D37 探索性資料分析(EDA)_遺失值與異常值的進階補值策略 作業.ipynb

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    "## 作業\n",
    "在鐵達尼資料集中，\n",
    "透過進階補值方法 KNN，將鐵達尼號中的 Titanic_train\n",
    ".csv 中的 age 的遺失值進行補值。    \n",
    "* step1: 觀察 Age 和 Pclass 與 Sex 是否有關連性?\n",
    "* step2: 如果有關連性，運用 KNN ，取出 Age 、 Pclass、 Sex的資料，以 Sex 與 Pclass 補 Age 遺失值。 "
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   "source": [
    "# import library\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import math\n",
    "import statistics\n",
    "import seaborn as sns\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 讀入資料"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 觀察 Age"
   ]
  }
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