{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# generate x feature encoder\n",
    "encX = LabelEncoder()\n",
    "encX.fit(['看電視', '讀書', '音樂', '游泳'])\n",
    "# generate y feature encoder\n",
    "ency = LabelEncoder()\n",
    "ency.fit(['是', '否'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(encX.classes_)  # '游泳' '看電視' '讀書' '音樂'分別會轉成0,1,2,3\n",
    "print(ency.classes_)  # 否、是分別會轉成0,1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_Xy = {'興趣': ['看電視', '讀書', '音樂', '看電視'], '成功與否': ['是', '否', '否', '是']}\n",
    "df = pd.DataFrame(data=data_Xy, index=['小明', '小林', '小英', '小陳'])\n",
    "df = df[['興趣', '成功與否']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_encode = df.copy()\n",
    "df_encode['興趣'] = encX.transform(df_encode['興趣'])\n",
    "df_encode['成功與否'] = ency.transform(df_encode['成功與否'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_encode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = np.array([1, 0, 0, 1])\n",
    "df['prediction'] = ency.inverse_transform(prediction)  # 將預測完的結果做反轉換\n",
    "df"
   ]
  }
 ],
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