example_04.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 빅데이터분석기사 실기 대비\n",
    "\n",
    "4회차 기출 문제 풀이: 자세한 설명 [링크](https://djccnt15.github.io/dataanalysis/bigdata_certi_05/)  \n",
    "\n",
    "문제 출처  \n",
    "\n",
    "- [4th-type1-python](https://www.kaggle.com/code/agileteam/4th-type1-python/notebook)\n",
    "- [4th-t2-python](https://www.kaggle.com/code/agileteam/4th-t2-python/notebook)\n",
    "\n",
    "데이터 출처  \n",
    "\n",
    "- [Big Data Analytics Certification KR 2022](https://www.kaggle.com/competitions/big-data-analytics-certification-kr-2022/data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 작업형 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 문제 1-1\n",
    "\n",
    "age 컬럼의 3사분위수와 1사분위수의 차를 절대값으로 구하고, 소수점 버려서, 정수로 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('data/4th/basic1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50\n"
     ]
    }
   ],
   "source": [
    "res = int(abs(df['age'].quantile(0.75) - df['age'].quantile(0.25)))\n",
    "\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 문제 1-2\n",
    "\n",
    "(loves 반응 + wows 반응) / (reactions 반응) 비율이 0.4보다 크고 0.5보다 작으면서, status_type이 'video'인 데이터의 개수  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('data/4th/fb.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90\n"
     ]
    }
   ],
   "source": [
    "ratio = (df['loves'] + df['wows']) / df['reactions']\n",
    "cond_1 = ratio > 0.4\n",
    "cond_2 = ratio < 0.5\n",
    "cond_3 = df['type'] == 'video'\n",
    "res = len(df.loc[cond_1 & cond_2 & cond_3])\n",
    "\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 문제 1-3\n",
    "\n",
    "date_added가 2018년 1월 이면서 country가 United Kingdom 단독 제작인 데이터의 개수  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('data/4th/nf.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date_added'] = pd.to_datetime(df['date_added'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "cond_1 = df['country'] == \"United Kingdom\"\n",
    "cond_2 = df['date_added'].dt.year == 2018\n",
    "cond_3 = df['date_added'].dt.month == 1\n",
    "res = len(df.loc[cond_1 & cond_2 & cond_3])\n",
    "\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 작업형 2\n",
    "\n",
    "기존 고객 분류 자료를 바탕으로 신규 고객이 어떤 분류에 속할지 예측  \n",
    "\n",
    "- 예측할 값(y): \"Segmentation\" (1,2,3,4)\n",
    "\n",
    "**제출 형식**\n",
    "\n",
    "```\n",
    "ID,Segmentation\n",
    "458989,1\n",
    "458994,2\n",
    "459000,3\n",
    "459003,4\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 읽기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('data/4th/train.csv')\n",
    "test = pd.read_csv('data/4th/test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6665 entries, 0 to 6664\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   ID               6665 non-null   int64  \n",
      " 1   Gender           6665 non-null   object \n",
      " 2   Ever_Married     6665 non-null   object \n",
      " 3   Age              6665 non-null   int64  \n",
      " 4   Graduated        6665 non-null   object \n",
      " 5   Profession       6665 non-null   object \n",
      " 6   Work_Experience  6665 non-null   float64\n",
      " 7   Spending_Score   6665 non-null   object \n",
      " 8   Family_Size      6665 non-null   float64\n",
      " 9   Var_1            6665 non-null   object \n",
      " 10  Segmentation     6665 non-null   int64  \n",
      "dtypes: float64(2), int64(3), object(6)\n",
      "memory usage: 572.9+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(train.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Gender</th>\n",
       "      <th>Ever_Married</th>\n",
       "      <th>Age</th>\n",
       "      <th>Graduated</th>\n",
       "      <th>Profession</th>\n",
       "      <th>Work_Experience</th>\n",
       "      <th>Spending_Score</th>\n",
       "      <th>Family_Size</th>\n",
       "      <th>Var_1</th>\n",
       "      <th>Segmentation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>462809</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>22</td>\n",
       "      <td>No</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Cat_4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>466315</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>67</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cat_6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>461735</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>67</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Cat_6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>461319</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>56</td>\n",
       "      <td>No</td>\n",
       "      <td>Artist</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Cat_6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>460156</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>32</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Cat_6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID  Gender Ever_Married  Age Graduated  Profession  Work_Experience  \\\n",
       "0  462809    Male           No   22        No  Healthcare              1.0   \n",
       "1  466315  Female          Yes   67       Yes    Engineer              1.0   \n",
       "2  461735    Male          Yes   67       Yes      Lawyer              0.0   \n",
       "3  461319    Male          Yes   56        No      Artist              0.0   \n",
       "4  460156    Male           No   32       Yes  Healthcare              1.0   \n",
       "\n",
       "  Spending_Score  Family_Size  Var_1  Segmentation  \n",
       "0            Low          4.0  Cat_4             4  \n",
       "1            Low          1.0  Cat_6             2  \n",
       "2           High          2.0  Cat_6             2  \n",
       "3        Average          2.0  Cat_6             3  \n",
       "4            Low          3.0  Cat_6             3  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2154 entries, 0 to 2153\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   ID               2154 non-null   int64  \n",
      " 1   Gender           2154 non-null   object \n",
      " 2   Ever_Married     2154 non-null   object \n",
      " 3   Age              2154 non-null   int64  \n",
      " 4   Graduated        2154 non-null   object \n",
      " 5   Profession       2154 non-null   object \n",
      " 6   Work_Experience  2154 non-null   float64\n",
      " 7   Spending_Score   2154 non-null   object \n",
      " 8   Family_Size      2154 non-null   float64\n",
      " 9   Var_1            2154 non-null   object \n",
      "dtypes: float64(2), int64(2), object(6)\n",
      "memory usage: 168.4+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(test.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 2 3 1]\n"
     ]
    }
   ],
   "source": [
    "print(train['Segmentation'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Gender</th>\n",
       "      <th>Ever_Married</th>\n",
       "      <th>Age</th>\n",
       "      <th>Graduated</th>\n",
       "      <th>Profession</th>\n",
       "      <th>Work_Experience</th>\n",
       "      <th>Spending_Score</th>\n",
       "      <th>Family_Size</th>\n",
       "      <th>Var_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>458989</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>36</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cat_6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>458994</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Cat_6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>459000</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>59</td>\n",
       "      <td>No</td>\n",
       "      <td>Executive</td>\n",
       "      <td>11.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Cat_6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>459003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>47</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Doctor</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Cat_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>459005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>61</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Doctor</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Cat_6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID  Gender Ever_Married  Age Graduated  Profession  Work_Experience  \\\n",
       "0  458989  Female          Yes   36       Yes    Engineer              0.0   \n",
       "1  458994    Male          Yes   37       Yes  Healthcare              8.0   \n",
       "2  459000    Male          Yes   59        No   Executive             11.0   \n",
       "3  459003    Male          Yes   47       Yes      Doctor              0.0   \n",
       "4  459005    Male          Yes   61       Yes      Doctor              5.0   \n",
       "\n",
       "  Spending_Score  Family_Size  Var_1  \n",
       "0            Low          1.0  Cat_6  \n",
       "1        Average          4.0  Cat_6  \n",
       "2           High          2.0  Cat_6  \n",
       "3           High          5.0  Cat_4  \n",
       "4            Low          3.0  Cat_6  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 테이블 병합 꼼수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([train, test])\n",
    "\n",
    "cols_obj = df.select_dtypes(include='object').columns\n",
    "cols_num = [col for col in df.columns if col not in cols_obj and col not in ['ID', 'Segmentation']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Gender', 'Ever_Married', 'Graduated', 'Profession', 'Spending_Score',\n",
      "       'Var_1'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(cols_obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Age', 'Work_Experience', 'Family_Size']\n"
     ]
    }
   ],
   "source": [
    "print(cols_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "id = test.iloc[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       458989\n",
      "1       458994\n",
      "2       459000\n",
      "3       459003\n",
      "4       459005\n",
      "         ...  \n",
      "2149    467950\n",
      "2150    467954\n",
      "2151    467958\n",
      "2152    467961\n",
      "2153    467968\n",
      "Name: ID, Length: 2154, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender=['Male' 'Female']\n",
      "Ever_Married=['No' 'Yes']\n",
      "Graduated=['No' 'Yes']\n",
      "Profession=['Healthcare' 'Engineer' 'Lawyer' 'Artist' 'Doctor' 'Homemaker'\n",
      " 'Entertainment' 'Marketing' 'Executive']\n",
      "Spending_Score=['Low' 'High' 'Average']\n",
      "Var_1=['Cat_4' 'Cat_6' 'Cat_7' 'Cat_3' 'Cat_1' 'Cat_2' 'Cat_5']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None, None]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[print(f'{col}={df[col].unique()}') for col in cols_obj]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Ever_Married', 'Graduated', 'Profession', 'Spending_Score',\n",
       "       'Var_1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_obj  # type: ignore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_binary = ['Gender', 'Ever_Married', 'Graduated']\n",
    "col_spend = ['Low', 'Average', 'High']\n",
    "col_var = ['Cat_1', 'Cat_2', 'Cat_3', 'Cat_4', 'Cat_5', 'Cat_6', 'Cat_7']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "for col in cols_binary:\n",
    "    df[col] = encoder.fit_transform(df[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder_spend = LabelEncoder()\n",
    "encoder_spend.fit(col_spend)\n",
    "df['Spending_Score'] = encoder_spend.transform(df['Spending_Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder_var = LabelEncoder()\n",
    "encoder_var.fit(col_var)\n",
    "df['Var_1'] = encoder_var.transform(df['Var_1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8819 entries, 0 to 2153\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   ID               8819 non-null   int64  \n",
      " 1   Gender           8819 non-null   int32  \n",
      " 2   Ever_Married     8819 non-null   int32  \n",
      " 3   Age              8819 non-null   int64  \n",
      " 4   Graduated        8819 non-null   int32  \n",
      " 5   Profession       8819 non-null   object \n",
      " 6   Work_Experience  8819 non-null   float64\n",
      " 7   Spending_Score   8819 non-null   int32  \n",
      " 8   Family_Size      8819 non-null   float64\n",
      " 9   Var_1            8819 non-null   int32  \n",
      " 10  Segmentation     6665 non-null   float64\n",
      "dtypes: float64(3), int32(5), int64(2), object(1)\n",
      "memory usage: 654.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Gender</th>\n",
       "      <th>Ever_Married</th>\n",
       "      <th>Age</th>\n",
       "      <th>Graduated</th>\n",
       "      <th>Profession</th>\n",
       "      <th>Work_Experience</th>\n",
       "      <th>Spending_Score</th>\n",
       "      <th>Family_Size</th>\n",
       "      <th>Var_1</th>\n",
       "      <th>Segmentation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>462809</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>466315</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>461735</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>461319</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>Artist</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>460156</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID  Gender  Ever_Married  Age  Graduated  Profession  Work_Experience  \\\n",
       "0  462809       1             0   22          0  Healthcare              1.0   \n",
       "1  466315       0             1   67          1    Engineer              1.0   \n",
       "2  461735       1             1   67          1      Lawyer              0.0   \n",
       "3  461319       1             1   56          0      Artist              0.0   \n",
       "4  460156       1             0   32          1  Healthcare              1.0   \n",
       "\n",
       "   Spending_Score  Family_Size  Var_1  Segmentation  \n",
       "0               2          4.0      3           4.0  \n",
       "1               2          1.0      5           2.0  \n",
       "2               1          2.0      5           2.0  \n",
       "3               0          2.0      5           3.0  \n",
       "4               2          3.0      5           3.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(data=df, columns=['Profession'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8819 entries, 0 to 2153\n",
      "Data columns (total 19 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   ID                        8819 non-null   int64  \n",
      " 1   Gender                    8819 non-null   int32  \n",
      " 2   Ever_Married              8819 non-null   int32  \n",
      " 3   Age                       8819 non-null   int64  \n",
      " 4   Graduated                 8819 non-null   int32  \n",
      " 5   Work_Experience           8819 non-null   float64\n",
      " 6   Spending_Score            8819 non-null   int32  \n",
      " 7   Family_Size               8819 non-null   float64\n",
      " 8   Var_1                     8819 non-null   int32  \n",
      " 9   Segmentation              6665 non-null   float64\n",
      " 10  Profession_Artist         8819 non-null   uint8  \n",
      " 11  Profession_Doctor         8819 non-null   uint8  \n",
      " 12  Profession_Engineer       8819 non-null   uint8  \n",
      " 13  Profession_Entertainment  8819 non-null   uint8  \n",
      " 14  Profession_Executive      8819 non-null   uint8  \n",
      " 15  Profession_Healthcare     8819 non-null   uint8  \n",
      " 16  Profession_Homemaker      8819 non-null   uint8  \n",
      " 17  Profession_Lawyer         8819 non-null   uint8  \n",
      " 18  Profession_Marketing      8819 non-null   uint8  \n",
      "dtypes: float64(3), int32(5), int64(2), uint8(9)\n",
      "memory usage: 663.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 테이블 분리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df.iloc[:train.shape[0]]\n",
    "test = df.iloc[train.shape[0]:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wizcore\\AppData\\Local\\Temp\\ipykernel_24208\\3233341843.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test.drop(columns=['Segmentation', 'ID'], inplace=True)\n"
     ]
    }
   ],
   "source": [
    "test.drop(columns=['Segmentation', 'ID'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 종속변수/독립변수 분리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "endog = np.array(train['Segmentation']).reshape(-1, 1)\n",
    "exog = train.drop(columns=['Segmentation', 'ID'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 모델 생성/학습/평가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    i  score_mean\n",
      "2  30    0.480870\n",
      "1  20    0.476669\n",
      "3  40    0.474119\n",
      "5  60    0.471118\n",
      "4  50    0.470818\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier as KNN\n",
    "\n",
    "endog = endog.flatten()\n",
    "\n",
    "score = [{'i': i, 'score_mean': cross_val_score(KNN(n_neighbors=i), X=exog, y=endog, cv=5).mean()} for i in range(10, 500, 10)]\n",
    "score = pd.DataFrame(score).sort_values(by='score_mean', ascending=False)\n",
    "\n",
    "print(score.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 2. 3. 4.]\n"
     ]
    }
   ],
   "source": [
    "model = KNN(n_neighbors=score.iloc[0, 0])\n",
    "model.fit(X=exog, y=endog)\n",
    "\n",
    "print(model.classes_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 예측"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 3. 3. ... 2. 3. 3.]\n"
     ]
    }
   ],
   "source": [
    "predict = model.predict(test)\n",
    "\n",
    "print(predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.36666667 0.3        0.13333333 0.2       ]\n",
      " [0.23333333 0.3        0.4        0.06666667]\n",
      " [0.1        0.36666667 0.53333333 0.        ]\n",
      " ...\n",
      " [0.26666667 0.43333333 0.06666667 0.23333333]\n",
      " [0.16666667 0.33333333 0.43333333 0.06666667]\n",
      " [0.26666667 0.2        0.3        0.23333333]]\n"
     ]
    }
   ],
   "source": [
    "predict_proba = model.predict_proba(test)\n",
    "\n",
    "print(predict_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2154 2154\n"
     ]
    }
   ],
   "source": [
    "print(len(id), len(predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       ID  Segmentation\n",
      "0  458989             1\n",
      "1  458994             3\n",
      "2  459000             3\n",
      "3  459003             2\n",
      "4  459005             2\n"
     ]
    }
   ],
   "source": [
    "result = pd.DataFrame({'ID': id, 'Segmentation': predict})\n",
    "result['Segmentation'] = result['Segmentation'].astype(int)\n",
    "\n",
    "print(result.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.to_csv('result.csv', index=False)"
   ]
  }
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