titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   yes\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   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adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult male   no\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female yes\\n1st  adult female no\\n1st  adult female no\\n1st  adult female no\\n1st  adult female no\\n1st  child male   yes\\n1st  child male   yes\\n1st  child male   yes\\n1st  child male   yes\\n1st  child male   yes\\n1st  child female yes\\n2nd  adult male   yes\\n2nd  adult male   yes\\n2nd  adult male   yes\\n2nd  adult male   yes\\n2nd  adult male   yes\\n2nd  adult male   yes\\n2nd  adult male   yes\\n2nd  adult male   yes\\n2nd  adult male   yes\\n2nd  adult male   yes\\n2nd  adult male   yes\\n2nd  adult male   yes\\n2nd  adult male   yes\\n2nd  adult male   yes\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   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adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult male   no\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female yes\\n2nd  adult female no\\n2nd  adult female no\\n2nd  adult female no\\n2nd  adult female no\\n2nd  adult female no\\n2nd  adult female no\\n2nd  adult female no\\n2nd  adult female no\\n2nd  adult female no\\n2nd  adult female no\\n2nd  adult female no\\n2nd  adult female no\\n2nd  adult female no\\n2nd  child male   yes\\n2nd  child male   yes\\n2nd  child male   yes\\n2nd  child male   yes\\n2nd  child male   yes\\n2nd  child male   yes\\n2nd  child male   yes\\n2nd  child male   yes\\n2nd  child male   yes\\n2nd  child male   yes\\n2nd  child male   yes\\n2nd  child female yes\\n2nd  child female yes\\n2nd  child female yes\\n2nd  child female yes\\n2nd  child female yes\\n2nd  child female yes\\n2nd  child female yes\\n2nd  child female yes\\n2nd  child female yes\\n2nd  child female yes\\n2nd  child female yes\\n2nd  child female yes\\n2nd  child female yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   yes\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult 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adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult male   no\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female yes\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  adult female no\\n3rd  child male   yes\\n3rd  child male   yes\\n3rd  child male   yes\\n3rd  child male   yes\\n3rd  child male   yes\\n3rd  child male   yes\\n3rd  child male   yes\\n3rd  child male   yes\\n3rd  child male   yes\\n3rd  child male   yes\\n3rd  child male   yes\\n3rd  child male   yes\\n3rd  child male   yes\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child male   no\\n3rd  child female yes\\n3rd  child female yes\\n3rd  child female yes\\n3rd  child female yes\\n3rd  child female yes\\n3rd  child female yes\\n3rd  child female yes\\n3rd  child female yes\\n3rd  child female yes\\n3rd  child female yes\\n3rd  child female yes\\n3rd  child female yes\\n3rd  child female yes\\n3rd  child female yes\\n3rd  child female no\\n3rd  child female no\\n3rd  child female no\\n3rd  child female no\\n3rd  child female no\\n3rd  child female no\\n3rd  child female no\\n3rd  child female no\\n3rd  child female no\\n3rd  child female no\\n3rd  child female no\\n3rd  child female no\\n3rd  child female no\\n3rd  child female no\\n3rd  child female no\\n3rd  child female no\\n3rd  child female no\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   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male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   yes\\ncrew adult male   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adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult male   no\\ncrew adult female yes\\ncrew adult female yes\\ncrew adult female yes\\ncrew adult female yes\\ncrew adult female yes\\ncrew adult female yes\\ncrew adult female yes\\ncrew adult female yes\\ncrew adult female yes\\ncrew adult female yes\\ncrew adult female yes\\ncrew adult female yes\\ncrew adult female yes\\ncrew adult female yes\\ncrew adult female yes\\ncrew adult female yes\\ncrew adult female yes\\ncrew adult female yes\\ncrew adult female yes\\ncrew adult female yes\\ncrew adult female no\\ncrew adult female no\\ncrew adult female no\\n'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = open(\"./Dataset.data\")\n",
    "data = f.read()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
       " '1st  adult male   yes',\n",
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       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " '3rd  adult male   no',\n",
       " ...]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.split(\"\\n\")\n",
    "data = data[:-1]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['1st', 'adult', 'male', 'yes']], dtype='<U5')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def data_split(data):\n",
    "    data = re.sub(\"\\s+\",\" \",data) #xóa các khoảng trắng dư thừa\n",
    "    data = data.split()           #tách chuỗi bằng các khoảng trắng\n",
    "    data = np.array([data])\n",
    "    return data\n",
    "data_split(data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['1st', 'adult', 'male', 'yes'],\n",
       "       ['1st', 'adult', 'male', 'yes'],\n",
       "       ['1st', 'adult', 'male', 'yes'],\n",
       "       ...,\n",
       "       ['crew', 'adult', 'female', 'no'],\n",
       "       ['crew', 'adult', 'female', 'no'],\n",
       "       ['crew', 'adult', 'female', 'no']], dtype='<U6')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_np = data_split(data[0])\n",
    "for i, d in enumerate(data):\n",
    "    data_np = np.concatenate((data_np,data_split(d)), axis = 0)\n",
    "data_np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2202, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_np.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_np[:, 0][np.where(data_np[:, 0] == '1st')] = 0\n",
    "data_np[:, 0][np.where(data_np[:, 0] == '2nd')] = 1\n",
    "data_np[:, 0][np.where(data_np[:, 0] == '3rd')] = 2\n",
    "data_np[:, 0][np.where(data_np[:, 0] == 'crew')] = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_np[:, 1][np.where(data_np[:, 1] == 'adult')] = 0\n",
    "data_np[:, 1][np.where(data_np[:, 1] == 'child')] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_np[:, 2][np.where(data_np[:, 2] == 'male')] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_np[:, 2][np.where(data_np[:, 2] == 'female')] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_np[:, 3][np.where(data_np[:, 3] == 'yes')] = 0\n",
    "data_np[:, 3][np.where(data_np[:, 3] == 'no')] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['0', '0', '0', '0'],\n",
       "       ['0', '0', '0', '0'],\n",
       "       ['0', '0', '0', '0'],\n",
       "       ...,\n",
       "       ['3', '0', '1', '1'],\n",
       "       ['3', '0', '1', '1'],\n",
       "       ['3', '0', '1', '1']], dtype='<U6')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0],\n",
       "       [0, 0, 0, 0],\n",
       "       [0, 0, 0, 0],\n",
       "       ...,\n",
       "       [3, 0, 1, 1],\n",
       "       [3, 0, 1, 1],\n",
       "       [3, 0, 1, 1]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_np = np.array(data_np,dtype=np.int)\n",
    "data_np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ntraining = int(0.8 * data_np.shape[0])\n",
    "np.random.shuffle(data_np)             # trộn bộ dữ liệu trước khi phân chia\n",
    "training_att = data_np[0:ntraining, 0:3]\n",
    "testing_att = data_np[ntraining:,0:3]\n",
    "training_label = data_np[0:ntraining,3]\n",
    "testing_label = data_np[ntraining:,3]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1761, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_att.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def predict(x,k):\n",
    "    \"\"\"\n",
    "    dự đoán nhãn mới của 1 điểm dữ liệu x bằng thuật toán k-NN\n",
    "    \"\"\"\n",
    "    \n",
    "    distance = (x-training_att)\n",
    "    distance = np.linalg.norm(distance,axis = 1) # tính toán khoảng cách từ điểm cần \n",
    "                                                # dự đoán đến toàn bộ dữ liệu trong tập train     \n",
    "                                   \n",
    "    indices = np.argsort(distance)             \n",
    "    nearest = indices[0:k]         # lấy chỉ số của những điểm dữ liệu gần với x nhất  \n",
    "    \n",
    "    # đếm những điểm dữ liệu mà nhãn là 0 trong tập nearest\n",
    "    dem0 = 0\n",
    "    for i in nearest:\n",
    "        if training_label[i] == 0:\n",
    "            dem0 += 1\n",
    "    dem1 = k - dem0\n",
    "    \n",
    "    # quyết định nhãn mới của điểm dữ liệu cần dự đoán\n",
    "    if dem0 > dem1:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "predict(testing_att[6],k=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0,\n",
       "       1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1,\n",
       "       1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
       "       1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1,\n",
       "       1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1,\n",
       "       0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1,\n",
       "       1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
       "       1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n",
       "       0])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 0, 0],\n",
       "       [2, 0, 0],\n",
       "       [3, 0, 0],\n",
       "       ...,\n",
       "       [2, 0, 1],\n",
       "       [2, 0, 0],\n",
       "       [3, 0, 0]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_att"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.04535147392289"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# thực hiện dự đoán trên tập testing và đếm số lượng điểm dự đoán chính xác\n",
    "def testAll(k):\n",
    "    dem =0 \n",
    "    for i, t in enumerate(testing_att):\n",
    "        if predict(t,k)==testing_label[i]:\n",
    "            dem += 1\n",
    "\n",
    "    # in ra tỷ lệ điểm dự đoán chính xác\n",
    "    return dem / testing_label.shape[0] * 100 \n",
    "\n",
    "testAll(k = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'percent')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "list_acc = []\n",
    "for k in range(1,101):\n",
    "    list_acc.append(testAll(k))\n",
    "    \n",
    "plt.plot(list_acc)\n",
    "plt.xlabel('k',fontsize=18)\n",
    "plt.ylabel('percent',fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.04535147392289"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testAll(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'percent')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sử dụng thư viện KNeighborsClassifier từ sklearn.neighbors \n",
    "list_acc = []\n",
    "for k in range(1,101):\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(training_att,training_label)\n",
    "    list_acc.append(knn.score(testing_att,testing_label)*100)\n",
    "plt.plot(list_acc)\n",
    "plt.xlabel('k',fontsize=16)\n",
    "plt.ylabel('percent',fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.32426303854876"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_acc[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7845804988662132"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Naive bayes với sklearn\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn import metrics as sk_metrics\n",
    "\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(training_att,training_label)\n",
    "gnb_predictions = gnb.predict(testing_att)\n",
    "sk_metrics.accuracy_score(testing_label, gnb_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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