11.KNN(k-Nearest Neighbors).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "#데이터 시각화 패키지\n",
    "import seaborn as sns\n",
    "\n",
    "#train, test 데이터 분할 함수\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#sklearn.neighbors의 KNN:KneifhborsRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "#ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "matplotlib.rc('font',family = 'NanumGothicCoding')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FAT</th>\n",
       "      <th>AGE</th>\n",
       "      <th>WEIGHT</th>\n",
       "      <th>HEIGHT</th>\n",
       "      <th>NECK</th>\n",
       "      <th>CHEST</th>\n",
       "      <th>ABDOMEN</th>\n",
       "      <th>HIP</th>\n",
       "      <th>THIGH</th>\n",
       "      <th>KNEE</th>\n",
       "      <th>ANKLE</th>\n",
       "      <th>BICEPS</th>\n",
       "      <th>FOREARM</th>\n",
       "      <th>WRIST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.2</td>\n",
       "      <td>46</td>\n",
       "      <td>363.15</td>\n",
       "      <td>72.25</td>\n",
       "      <td>51.2</td>\n",
       "      <td>136.2</td>\n",
       "      <td>148.1</td>\n",
       "      <td>147.7</td>\n",
       "      <td>87.3</td>\n",
       "      <td>49.1</td>\n",
       "      <td>29.6</td>\n",
       "      <td>45.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.8</td>\n",
       "      <td>27</td>\n",
       "      <td>168.00</td>\n",
       "      <td>71.25</td>\n",
       "      <td>38.1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>79.1</td>\n",
       "      <td>94.5</td>\n",
       "      <td>57.3</td>\n",
       "      <td>36.2</td>\n",
       "      <td>24.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.2</td>\n",
       "      <td>69</td>\n",
       "      <td>177.75</td>\n",
       "      <td>68.50</td>\n",
       "      <td>38.7</td>\n",
       "      <td>102.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>98.3</td>\n",
       "      <td>55.0</td>\n",
       "      <td>38.3</td>\n",
       "      <td>21.8</td>\n",
       "      <td>30.8</td>\n",
       "      <td>25.7</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.6</td>\n",
       "      <td>57</td>\n",
       "      <td>147.75</td>\n",
       "      <td>65.75</td>\n",
       "      <td>35.2</td>\n",
       "      <td>99.6</td>\n",
       "      <td>86.4</td>\n",
       "      <td>90.1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>21.3</td>\n",
       "      <td>31.7</td>\n",
       "      <td>27.3</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47.5</td>\n",
       "      <td>51</td>\n",
       "      <td>219.00</td>\n",
       "      <td>64.00</td>\n",
       "      <td>41.2</td>\n",
       "      <td>119.8</td>\n",
       "      <td>122.1</td>\n",
       "      <td>112.8</td>\n",
       "      <td>62.5</td>\n",
       "      <td>36.9</td>\n",
       "      <td>23.6</td>\n",
       "      <td>34.7</td>\n",
       "      <td>29.1</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    FAT  AGE  WEIGHT  HEIGHT  NECK  CHEST  ABDOMEN    HIP  THIGH  KNEE  ANKLE  \\\n",
       "0  35.2   46  363.15   72.25  51.2  136.2    148.1  147.7   87.3  49.1   29.6   \n",
       "1  11.8   27  168.00   71.25  38.1   93.0     79.1   94.5   57.3  36.2   24.5   \n",
       "2  22.2   69  177.75   68.50  38.7  102.0     95.0   98.3   55.0  38.3   21.8   \n",
       "3  10.6   57  147.75   65.75  35.2   99.6     86.4   90.1   53.0  35.0   21.3   \n",
       "4  47.5   51  219.00   64.00  41.2  119.8    122.1  112.8   62.5  36.9   23.6   \n",
       "\n",
       "   BICEPS  FOREARM  WRIST  \n",
       "0    45.0     29.0   21.4  \n",
       "1    29.0     30.0   18.8  \n",
       "2    30.8     25.7   18.8  \n",
       "3    31.7     27.3   16.9  \n",
       "4    34.7     29.1   18.4  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw = pd.read_csv(\"./data/체질검사.csv\", encoding = '949')\n",
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>WEIGHT</th>\n",
       "      <th>HEIGHT</th>\n",
       "      <th>NECK</th>\n",
       "      <th>CHEST</th>\n",
       "      <th>ABDOMEN</th>\n",
       "      <th>HIP</th>\n",
       "      <th>THIGH</th>\n",
       "      <th>KNEE</th>\n",
       "      <th>ANKLE</th>\n",
       "      <th>BICEPS</th>\n",
       "      <th>FOREARM</th>\n",
       "      <th>WRIST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46</td>\n",
       "      <td>363.15</td>\n",
       "      <td>72.25</td>\n",
       "      <td>51.2</td>\n",
       "      <td>136.2</td>\n",
       "      <td>148.1</td>\n",
       "      <td>147.7</td>\n",
       "      <td>87.3</td>\n",
       "      <td>49.1</td>\n",
       "      <td>29.6</td>\n",
       "      <td>45.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>168.00</td>\n",
       "      <td>71.25</td>\n",
       "      <td>38.1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>79.1</td>\n",
       "      <td>94.5</td>\n",
       "      <td>57.3</td>\n",
       "      <td>36.2</td>\n",
       "      <td>24.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>177.75</td>\n",
       "      <td>68.50</td>\n",
       "      <td>38.7</td>\n",
       "      <td>102.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>98.3</td>\n",
       "      <td>55.0</td>\n",
       "      <td>38.3</td>\n",
       "      <td>21.8</td>\n",
       "      <td>30.8</td>\n",
       "      <td>25.7</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57</td>\n",
       "      <td>147.75</td>\n",
       "      <td>65.75</td>\n",
       "      <td>35.2</td>\n",
       "      <td>99.6</td>\n",
       "      <td>86.4</td>\n",
       "      <td>90.1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>21.3</td>\n",
       "      <td>31.7</td>\n",
       "      <td>27.3</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51</td>\n",
       "      <td>219.00</td>\n",
       "      <td>64.00</td>\n",
       "      <td>41.2</td>\n",
       "      <td>119.8</td>\n",
       "      <td>122.1</td>\n",
       "      <td>112.8</td>\n",
       "      <td>62.5</td>\n",
       "      <td>36.9</td>\n",
       "      <td>23.6</td>\n",
       "      <td>34.7</td>\n",
       "      <td>29.1</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGE  WEIGHT  HEIGHT  NECK  CHEST  ABDOMEN    HIP  THIGH  KNEE  ANKLE  \\\n",
       "0   46  363.15   72.25  51.2  136.2    148.1  147.7   87.3  49.1   29.6   \n",
       "1   27  168.00   71.25  38.1   93.0     79.1   94.5   57.3  36.2   24.5   \n",
       "2   69  177.75   68.50  38.7  102.0     95.0   98.3   55.0  38.3   21.8   \n",
       "3   57  147.75   65.75  35.2   99.6     86.4   90.1   53.0  35.0   21.3   \n",
       "4   51  219.00   64.00  41.2  119.8    122.1  112.8   62.5  36.9   23.6   \n",
       "\n",
       "   BICEPS  FOREARM  WRIST  \n",
       "0    45.0     29.0   21.4  \n",
       "1    29.0     30.0   18.8  \n",
       "2    30.8     25.7   18.8  \n",
       "3    31.7     27.3   16.9  \n",
       "4    34.7     29.1   18.4  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw_y = df_raw['FAT']\n",
    "df_raw_x = df_raw.drop(\"FAT\", axis = 1, inplace = False)\n",
    "df_raw_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data X size : (176, 13)\n",
      "train data Y size : (176,)\n",
      "test data X size : (76, 13)\n",
      "test data Y size : (76,)\n"
     ]
    }
   ],
   "source": [
    "df_train_x, df_test_x, df_train_y, df_test_y = train_test_split(df_raw_x, df_raw_y\n",
    "                                                               , test_size = 0.3, random_state = 1234)\n",
    "print('train data X size : {}'.format(df_train_x.shape))\n",
    "print('train data Y size : {}'.format(df_train_y.shape))\n",
    "print('test data X size : {}'.format(df_test_x.shape))\n",
    "print('test data Y size : {}'.format(df_test_y.shape))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on trainning set : 0.740\n",
      "Score on test set : 0.539\n"
     ]
    }
   ],
   "source": [
    "knn_uncustomized = KNeighborsRegressor()\n",
    "knn_uncustomized.fit(df_train_x, df_train_y)\n",
    "\n",
    "print('Score on trainning set : {:.3f}'.format(knn_uncustomized.score(df_train_x, df_train_y)))\n",
    "print('Score on test set : {:.3f}'.format(knn_uncustomized.score(df_test_x, df_test_y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "          weights='uniform')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_uncustomized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_score = [];test_score=[]\n",
    "para_n_neighbors = [i for i in range(1, 16)]\n",
    "\n",
    "for v_n_neighbors in para_n_neighbors:\n",
    "    knn = KNeighborsRegressor(n_neighbors = v_n_neighbors)\n",
    "    knn.fit(df_train_x, df_train_y)\n",
    "    train_score.append(knn.score(df_train_x, df_train_y))\n",
    "    test_score.append(knn.score(df_test_x, df_test_y))\n",
    "    \n",
    "#저장\n",
    "df_score_knn = pd.DataFrame()\n",
    "df_score_knn['Neighbors'] = para_n_neighbors\n",
    "df_score_knn['TrainScore'] = train_score\n",
    "df_score_knn['TestScore'] = test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighbors</th>\n",
       "      <th>TrainScore</th>\n",
       "      <th>TestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.740</td>\n",
       "      <td>0.539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0.524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0.626</td>\n",
       "      <td>0.532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0.615</td>\n",
       "      <td>0.524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Neighbors  TrainScore  TestScore\n",
       "0           1       1.000      0.260\n",
       "1           2       0.841      0.416\n",
       "2           3       0.793      0.468\n",
       "3           4       0.755      0.512\n",
       "4           5       0.740      0.539\n",
       "5           6       0.720      0.515\n",
       "6           7       0.700      0.499\n",
       "7           8       0.687      0.516\n",
       "8           9       0.664      0.518\n",
       "9          10       0.646      0.524\n",
       "10         11       0.637      0.528\n",
       "11         12       0.629      0.526\n",
       "12         13       0.626      0.532\n",
       "13         14       0.616      0.528\n",
       "14         15       0.615      0.524"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score_knn.round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> neighbors 5가 적절"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_score = [];test_score=[]\n",
    "para_n_neighbors = [i for i in range(1, 11)] * 2\n",
    "para_weights = (['uniform']*10) + (['distance']*10)\n",
    "\n",
    "for (v_n_neighbors, v_weights) in zip(para_n_neighbors, para_weights):\n",
    "    knn = KNeighborsRegressor(n_neighbors = v_n_neighbors, weights = v_weights)\n",
    "    knn.fit(df_train_x, df_train_y)\n",
    "    train_score.append(knn.score(df_train_x, df_train_y))\n",
    "    test_score.append(knn.score(df_test_x, df_test_y))\n",
    "    \n",
    "#저장\n",
    "df_score_weight = pd.DataFrame()\n",
    "df_score_weight['Neighbors'] = para_n_neighbors\n",
    "df_score_weight['Weights'] = para_weights\n",
    "df_score_weight['TrainScore'] = train_score\n",
    "df_score_weight['TestScore'] = test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Neighbors</th>\n",
       "      <th>Weights</th>\n",
       "      <th>TrainScore</th>\n",
       "      <th>TestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>uniform</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.260301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>uniform</td>\n",
       "      <td>0.840753</td>\n",
       "      <td>0.415927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>uniform</td>\n",
       "      <td>0.792742</td>\n",
       "      <td>0.467537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>uniform</td>\n",
       "      <td>0.754752</td>\n",
       "      <td>0.511723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>uniform</td>\n",
       "      <td>0.740025</td>\n",
       "      <td>0.538785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Neighbors  Weights  TrainScore  TestScore\n",
       "0          1  uniform    1.000000   0.260301\n",
       "1          2  uniform    0.840753   0.415927\n",
       "2          3  uniform    0.792742   0.467537\n",
       "3          4  uniform    0.754752   0.511723\n",
       "4          5  uniform    0.740025   0.538785"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score_weight.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">TrainScore</th>\n",
       "      <th colspan=\"2\" halign=\"left\">TestScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weights</th>\n",
       "      <th>distance</th>\n",
       "      <th>uniform</th>\n",
       "      <th>distance</th>\n",
       "      <th>uniform</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighbors</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.432</td>\n",
       "      <td>0.416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.487</td>\n",
       "      <td>0.468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.527</td>\n",
       "      <td>0.512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.740</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          TrainScore         TestScore        \n",
       "Weights     distance uniform  distance uniform\n",
       "Neighbors                                     \n",
       "1                1.0   1.000     0.260   0.260\n",
       "2                1.0   0.841     0.432   0.416\n",
       "3                1.0   0.793     0.487   0.468\n",
       "4                1.0   0.755     0.527   0.512\n",
       "5                1.0   0.740     0.550   0.539"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score_weight_pivot = df_score_weight.pivot(index = 'Neighbors', columns = 'Weights', values = ['TrainScore', 'TestScore'])\n",
    "df_score_weight_pivot.round(3).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f782bbb0978>"
      ]
     },
     "execution_count": 24,
     "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": [
    "sns.lineplot(data = df_score_weight_pivot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_score = [];test_score=[]\n",
    "para_n_neighbors = [i for i in range(1, 11)] * 3\n",
    "para_metric = ['minkowski']*10 + ['euclidean']*10 + ['manhattan'] * 10\n",
    "\n",
    "for (v_n_neighbors, v_metric) in zip(para_n_neighbors, para_metric):\n",
    "    knn = KNeighborsRegressor(n_neighbors = v_n_neighbors, metric = v_metric)\n",
    "    \n",
    "    knn.fit(df_train_x, df_train_y)\n",
    "    train_score.append(knn.score(df_train_x, df_train_y))\n",
    "    test_score.append(knn.score(df_test_x, df_test_y))\n",
    "    \n",
    "#저장\n",
    "df_score_metric = pd.DataFrame()\n",
    "df_score_metric['Neighbors'] = para_n_neighbors\n",
    "df_score_metric['Metric'] = para_metric\n",
    "df_score_metric['TrainScore'] = train_score\n",
    "df_score_metric['TestScore'] = test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Neighbors</th>\n",
       "      <th>Metric</th>\n",
       "      <th>TrainScore</th>\n",
       "      <th>TestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>minkowski</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>minkowski</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>minkowski</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>minkowski</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>minkowski</td>\n",
       "      <td>0.740</td>\n",
       "      <td>0.539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>minkowski</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>minkowski</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>minkowski</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>minkowski</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>minkowski</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0.524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>0.740</td>\n",
       "      <td>0.539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0.524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>manhattan</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>manhattan</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>manhattan</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4</td>\n",
       "      <td>manhattan</td>\n",
       "      <td>0.766</td>\n",
       "      <td>0.448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5</td>\n",
       "      <td>manhattan</td>\n",
       "      <td>0.744</td>\n",
       "      <td>0.506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>6</td>\n",
       "      <td>manhattan</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>7</td>\n",
       "      <td>manhattan</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8</td>\n",
       "      <td>manhattan</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>9</td>\n",
       "      <td>manhattan</td>\n",
       "      <td>0.694</td>\n",
       "      <td>0.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10</td>\n",
       "      <td>manhattan</td>\n",
       "      <td>0.677</td>\n",
       "      <td>0.551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Neighbors     Metric  TrainScore  TestScore\n",
       "0           1  minkowski       1.000      0.260\n",
       "1           2  minkowski       0.841      0.416\n",
       "2           3  minkowski       0.793      0.468\n",
       "3           4  minkowski       0.755      0.512\n",
       "4           5  minkowski       0.740      0.539\n",
       "5           6  minkowski       0.720      0.515\n",
       "6           7  minkowski       0.700      0.499\n",
       "7           8  minkowski       0.687      0.516\n",
       "8           9  minkowski       0.664      0.518\n",
       "9          10  minkowski       0.646      0.524\n",
       "10          1  euclidean       1.000      0.260\n",
       "11          2  euclidean       0.841      0.416\n",
       "12          3  euclidean       0.793      0.468\n",
       "13          4  euclidean       0.755      0.512\n",
       "14          5  euclidean       0.740      0.539\n",
       "15          6  euclidean       0.720      0.515\n",
       "16          7  euclidean       0.700      0.499\n",
       "17          8  euclidean       0.687      0.516\n",
       "18          9  euclidean       0.664      0.518\n",
       "19         10  euclidean       0.646      0.524\n",
       "20          1  manhattan       1.000      0.142\n",
       "21          2  manhattan       0.840      0.357\n",
       "22          3  manhattan       0.790      0.381\n",
       "23          4  manhattan       0.766      0.448\n",
       "24          5  manhattan       0.744      0.506\n",
       "25          6  manhattan       0.718      0.547\n",
       "26          7  manhattan       0.708      0.549\n",
       "27          8  manhattan       0.707      0.545\n",
       "28          9  manhattan       0.694      0.550\n",
       "29         10  manhattan       0.677      0.551"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score_metric.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f782b043940>"
      ]
     },
     "execution_count": 42,
     "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": [
    "df_score_metric_pivot = df_score_metric.pivot(index = 'Neighbors', columns = 'Metric', values = ['TrainScore', 'TestScore'])\n",
    "level0 = df_score_metric_pivot.columns.get_level_values(0)\n",
    "level1 = df_score_metric_pivot.columns.get_level_values(1)\n",
    "df_score_metric_pivot.columns = level0 + '_' + level1\n",
    "sns.lineplot(data = df_score_metric_pivot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 시계열 분석"
   ]
  },
  {
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