OGYejOnTs6cuSItm3bJjc3N82aNSvJREvv3r01dOhQtWrVSs2aNVP27NkVGBiotWvXytvb27zaLG6/ox07dihfvnyqW7duiuK9ceOGWrdurebNmyskJEQrV65UbGysxo8fb77oqlevniZMmKAvv/xSYWFhKlKkiA4cOKDffvvN4gFAyY0ra9asGjNmjIYNG6bGjRurVatWypUrl/bs2aMdO3aoRIkS6tq1a4rO53G2trb69NNP1a1bN73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",
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sii_counts = train['sii'].value_counts().reset_index()\n",
+    "sii_counts.columns = ['sii', 'count']\n",
+    "total = sii_counts['count'].sum()\n",
+    "sii_counts['percentage'] = (sii_counts['count'] / total) * 100\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "\n",
+    "# SII\n",
+    "sns.barplot(x='sii', y='count', data=sii_counts, palette='Blues', ax=axes[0])\n",
+    "axes[0].set_title('Distribution of Severity Impairment Index (sii)', fontsize=14)\n",
+    "for p in axes[0].patches:\n",
+    "    height = p.get_height()\n",
+    "    percentage = sii_counts.loc[sii_counts['count'] == height, 'percentage'].values[0]\n",
+    "    axes[0].text(\n",
+    "        p.get_x() + p.get_width() / 2,\n",
+    "        height + 5, f'{int(height)} ({percentage:.1f}%)',\n",
+    "        ha=\"center\", fontsize=12\n",
+    "    )\n",
+    "\n",
+    "# PCIAT_Total for complete responses\n",
+    "sns.histplot(train['complete_resp_total'].dropna(), bins=20, ax=axes[1])\n",
+    "axes[1].set_title('Distribution of PCIAT_Total', fontsize=14)\n",
+    "axes[1].set_xlabel('PCIAT_Total for Complete PCIAT Responses')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Như vậy, qua phân tích, có 1241 trường hợp Missing (tức là số mẫu mà người được phỏng vấn không trả lời hết tất cả các câu dẫn đến không thể tính chính xác được điểm sii)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Phân tích và trực quan hóa các thông tin về chỉ số sii theo độ tuổi và giới tính"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# kiểm tra cột age và sex xem có giá trị nào thiếu không\n",
+    "assert train['Basic_Demos-Age'].isna().sum() == 0\n",
+    "assert train['Basic_Demos-Sex'].isna().sum() == 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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>count (%)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Children (5-12)</th>\n",
+       "      <td>2919 (73.71%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Adolescents (13-18)</th>\n",
+       "      <td>953 (24.07%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Adults (19-22)</th>\n",
+       "      <td>88 (2.22%)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         count (%)\n",
+       "Children (5-12)      2919 (73.71%)\n",
+       "Adolescents (13-18)   953 (24.07%)\n",
+       "Adults (19-22)          88 (2.22%)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Tỉ lệ phân bố theo từng nhóm tuổi\n",
+    "train['Age Group'] = pd.cut(\n",
+    "    train['Basic_Demos-Age'],\n",
+    "    bins=[4, 12, 18, 22],\n",
+    "    labels=['Children (5-12)', 'Adolescents (13-18)', 'Adults (19-22)']\n",
+    ")\n",
+    "calculate_stats(train, 'Age Group')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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>count (%)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Male</th>\n",
+       "      <td>2484 (62.73%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Female</th>\n",
+       "      <td>1476 (37.27%)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            count (%)\n",
+       "Male    2484 (62.73%)\n",
+       "Female  1476 (37.27%)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Tỉ lệ phân bố theo giới tính\n",
+    "sex_map = {0: 'Male', 1: 'Female'}\n",
+    "train['Basic_Demos-Sex'] = train['Basic_Demos-Sex'].map(sex_map)\n",
+    "calculate_stats(train, 'Basic_Demos-Sex')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Trực quan hóa nhóm tuổi, độ tuổi, giới tính theo điểm sii"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
+    "\n",
+    "# Đồ thị con 1: sii theo độ tuổi\n",
+    "sns.boxplot(y=train['Basic_Demos-Age'], x=train['sii'], ax=axes[0], palette='Blues')\n",
+    "axes[0].set_title('Điểm sii theo từng độ tuổi')\n",
+    "axes[0].set_ylabel('Tuổi')\n",
+    "axes[0].set_xlabel('Điểm sii')\n",
+    "\n",
+    "# Đồ thị con 2: sii theo nhóm độ tuổi\n",
+    "sns.boxplot(x=train['Age Group'], y=train['complete_resp_total'], ax=axes[1], palette='Blues')\n",
+    "axes[1].set_title('Điểm PCIAT_Total theo nhóm độ tuổi')\n",
+    "axes[1].set_xlabel('Nhóm độ tuổi')\n",
+    "axes[1].set_ylabel('Điểm PCIAT_Total')\n",
+    "\n",
+    "# Đồ thị con 3: sii theo giới tính\n",
+    "sns.histplot(data=train, x='complete_resp_total', hue='Basic_Demos-Sex',multiple='stack', bins=20, palette='Blues', ax=axes[2])\n",
+    "axes[2].set_title('Phân phối điểm PCIAT theo giới tính')\n",
+    "axes[2].set_xlabel('Điểm PCIAT')\n",
+    "axes[2].set_ylabel('Số lượng')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Phân tích biểu đồ hộp (Box Plots):\n",
+    "\n",
+    " - Mối quan hệ giữa SII và độ tuổi:\n",
+    "\n",
+    "Các điểm số SII cao hơn (tức là các mức độ nghiêm trọng như moderate và severe) thường liên quan đến các nhóm tuổi lớn hơn (ví dụ, nhóm adolescents và adults).\n",
+    "Có nghĩa là nhóm tuổi cao hơn có xu hướng bị ảnh hưởng nhiều hơn bởi vấn đề liên quan đến Internet, được thể hiện qua các điểm số SII cao hơn. Tuy nhiên, có một sự chồng chéo đáng kể giữa các nhóm độ tuổi trong mỗi mức điểm SII, tức là có những trường hợp trong các nhóm tuổi trẻ hơn vẫn có điểm SII cao.\n",
+    "\n",
+    " - Mối quan hệ giữa PCIAT_Total và độ tuổi:\n",
+    "\n",
+    "Median PCIAT_Total (tổng PCIAT cho các phản hồi đầy đủ) cao hơn ở nhóm adolescents (thanh thiếu niên), chỉ ra rằng có một mối quan hệ hình chữ U giữa tuổi tác và sự suy giảm do vấn đề sử dụng Internet (PIU - Problematic Internet Use).\n",
+    "Do các thanh thiếu niên có xu hướng có điểm PCIAT_Total cao hơn, trong khi các nhóm tuổi khác có thể có điểm thấp hơn. Điều này gợi ý rằng vấn đề sử dụng Internet có thể đạt đỉnh trong độ tuổi thanh thiếu niên, sau đó giảm dần khi người ta lớn lên.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " - Sự khác biệt giữa nam và nữ:\n",
+    "\n",
+    "Các sự khác biệt giữa nam và nữ là tương đối rõ ràng. Chứng tỏ có sử ảnh hưởng của Nam và Nữ đối với mức độ ảnh hưởng sii.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " - Phân phối sii theo từng độ tuổi:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# tính toán phân phối điểm PCIAT theo nhóm độ tuổi và sii\n",
+    "stats = train.groupby(['Age Group', 'sii']).size().unstack(fill_value=0)  \n",
+    "\n",
+    "# Tạo một hàng các biểu đồ con với số cột là sô số nhóm của stats (3 nhóm là children, adolescents, adults)\n",
+    "fig, axes = plt.subplots(1, len(stats), figsize=(18, 5))\n",
+    "\n",
+    "\n",
+    "# Vẽ biểu đồ pie cho mỗi nhóm tuổi\n",
+    "for i, age_group in enumerate(stats.index):\n",
+    "    group_counts = stats.loc[age_group] / stats.loc[age_group].sum() \n",
+    "    axes[i].pie(\n",
+    "        group_counts, labels=group_counts.index, autopct='%1.1f%%', \n",
+    "        startangle=140, colors=sns.color_palette('Blues'),\n",
+    "        labeldistance=1.05, pctdistance=0.7)\n",
+    "    axes[i].set_title(f'Phân phối điểm sii theo nhóm tuổi {age_group}')\n",
+    "    axes[i].axis('equal')\n",
+    "    \n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Phân tích biểu đồ Tròn (Pie Charts):\n",
+    "\n",
+    " - Phân phối của SII theo độ tuổi: Phân phối SII ở nhóm trẻ em và người lớn chủ yếu nghiêng về các giá trị thấp hơn (None và Mild), trong khi nhóm thanh thiếu niên có phân phối khá cân bằng giữa các giá trị \"None\", \"Mild\" và \"Moderate\".\n",
+    "Do nhóm trẻ em và người lớn có xu hướng không gặp phải vấn đề nghiêm trọng với sử dụng Internet, trong khi thanh thiếu niên có sự phân bổ rộng hơn, cho thấy một số vấn đề đang bắt đầu phát sinh ở nhóm này.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Phân bố tỉ lệ các nhóm tuổi theo từng mức sii"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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>sii</th>\n",
+       "      <th>Missing</th>\n",
+       "      <th>0 (None)</th>\n",
+       "      <th>1 (Mild)</th>\n",
+       "      <th>2 (Moderate)</th>\n",
+       "      <th>3 (Severe)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Age Group</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Children (5-12)</th>\n",
+       "      <td>858 (29.4%)</td>\n",
+       "      <td>1359 (46.6%)</td>\n",
+       "      <td>493 (16.9%)</td>\n",
+       "      <td>203 (7.0%)</td>\n",
+       "      <td>6 (0.2%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Adolescents (13-18)</th>\n",
+       "      <td>330 (34.6%)</td>\n",
+       "      <td>211 (22.1%)</td>\n",
+       "      <td>217 (22.8%)</td>\n",
+       "      <td>169 (17.7%)</td>\n",
+       "      <td>26 (2.7%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Adults (19-22)</th>\n",
+       "      <td>53 (60.2%)</td>\n",
+       "      <td>16 (18.2%)</td>\n",
+       "      <td>12 (13.6%)</td>\n",
+       "      <td>5 (5.7%)</td>\n",
+       "      <td>2 (2.3%)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "sii                      Missing      0 (None)     1 (Mild) 2 (Moderate)  \\\n",
+       "Age Group                                                                  \n",
+       "Children (5-12)      858 (29.4%)  1359 (46.6%)  493 (16.9%)   203 (7.0%)   \n",
+       "Adolescents (13-18)  330 (34.6%)   211 (22.1%)  217 (22.8%)  169 (17.7%)   \n",
+       "Adults (19-22)        53 (60.2%)    16 (18.2%)   12 (13.6%)     5 (5.7%)   \n",
+       "\n",
+       "sii                 3 (Severe)  \n",
+       "Age Group                       \n",
+       "Children (5-12)       6 (0.2%)  \n",
+       "Adolescents (13-18)  26 (2.7%)  \n",
+       "Adults (19-22)        2 (2.3%)  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_group_sii_distribution = train.groupby(['Age Group', 'sii']).size().unstack().apply(lambda x: x / x.sum() * 100, axis=1).applymap(lambda x: f'{x:.1f}%')\n",
+    "age_group_sii_distribution_counts = train.groupby(['Age Group', 'sii']).size().unstack(fill_value=0)\n",
+    "age_group_sii_distribution_combined = age_group_sii_distribution_counts.astype(str) + ' (' + age_group_sii_distribution + ')'\n",
+    "age_group_sii_distribution_combined\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nhận xét: \n",
+    "- Nhóm tuổi trẻ em chủ yếu có mức độ nghiện thấp, với phần lớn rơi vào mức độ 0 (None) và 1 (Mild)\n",
+    "- Thanh thiếu niên có sự phân bố đa dạng hơn, với một tỷ lệ đáng kể ở mức độ 2 (Moderate)\n",
+    "- Người lớn có tỷ lệ Missing cao"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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>sii</th>\n",
+       "      <th>Missing</th>\n",
+       "      <th>0 (None)</th>\n",
+       "      <th>1 (Mild)</th>\n",
+       "      <th>2 (Moderate)</th>\n",
+       "      <th>3 (Severe)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Age Group</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Children (5-12)</th>\n",
+       "      <td>0 (0.0%)</td>\n",
+       "      <td>1359 (65.9%)</td>\n",
+       "      <td>493 (23.9%)</td>\n",
+       "      <td>203 (9.8%)</td>\n",
+       "      <td>6 (0.3%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Adolescents (13-18)</th>\n",
+       "      <td>0 (0.0%)</td>\n",
+       "      <td>211 (33.9%)</td>\n",
+       "      <td>217 (34.8%)</td>\n",
+       "      <td>169 (27.1%)</td>\n",
+       "      <td>26 (4.2%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Adults (19-22)</th>\n",
+       "      <td>0 (0.0%)</td>\n",
+       "      <td>16 (45.7%)</td>\n",
+       "      <td>12 (34.3%)</td>\n",
+       "      <td>5 (14.3%)</td>\n",
+       "      <td>2 (5.7%)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "sii                   Missing      0 (None)     1 (Mild) 2 (Moderate)  \\\n",
+       "Age Group                                                               \n",
+       "Children (5-12)      0 (0.0%)  1359 (65.9%)  493 (23.9%)   203 (9.8%)   \n",
+       "Adolescents (13-18)  0 (0.0%)   211 (33.9%)  217 (34.8%)  169 (27.1%)   \n",
+       "Adults (19-22)       0 (0.0%)    16 (45.7%)   12 (34.3%)    5 (14.3%)   \n",
+       "\n",
+       "sii                 3 (Severe)  \n",
+       "Age Group                       \n",
+       "Children (5-12)       6 (0.3%)  \n",
+       "Adolescents (13-18)  26 (4.2%)  \n",
+       "Adults (19-22)        2 (5.7%)  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats = train[train['sii'] != 'Missing'].groupby(\n",
+    "    ['Age Group', 'sii']\n",
+    ").size().unstack(fill_value=0)\n",
+    "stats_prop = stats.div(stats.sum(axis=1), axis=0) * 100\n",
+    "\n",
+    "stats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)'\n",
+    "stats"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vấn đề với kích thước mẫu không đồng đều:\n",
+    "\n",
+    " - Số lượng thanh thiếu niên ít hơn so với trẻ em, và số lượng người lớn thì rất ít (chỉ có 88 người, và trong đó chỉ 36 người có SII). Do mẫu dữ liệu của bạn không đồng đều, với số lượng mẫu thanh thiếu niên và người lớn ít hơn nhiều so với trẻ em. Điều này có thể dẫn đến những sai lệch trong phân tích, bởi vì các nhóm thiếu đại diện có thể không phản ánh chính xác tình trạng của toàn bộ quần thể.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vấn đề phân bố không đều của SII:\n",
+    "\n",
+    " - Phân phối tổng thể của SII bị méo mó và có xu hướng nghiêng về các giá trị thấp. Các trường hợp nghiêm trọng rất hiếm, điều này có thể tạo ra các mối quan hệ mà chúng ta không thể thấy được do số lượng mẫu không đồng đều và sự thiếu đại diện của các trường hợp nghiêm trọng. Nếu dữ liệu bị lệch về một phía (ví dụ, quá nhiều trường hợp nhẹ và rất ít trường hợp nghiêm trọng), điều này có thể làm cho các mô hình học máy hoặc phân tích thống kê không chính xác, vì chúng không đủ mẫu để đại diện cho toàn bộ phân phối. Các mối quan hệ tiềm ẩn có thể bị che khuất hoặc không thể nhìn thấy do sự thiếu sót của các trường hợp cực đoan.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# III. Ảnh hưởng của mùa đến điểm sii"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "season_pie = train['Basic_Demos-Enroll_Season'].value_counts()\n",
+    "plt.pie(season_pie.values, labels=season_pie.index, autopct='%1.1f%%', colors=sns.color_palette('Blues'))\n",
+    "plt.title('Season of Enrollment')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " - Nhận xét: Các cột mùa có dữ liệu không liên quan và có tỉ lệ phân bố đồng đều. Vậy dữ liệu mùa không ảnh hưởng đến kết quả sii. Như vậy có thể đưa ra phương án loại bỏ các cột có 'Season' ra khỏi dữ liệu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Nhận dạng một số đặc trưng có tác động qua lại "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Các cột mới được tạo ra nhằm mục đích kết hợp các đặc trưng sẵn có để tạo ra các biến số phức tạp hơn, giúp máy có khả năng nắm bắt được nhiều mối quan hệ giữa các yếu tố trong dữ liệu. Từ đó giúp máy học được dữ liệu một các hiệu quả hơn\n",
+    "1. BMI_Age: Chỉ số khối cơ thể (BMI) nhân với tuổi.\n",
+    "data['BMI_Age'] = data['Physical-BMI'] * data['Basic_Demos-Age']\n",
+    " -> Đo lường ảnh hưởng của độ tuổi đến BMI, vì BMI có thể thay đổi theo tuổi.\n",
+    "2. Internet_Hours_Age: Số giờ sử dụng máy tính/internet mỗi ngày nhân với tuổi.\n",
+    "data['Internet_Hours_Age'] = data['PreInt_EduHx-computerinternet_hoursday'] * data['Basic_Demos-Age']\n",
+    "-> Đo lường mức độ ảnh hưởng của độ tuổi đến thời gian sử dụng internet.\n",
+    "3. BMI_Internet_Hours: Kết hợp giữa BMI và số giờ sử dụng internet mỗi ngày.\n",
+    "data['BMI_Internet_Hours'] = data['Physical-BMI'] * data['PreInt_EduHx-computerinternet_hoursday']\n",
+    " -> Phân tích tác động của thời gian ngồi trước máy tính lên sức khỏe qua chỉ số BMI.\n",
+    "4. BFP_BMI: Tỷ lệ phần trăm mỡ cơ thể (Body Fat Percentage - BFP) chia cho chỉ số BMI.\n",
+    "data['BFP_BMI'] = data['BIA-BIA_Fat'] / data['BIA-BIA_BMI']\n",
+    " -> Đánh giá sự tương quan giữa tỷ lệ mỡ cơ thể và BMI.\n",
+    "5. FFMI_BFP: Tỷ lệ giữa FFMI (Fat-Free Mass Index - Chỉ số khối lượng cơ thể không mỡ) và BFP.\n",
+    "data['FFMI_BFP'] = data['BIA-BIA_FFMI'] / data['BIA-BIA_Fat']\n",
+    " -> Đánh giá tỷ lệ cơ bắp và mỡ trong cơ thể.\n",
+    "6. FMI_BFP: Tỷ lệ giữa FMI (Fat Mass Index - Chỉ số khối lượng mỡ) và BFP.\n",
+    "data['FMI_BFP'] = data['BIA-BIA_FMI'] / data['BIA-BIA_Fat']\n",
+    " -> Đo lường mối quan hệ giữa tổng lượng mỡ và phần trăm mỡ cơ thể.\n",
+    "7. LST_TBW: Tỷ lệ giữa khối lượng cơ thể không mỡ (Lean Soft Tissue - LST) và tổng lượng nước trong cơ thể (Total Body Water - TBW).\n",
+    "data['LST_TBW'] = data['BIA-BIA_LST'] / data['BIA-BIA_TBW']\n",
+    " -> Phân tích vai trò của lượng nước cơ thể trong việc duy trì cơ bắp.\n",
+    "8. BFP_BMR: Tỷ lệ phần trăm mỡ cơ thể nhân với tốc độ trao đổi chất cơ bản (Basal Metabolic Rate - BMR).\n",
+    "data['BFP_BMR'] = data['BIA-BIA_Fat'] * data['BIA-BIA_BMR']\n",
+    " -> Đo lường ảnh hưởng của mỡ cơ thể đến tốc độ trao đổi chất.\n",
+    "9. BFP_DEE: Tỷ lệ phần trăm mỡ cơ thể nhân với năng lượng tiêu hao hàng ngày (Daily Energy Expenditure - DEE).\n",
+    "data['BFP_DEE'] = data['BIA-BIA_Fat'] * data['BIA-BIA_DEE']\n",
+    " -> Đánh giá mức năng lượng tiêu hao hàng ngày có liên quan đến lượng mỡ trong cơ thể.\n",
+    "10. BMR_Weight: Tỷ lệ giữa tốc độ trao đổi chất cơ bản và cân nặng.\n",
+    "data['BMR_Weight'] = data['BIA-BIA_BMR'] / data['Physical-Weight']\n",
+    " -> Phân tích mối quan hệ giữa cân nặng và BMR.\n",
+    "11. DEE_Weight: Tỷ lệ giữa năng lượng tiêu hao hàng ngày và cân nặng.\n",
+    "data['DEE_Weight'] = data['BIA-BIA_DEE'] / data['Physical-Weight']\n",
+    " -> Xem xét mức tiêu hao năng lượng so với cân nặng cơ thể.\n",
+    "12. SMM_Height: Tỷ lệ giữa khối lượng cơ xương (Skeletal Muscle Mass - SMM) và chiều cao.\n",
+    "data['SMM_Height'] = data['BIA-BIA_SMM'] / data['Physical-Height']\n",
+    " -> Phân tích ảnh hưởng của chiều cao ��ến khối lượng cơ.\n",
+    "13. Muscle_to_Fat: Tỷ lệ giữa khối lượng cơ xương và chỉ số khối lượng mỡ.\n",
+    "data['Muscle_to_Fat'] = data['BIA-BIA_SMM'] / data['BIA-BIA_FMI']\n",
+    " -> Xác định sự cân bằng giữa cơ bắp và mỡ trong cơ thể.\n",
+    "14. Hydration_Status: Tỷ lệ giữa tổng lượng nước cơ thể và cân nặng.\n",
+    "data['Hydration_Status'] = data['BIA-BIA_TBW'] / data['Physical-Weight']\n",
+    " -> Đánh giá tình trạng hydrat hóa của cơ thể.\n",
+    "15. ICW_TBW: Tỷ lệ giữa lượng nước trong tế bào (Intracellular Water - ICW) và tổng lượng nước cơ thể.\n",
+    "data['ICW_TBW'] = data['BIA-BIA_ICW'] / data['BIA-BIA_TBW']\n",
+    " -> Đo lường sự phân bố nước trong cơ thể, đánh giá sức khỏe tế bào."
+   ]
+  }
+ ],
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