Tobigs_4wk_KNN_과제_000.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_PTZ7GaPe4PF"
   },
   "source": [
    "# 20기 KNN 정규세션 과제"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k_IkS8Ybe4PI"
   },
   "source": [
    "**데이터:** [blackfriday | Kaggle](https://www.kaggle.com/llopesolivei/blackfriday)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZvUdeo2he4PI"
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lTlQgiSie4PI"
   },
   "source": [
    "## 0. 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "id": "eNTRK8Ete4PJ",
    "outputId": "de247747-4645-49a6-c5d2-38cc1a381d42"
   },
   "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>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001088</td>\n",
       "      <td>P00046042</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1004493</td>\n",
       "      <td>P00347742</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1005302</td>\n",
       "      <td>P00048942</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001348</td>\n",
       "      <td>P00145242</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001348</td>\n",
       "      <td>P00106742</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5780</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID Product_ID Gender   Age  Occupation City_Category  \\\n",
       "0  1001088  P00046042      F  0-17          10             A   \n",
       "1  1004493  P00347742      F  0-17          10             A   \n",
       "2  1005302  P00048942      F  0-17          10             A   \n",
       "3  1001348  P00145242      F  0-17          10             A   \n",
       "4  1001348  P00106742      F  0-17          10             A   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          3               0                   5   \n",
       "1                          1               0                   7   \n",
       "2                          1               0                   1   \n",
       "3                          3               0                   2   \n",
       "4                          3               0                   3   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  \n",
       "0                17.0                 NaN      2010  \n",
       "1                 NaN                 NaN      4483  \n",
       "2                 4.0                 NaN      7696  \n",
       "3                 4.0                 NaN     16429  \n",
       "4                 5.0                 NaN      5780  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"blackfriday.csv\", index_col = 0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Preprocssing / EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1-1 데이터 탐색 및 결측치 제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4998 entries, 0 to 4997\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   User_ID                     4998 non-null   int64  \n",
      " 1   Product_ID                  4998 non-null   object \n",
      " 2   Gender                      4998 non-null   object \n",
      " 3   Age                         4998 non-null   object \n",
      " 4   Occupation                  4998 non-null   int64  \n",
      " 5   City_Category               4998 non-null   object \n",
      " 6   Stay_In_Current_City_Years  4998 non-null   object \n",
      " 7   Marital_Status              4998 non-null   int64  \n",
      " 8   Product_Category_1          4998 non-null   int64  \n",
      " 9   Product_Category_2          3465 non-null   float64\n",
      " 10  Product_Category_3          1544 non-null   float64\n",
      " 11  Purchase                    4998 non-null   int64  \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 507.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Product_Category_2, Product_Category_3 두 feature에서 결측치 확인` <br>\n",
    "많은 비율의 결측치를 가지고 있기 때문에 제거하고자 한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 결측치 제거\n",
    "df = df.drop(['Product_Category_2','Product_Category_3'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID                       0\n",
       "Product_ID                    0\n",
       "Gender                        0\n",
       "Age                           0\n",
       "Occupation                    0\n",
       "City_Category                 0\n",
       "Stay_In_Current_City_Years    0\n",
       "Marital_Status                0\n",
       "Product_Category_1            0\n",
       "Purchase                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4998, 10)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>User_ID</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.998000e+03</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.003015e+06</td>\n",
       "      <td>8.036815</td>\n",
       "      <td>0.421369</td>\n",
       "      <td>5.320128</td>\n",
       "      <td>9234.655462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.709910e+03</td>\n",
       "      <td>6.442697</td>\n",
       "      <td>0.493828</td>\n",
       "      <td>3.912281</td>\n",
       "      <td>4999.225081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000003e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.001560e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5497.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.003040e+06</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8049.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.004417e+06</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12039.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.006037e+06</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>23913.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            User_ID   Occupation  Marital_Status  Product_Category_1  \\\n",
       "count  4.998000e+03  4998.000000     4998.000000         4998.000000   \n",
       "mean   1.003015e+06     8.036815        0.421369            5.320128   \n",
       "std    1.709910e+03     6.442697        0.493828            3.912281   \n",
       "min    1.000003e+06     0.000000        0.000000            1.000000   \n",
       "25%    1.001560e+06     2.000000        0.000000            1.000000   \n",
       "50%    1.003040e+06     7.000000        0.000000            5.000000   \n",
       "75%    1.004417e+06    14.000000        1.000000            8.000000   \n",
       "max    1.006037e+06    20.000000        1.000000           20.000000   \n",
       "\n",
       "           Purchase  \n",
       "count   4998.000000  \n",
       "mean    9234.655462  \n",
       "std     4999.225081  \n",
       "min       13.000000  \n",
       "25%     5497.750000  \n",
       "50%     8049.000000  \n",
       "75%    12039.000000  \n",
       "max    23913.000000  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'User_ID': 2656,\n",
       " 'Product_ID': 1797,\n",
       " 'Gender': 2,\n",
       " 'Age': 7,\n",
       " 'Occupation': 21,\n",
       " 'City_Category': 3,\n",
       " 'Stay_In_Current_City_Years': 5,\n",
       " 'Marital_Status': 2,\n",
       " 'Product_Category_1': 20,\n",
       " 'Purchase': 3736}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# unique value 개수파악\n",
    "\n",
    "unique_cnt = {}\n",
    "\n",
    "for i in df.columns :\n",
    "    unique_cnt[i] = df[i].nunique()\n",
    "    \n",
    "unique_cnt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1-2 데이터 파악\n",
    "\n",
    "`feature(unique 데이터 수)`\n",
    "\n",
    "1. User_ID : 사용자 ID(2656)\n",
    "2. Product_ID : 상품 ID(1797)\n",
    "3. Gender : 성(2)\n",
    "4. Age : 연령(7)\n",
    "5. Occupation : 직업(21)\n",
    "6. City_Category : 도시 종류(3)\n",
    "7. Stay_In_Current_City_Years : 현재 도시에 머문 년수(5)\n",
    "8. Marital_Status : 결혼 여부(2)\n",
    "9. Product_Category_1 : 상품 범주 1(20)\n",
    "\n",
    "10. Purchase - 구매량(3736) -> `target 변수`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1-3 데이터 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='count'>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gender\n",
    "\n",
    "sns.countplot(x = \"Gender\", data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`구매자의 대부분은 남성임을 확인할 수 있다.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Marital_Status', ylabel='count'>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = \"Marital_Status\", data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`미혼의 사람들이 기혼의 사람들 보다 더 많이 구매한 것을 확인할 수 있다.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='count'>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(x = \"Age\", data = df, color = \"r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`구매자 중 청년의 비율이 압도적으로 많은 것을 확인할 수 있다.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Occupation', ylabel='count'>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.countplot(x = \"Occupation\", data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='City_Category', ylabel='count'>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = \"City_Category\", data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Stay_In_Current_City_Years', ylabel='count'>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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atGnZbrvtctZZZ2Xs2LHve87m5uZ07949ixYtWuvLGKG19ev3cmuPwEY0Z07f1h4BALYo69IGrRpbbYXYoi0RW1sWsQUAG9e6tMEm/Z4tAACAtkpsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlEBsAQAAlKBVY2vKlCk56qijUldXl6qqqtx1110t9hdFkQsuuCB9+vRJ586dc9hhh+XFF19ssea1117LCSeckG7duqVHjx4ZOXJkFi9e3GLNzJkzc8ABB6RTp06pr6/P5ZdfXvapAQAAW7hWja0lS5Zk4MCBmThx4hr3X3755fne976XH/3oR3niiSey9dZbZ9iwYXnrrbcqa0444YQ899xzmTRpUu65555MmTIlp59+emV/c3Nzhg4dmoaGhkyfPj1XXHFFxo8fnxtuuKH08wMAALZcVUVRFK09RJJUVVXlzjvvzNFHH53kH89q1dXV5Wtf+1rOPffcJMmiRYvSu3fv3HzzzTn22GPzhz/8If3798+0adOy3377JUnuu+++fOpTn8pf//rX1NXV5brrrsu//du/pampKdXV1UmSb37zm7nrrrvywgsvvK/Zmpub07179yxatCjdunXb8CcPG1C/fi+39ghsRHPm9G3tEQBgi7IubbDJvmdrzpw5aWpqymGHHVbZ1r179wwePDhTp05NkkydOjU9evSohFaSHHbYYWnXrl2eeOKJypoDDzywElpJMmzYsMyaNSuvv/76Gu976dKlaW5ubnEBAABYF5tsbDU1NSVJevfu3WJ77969K/uamprSq1evFvs7dOiQnj17tlizpmO88z7+2YQJE9K9e/fKpb6+/oOfEAAAsEXZZGOrNY0bNy6LFi2qXP7yl7+09kgAAEAbs8nGVm1tbZJk/vz5LbbPnz+/sq+2tjYLFixosX/FihV57bXXWqxZ0zHeeR//rKamJt26dWtxAQAAWBfrFVuHHHJIFi5cuNr25ubmHHLIIR90piRJv379UltbmwcffLDF8Z944okMGTIkSTJkyJAsXLgw06dPr6x56KGHsmrVqgwePLiyZsqUKVm+fHllzaRJk7Lrrrtmm2222SCzAgAA/LP1iq2HH344y5YtW237W2+9lUceeeR9H2fx4sVpbGxMY2Njkn98KEZjY2Pmzp2bqqqqnH322bn00kvzH//xH3nmmWdy0kknpa6urvKJhbvvvnuOOOKInHbaaXnyySfz6KOPZvTo0Tn22GNTV1eXJDn++ONTXV2dkSNH5rnnnsvtt9+ea6+9NmPGjFmfUwcAAHhfOqzL4pkzZ1Z+fv7551t8wMTKlStz33335UMf+tD7Pt5TTz2Vgw8+uHL97QAaMWJEbr755nzjG9/IkiVLcvrpp2fhwoX5xCc+kfvuuy+dOnWq3OaWW27J6NGjc+ihh6Zdu3Y55phj8r3vfa+yv3v37rn//vszatSoDBo0KNttt10uuOCCFt/FBQAAsKGt0/dstWvXLlVVVUn+8T1Y/6xz5875/ve/n1NPPXXDTbgJ8D1btCW+Z2vL4nu2AGDjWpc2WKdntubMmZOiKLLTTjvlySefzPbbb1/ZV11dnV69eqV9+/brNzUAAMBmZJ1iq6GhIUmyatWqUoYBAADYXKxTbL3Tiy++mN/+9rdZsGDBavF1wQUXfODBAAAA2rL1iq3/+T//Z84444xst912qa2trbyPK0mqqqrEFgAAsMVbr9i69NJL861vfStjx47d0PMAAABsFtbre7Zef/31fP7zn9/QswAAAGw21iu2Pv/5z+f+++/f0LMAAABsNtbrZYS77LJLzj///Dz++OPZa6+90rFjxxb7v/rVr26Q4QAAANqqdfpS47f169fv3Q9YVZU//elPH2ioTY0vNaYt8aXGWxZfagwAG1dpX2r8tjlz5qzXYAAAAFuK9XrPFgAAAO9tvZ7ZOvXUU99z/4033rhewwAAAGwu1iu2Xn/99RbXly9fnmeffTYLFy7MIYccskEGAwAAaMvWK7buvPPO1batWrUqZ5xxRnbeeecPPBQAAEBbt8Hes9WuXbuMGTMmV1999YY6JAAAQJu1Xs9svZuXXnopK1as2JCHBGAT5WsGtiy+ZgBg3a1XbI0ZM6bF9aIoMm/evNx7770ZMWLEBhlsc+MfJVsW/ygBAGC9Yuv3v/99i+vt2rXL9ttvnyuvvHKtn1QIAACwJViv2Prtb3+7oecAAADYrHyg92y98sormTVrVpJk1113zfbbb79BhgIAAGjr1uvTCJcsWZJTTz01ffr0yYEHHpgDDzwwdXV1GTlyZN58880NPSMAAECbs16xNWbMmEyePDm/+tWvsnDhwixcuDB33313Jk+enK997WsbekYAAIA2Z71eRvjv//7v+eUvf5mDDjqosu1Tn/pUOnfunH/913/Nddddt6HmAwAAaJPW65mtN998M717915te69evbyMEAAAIOsZW0OGDMmFF16Yt956q7Lt73//ey666KIMGTJkgw0HAADQVq3XywivueaaHHHEEdlhhx0ycODAJMnTTz+dmpqa3H///Rt0QAAAgLZovWJrr732yosvvphbbrklL7zwQpLkuOOOywknnJDOnTtv0AEBAADaovWKrQkTJqR379457bTTWmy/8cYb88orr2Ts2LEbZDgAAIC2ar3es3X99ddnt912W237HnvskR/96EcfeCgAAIC2br1iq6mpKX369Flt+/bbb5958+Z94KEAAADauvWKrfr6+jz66KOrbX/00UdTV1f3gYcCAABo69brPVunnXZazj777CxfvjyHHHJIkuTBBx/MN77xjXzta1/boAMCAAC0ResVW1//+tfz6quv5swzz8yyZcuSJJ06dcrYsWMzbty4DTogAABAW7ResVVVVZXvfOc7Of/88/OHP/whnTt3zoc//OHU1NRs6PkAAADapPWKrbd16dIl+++//4aaBQAAYLOxXh+QAQAAwHsTWwAAACUQWwAAACUQWwAAACUQWwAAACUQWwAAACUQWwAAACUQWwAAACUQWwAAACUQWwAAACUQWwAAACUQWwAAACUQWwAAACUQWwAAACUQWwAAACUQWwAAACUQWwAAACXY5GOrb9++qaqqWu0yatSoJMlBBx202r6vfOUrLY4xd+7cHHnkkdlqq63Sq1evfP3rX8+KFSta43QAAIAtRIfWHmBtpk2blpUrV1auP/vsszn88MPz+c9/vrLttNNOy8UXX1y5vtVWW1V+XrlyZY488sjU1tbmsccey7x583LSSSelY8eO+fa3v71xTgIAANjibPKxtf3227e4ftlll2XnnXfOJz/5ycq2rbbaKrW1tWu8/f3335/nn38+DzzwQHr37p299947l1xyScaOHZvx48enurq61PkBAIAt0yb/MsJ3WrZsWX72s5/l1FNPTVVVVWX7Lbfcku222y577rlnxo0blzfffLOyb+rUqdlrr73Su3fvyrZhw4alubk5zz333BrvZ+nSpWlubm5xAQAAWBeb/DNb73TXXXdl4cKFOfnkkyvbjj/++DQ0NKSuri4zZ87M2LFjM2vWrNxxxx1JkqamphahlaRyvampaY33M2HChFx00UXlnAQAALBFaFOx9eMf/zjDhw9PXV1dZdvpp59e+XmvvfZKnz59cuihh+all17KzjvvvF73M27cuIwZM6Zyvbm5OfX19es/OAAAsMVpM7H15z//OQ888EDlGat3M3jw4CTJ7Nmzs/POO6e2tjZPPvlkizXz589Pknd9n1dNTU1qamo2wNQAwAfVr9/LrT0CG9GcOX1bewTYYNrMe7Zuuumm9OrVK0ceeeR7rmtsbEyS9OnTJ0kyZMiQPPPMM1mwYEFlzaRJk9KtW7f079+/tHkBAIAtW5t4ZmvVqlW56aabMmLEiHTo8P9Gfumll3LrrbfmU5/6VLbddtvMnDkz55xzTg488MAMGDAgSTJ06ND0798/J554Yi6//PI0NTXlvPPOy6hRozx7BQAAlKZNxNYDDzyQuXPn5tRTT22xvbq6Og888ECuueaaLFmyJPX19TnmmGNy3nnnVda0b98+99xzT84444wMGTIkW2+9dUaMGNHie7kAAAA2tDYRW0OHDk1RFKttr6+vz+TJk9d6+4aGhvz6178uYzQAAIA1ajPv2QIAAGhLxBYAAEAJxBYAAEAJxBYAAEAJxBYAAEAJxBYAAEAJxBYAAEAJxBYAAEAJxBYAAEAJxBYAAEAJxBYAAEAJxBYAAEAJOrT2AAAAsCno1+/l1h6BjWjOnL6l34dntgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEogtgAAAEqwScfW+PHjU1VV1eKy2267Vfa/9dZbGTVqVLbddtt06dIlxxxzTObPn9/iGHPnzs2RRx6ZrbbaKr169crXv/71rFixYmOfCgAAsIXp0NoDrM0ee+yRBx54oHK9Q4f/N/I555yTe++9N7/4xS/SvXv3jB49Op/97Gfz6KOPJklWrlyZI488MrW1tXnssccyb968nHTSSenYsWO+/e1vb/RzAQAAthybfGx16NAhtbW1q21ftGhRfvzjH+fWW2/NIYcckiS56aabsvvuu+fxxx/Pxz72sdx///15/vnn88ADD6R3797Ze++9c8kll2Ts2LEZP358qqurN/bpAAAAW4hN+mWESfLiiy+mrq4uO+20U0444YTMnTs3STJ9+vQsX748hx12WGXtbrvtlh133DFTp05NkkydOjV77bVXevfuXVkzbNiwNDc357nnnnvX+1y6dGmam5tbXAAAANbFJh1bgwcPzs0335z77rsv1113XebMmZMDDjggb7zxRpqamlJdXZ0ePXq0uE3v3r3T1NSUJGlqamoRWm/vf3vfu5kwYUK6d+9eudTX12/YEwMAADZ7m/TLCIcPH175ecCAARk8eHAaGhryf/7P/0nnzp1Lu99x48ZlzJgxlevNzc2CCwAAWCeb9DNb/6xHjx75yEc+ktmzZ6e2tjbLli3LwoULW6yZP39+5T1etbW1q3064dvX1/Q+sLfV1NSkW7duLS4AAADrok3F1uLFi/PSSy+lT58+GTRoUDp27JgHH3ywsn/WrFmZO3duhgwZkiQZMmRInnnmmSxYsKCyZtKkSenWrVv69++/0ecHAAC2HJv0ywjPPffcHHXUUWloaMjf/va3XHjhhWnfvn2OO+64dO/ePSNHjsyYMWPSs2fPdOvWLWeddVaGDBmSj33sY0mSoUOHpn///jnxxBNz+eWXp6mpKeedd15GjRqVmpqaVj47AABgc7ZJx9Zf//rXHHfccXn11Vez/fbb5xOf+EQef/zxbL/99kmSq6++Ou3atcsxxxyTpUuXZtiwYfnhD39YuX379u1zzz335IwzzsiQIUOy9dZbZ8SIEbn44otb65QAAIAtxCYdW7fddtt77u/UqVMmTpyYiRMnvuuahoaG/PrXv97QowEAALynNvWeLQAAgLZCbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRAbAEAAJRgk46tCRMmZP/990/Xrl3Tq1evHH300Zk1a1aLNQcddFCqqqpaXL7yla+0WDN37twceeSR2WqrrdKrV698/etfz4oVKzbmqQAAAFuYDq09wHuZPHlyRo0alf333z8rVqzI//gf/yNDhw7N888/n6233rqy7rTTTsvFF19cub7VVltVfl65cmWOPPLI1NbW5rHHHsu8efNy0kknpWPHjvn2t7+9Uc8HAADYcmzSsXXfffe1uH7zzTenV69emT59eg488MDK9q222iq1tbVrPMb999+f559/Pg888EB69+6dvffeO5dccknGjh2b8ePHp7q6utRzAAAAtkyb9MsI/9miRYuSJD179myx/ZZbbsl2222XPffcM+PGjcubb75Z2Td16tTstdde6d27d2XbsGHD0tzcnOeee26N97N06dI0Nze3uAAAAKyLTfqZrXdatWpVzj777PzLv/xL9txzz8r2448/Pg0NDamrq8vMmTMzduzYzJo1K3fccUeSpKmpqUVoJalcb2pqWuN9TZgwIRdddFFJZwIAAGwJ2kxsjRo1Ks8++2x+97vftdh++umnV37ea6+90qdPnxx66KF56aWXsvPOO6/XfY0bNy5jxoypXG9ubk59ff36DQ4AAGyR2sTLCEePHp177rknv/3tb7PDDju859rBgwcnSWbPnp0kqa2tzfz581usefv6u73Pq6amJt26dWtxAQAAWBebdGwVRZHRo0fnzjvvzEMPPZR+/fqt9TaNjY1Jkj59+iRJhgwZkmeeeSYLFiyorJk0aVK6deuW/v37lzI3AADAJv0ywlGjRuXWW2/N3Xffna5du1beY9W9e/d07tw5L730Um699dZ86lOfyrbbbpuZM2fmnHPOyYEHHpgBAwYkSYYOHZr+/fvnxBNPzOWXX56mpqacd955GTVqVGpqalrz9AAAgM3YJv3M1nXXXZdFixbloIMOSp8+fSqX22+/PUlSXV2dBx54IEOHDs1uu+2Wr33taznmmGPyq1/9qnKM9u3b55577kn79u0zZMiQfPGLX8xJJ53U4nu5AAAANrRN+pmtoijec399fX0mT5681uM0NDTk17/+9YYaCwAAYK026We2AAAA2iqxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUAKxBQAAUIItKrYmTpyYvn37plOnThk8eHCefPLJ1h4JAADYTG0xsXX77bdnzJgxufDCCzNjxowMHDgww4YNy4IFC1p7NAAAYDO0xcTWVVddldNOOy2nnHJK+vfvnx/96EfZaqutcuONN7b2aAAAwGaoQ2sPsDEsW7Ys06dPz7hx4yrb2rVrl8MOOyxTp05dbf3SpUuzdOnSyvVFixYlSZqbm9d7hlWr3ljv29L2fJDHygflsbZl8VhjY/FYY2PxWGNjWd/H2tu3K4pirWu3iNj6r//6r6xcuTK9e/dusb1379554YUXVls/YcKEXHTRRattr6+vL21GNi/du7f2BGwpPNbYWDzW2Fg81thYPuhj7Y033kj3tRxki4itdTVu3LiMGTOmcn3VqlV57bXXsu2226aqqqoVJ2tbmpubU19fn7/85S/p1q1ba4/DZsxjjY3FY42NxWONjcVjbd0VRZE33ngjdXV1a127RcTWdtttl/bt22f+/Pktts+fPz+1tbWrra+pqUlNTU2LbT169ChzxM1at27d/J+XjcJjjY3FY42NxWONjcVjbd2s7Rmtt20RH5BRXV2dQYMG5cEHH6xsW7VqVR588MEMGTKkFScDAAA2V1vEM1tJMmbMmIwYMSL77bdfPvrRj+aaa67JkiVLcsopp7T2aAAAwGZoi4mtL3zhC3nllVdywQUXpKmpKXvvvXfuu+++1T40gw2npqYmF1544WovyYQNzWONjcVjjY3FY42NxWOtXFXF+/nMQgAAANbJFvGeLQAAgI1NbAEAAJRAbAEAAJRAbAEAAJRAbLHBXXfddRkwYEDly/GGDBmS//t//29rj8VmaMqUKTnqqKNSV1eXqqqq3HXXXa09EpupCRMmZP/990/Xrl3Tq1evHH300Zk1a1Zrj8Vm4rLLLktVVVXOPvvs1h6FLczEiRPTt2/fdOrUKYMHD86TTz7Z2iNtdsQWG9wOO+yQyy67LNOnT89TTz2VQw45JJ/5zGfy3HPPtfZobGaWLFmSgQMHZuLEia09Cpu5yZMnZ9SoUXn88cczadKkLF++PEOHDs2SJUtaezTauGnTpuX666/PgAED3nNdVVVVXn755Y0zFFuE22+/PWPGjMmFF16YGTNmZODAgRk2bFgWLFjQ2qNtVnz0OxtFz549c8UVV2TkyJGtPQqbqaqqqtx55505+uijW3sUtgCvvPJKevXqlcmTJ+fAAw9s7XFooxYvXpx99903P/zhD3PppZdm7733zjXXXLPGtVVVVZkzZ0769u27UWdk8zV48ODsv//++cEPfpAkWbVqVerr63PWWWflm9/8ZitPt/nwzBalWrlyZW677bYsWbIkQ4YMae1xADaIRYsWJfnHf0iC9TVq1KgceeSROeyww1p7FLYwy5Yty/Tp01s89tq1a5fDDjssU6dObcXJNj8dWnsANk/PPPNMhgwZkrfeeitdunTJnXfemf79+7f2WAAf2KpVq3L22WfnX/7lX7Lnnnu29ji0UbfddltmzJiRadOmtfYobIH+67/+KytXrkzv3r1bbO/du3deeOGFVppq8yS2KMWuu+6axsbGLFq0KL/85S8zYsSITJ48WXABbd6oUaPy7LPP5ne/+11rj0Ib9Ze//CX//b//90yaNCmdOnVa45rhw4fnkUceabFtjz32SFVVVZKkoaHBe6GhDRBblKK6ujq77LJLkmTQoEGZNm1arr322lx//fWtPBnA+hs9enTuueeeTJkyJTvssENrj0MbNX369CxYsCD77rtvZdvKlSszZcqU/OAHP8jSpUvzv/7X/8rf//73yv4Pf/jD+fWvf50PfehDSZKOHTtu9LnZfGy33XZp37595s+f32L7/PnzU1tb20pTbZ7EFhvFqlWrsnTp0tYeA2C9FEWRs846K3feeWcefvjh9OvXr7VHog079NBD88wzz7TYdsopp2S33XbL2LFj0759+0pUvVNDQ4MPyGCDqK6uzqBBg/Lggw9WPlhq1apVefDBBzN69OjWHW4zI7bY4MaNG5fhw4dnxx13zBtvvJFbb701Dz/8cH7zm9+09mhsZhYvXpzZs2dXrs+ZMyeNjY3p2bNndtxxx1acjM3NqFGjcuutt+buu+9O165d09TUlCTp3r17Onfu3MrT0dZ07dp1tff7bb311tl22229D5CNZsyYMRkxYkT222+/fPSjH80111yTJUuW5JRTTmnt0TYrYosNbsGCBTnppJMyb968dO/ePQMGDMhvfvObHH744a09GpuZp556KgcffHDl+pgxY5IkI0aMyM0339xKU7E5uu6665IkBx10UIvtN910U04++eSNPxDAB/SFL3whr7zySi644II0NTVl7733zn333bfah2bwwfieLQAAgBL4ni0AAIASiC0AAIASiC0AAIASiC0AAIASiC0AAIASiC0AAIASiC0AAIASiC0AaIP69u2ba665prXHAOA9iC2ANu6VV17JGWeckR133DE1NTWpra3NsGHD8uijjyZJqqqqctddd23UmcaPH5+99957gx5z9uzZOeWUU7LDDjukpqYm/fr1y3HHHZennnpqg97PhvTyyy+nqqoqjY2N63S75ubm/Nu//Vt22223dOrUKbW1tTnssMNyxx13pCiKJMm0adNy+umnV26zIf/OkydPTseOHfO73/2uxfYlS5Zkp512yrnnnrtB7gdgc9ehtQcA4IM55phjsmzZsvzkJz/JTjvtlPnz5+fBBx/Mq6++2tqjbTBPPfVUDj300Oy55565/vrrs9tuu+WNN97I3Xffna997WuZPHnyeh135cqVqaqqSrt2Lf/b47Jly1JdXb0hRl9nCxcuzCc+8YksWrQol156afbff/906NAhkydPzje+8Y0ccsgh6dGjR7bffvvSZvjkJz+Zs846KyeffHKefvrpbL311kmSb3zjG+ncuXMuvfTSDX6frfk7ByhNAUCb9frrrxdJiocffniN+xsaGooklUtDQ0NRFEUxe/bs4tOf/nTRq1evYuutty7222+/YtKkSZXbXXTRRcUee+yx2vEGDhxYnHfeeWud68ILLywGDhxYuT5ixIjiM5/5THHFFVcUtbW1Rc+ePYszzzyzWLZs2VqPtWrVqmKPPfYoBg0aVKxcuXK1/a+//npRFEXx29/+tkhSuV4URfH73/++SFLMmTOnKIqiuOmmm4ru3bsXd999d7H77rsX7du3L+bMmVM0NDQUF198cXHiiScWXbt2LUaMGFEURVE88sgjxSc+8YmiU6dOxQ477FCcddZZxeLFiyvHb2hoKL71rW8Vp5xyStGlS5eivr6+uP766yv73/m7T1J88pOfXOv5nnHGGcXWW29d/Od//udq+954441i+fLllfu++uqrKz//8995zpw5RVVVVTFt2rQWx7j66quLHXfccY2/y3f6+9//Xuy+++7FqFGjiqIoioceeqiorq4unnrqqWLlypXFt7/97aJv375Fp06digEDBhS/+MUvKrddsWJFceqpp1b2f+QjHymuueaaFsd/+zFx6aWXFn369Cn69u1bFEVRTJw4sdhll12KmpqaolevXsUxxxyz1t8ZwKZKbAG0YcuXLy+6dOlSnH322cVbb7212v4FCxYUSYqbbrqpmDdvXrFgwYKiKIqisbGx+NGPflQ888wzxR//+MfivPPOKzp16lT8+c9/LoqiKP7yl78U7dq1K5588snKsWbMmFFUVVUVL7300lrnWlNsdevWrfjKV75S/OEPfyh+9atfFVtttVVxww03rPVYM2bMKJIUt95663uue7+x1bFjx+LjH/948eijjxYvvPBCsWTJkqKhoaHo1q1b8d3vfreYPXt25bL11lsXV199dfHHP/6xePTRR4t99tmnOPnkkyvHb2hoKHr27FlMnDixePHFF4sJEyYU7dq1K1544YWiKIriySefLJIUDzzwQDFv3rzi1Vdffc9zWLlyZbHNNtsUp59++lp/L++MrXf7Ox9++OHFmWee2eJ2AwYMKC644IK1Hr8oimLatGlFx44di7vuuqvo27dvMX78+KIoiuLSSy8tdtttt+K+++4rXnrppeKmm24qampqKtG/bNmy4oILLiimTZtW/OlPfyp+9rOfFVtttVVx++23V449YsSIokuXLsWJJ55YPPvss8Wzzz5bTJs2rWjfvn1x6623Fi+//HIxY8aM4tprr31fswJsisQWQBv3y1/+sthmm22KTp06FR//+MeLcePGFU8//XRlf5LizjvvXOtx9thjj+L73/9+5frw4cOLM844o3L9rLPOKg466KD3NdOaYquhoaFYsWJFZdvnP//54gtf+MJaj3X77bcXSYoZM2a857r3G1tJisbGxha3bWhoKI4++ugW20aOHLla9DzyyCNFu3btir///e+V233xi1+s7F+1alXRq1ev4rrrriuKoijmzJlTJCl+//vfr/U8i6Io5s+fXyQprrrqqrWufWdsFcWa/8633357sc0221RCfPr06UVVVVXl9/F+XHDBBUW7du2KQYMGFcuXLy/eeuutYquttioee+yxFutGjhxZHHfcce96nFGjRrV4lmrEiBFF7969i6VLl1a2/fu//3vRrVu3orm5+X3PB7Ap8wEZAG3cMccck7/97W/5j//4jxxxxBF5+OGHs+++++bmm29+19ssXrw45557bnbffff06NEjXbp0yR/+8IfMnTu3sua0007Lz3/+87z11ltZtmxZbr311px66qnrPecee+yR9u3bV6736dMnCxYsWOvtiv//AyE2lOrq6gwYMGC17fvtt1+L608//XRuvvnmdOnSpXIZNmxYVq1alTlz5lTWvfNYVVVVqa2tfV/ntSYb+lyPPvrotG/fPnfeeWeS5Oabb87BBx+cvn37vu9jnH/++Vm1alW++c1vpkOHDpk9e3befPPNHH744S1+Nz/96U/z0ksvVW43ceLEDBo0KNtvv326dOmSG264ocXjK0n22muvFu/TOvzww9PQ0JCddtopJ554Ym655Za8+eabH+yXANCKfEAGwGagU6dOOfzww3P44Yfn/PPPz5e+9KVceOGFOfnkk9e4/txzz82kSZPy3e9+N7vssks6d+6cz33uc1m2bFllzVFHHZWamprceeedqa6uzvLly/O5z31uvWfs2LFji+tVVVVZtWrVWm/3kY98JEnywgsvZJ999nnXdW9/yMU7g2X58uWrrevcuXOqqqpW2/72h0C8bfHixfnyl7+cr371q6ut3XHHHSs/r+95rcn222+fHj165IUXXliv2/+z6urqnHTSSbnpppvy2c9+NrfeemuuvfbadTpGhw4dWvzv4sWLkyT33ntvPvShD7VYW1NTkyS57bbbcu655+bKK6/MkCFD0rVr11xxxRV54oknWqz/5995165dM2PGjDz88MO5//77c8EFF2T8+PGZNm1aevTosU5zA2wKxBbAZqh///6VjwHv2LFjVq5c2WL/o48+mpNPPjn/7b/9tyT/+Af0yy+/3GJNhw4dMmLEiNx0002prq7Osccem86dO2+M8VvYe++9079//1x55ZX5whe+sNonBy5cuLDFp/PNmzcv22yzTZKs80euv9O+++6b559/Prvssst6H+PtZ23++ff/btq1a5djjz02//t//+9ceOGFqaura7F/8eLF6dSpUyV83mlNf+ck+dKXvpQ999wzP/zhD7NixYp89rOfXY8z+X/69++fmpqazJ07N5/85CfXuObRRx/Nxz/+8Zx55pmVbe981uu9dOjQIYcddlgOO+ywXHjhhenRo0ceeuihDzw3QGvwMkKANuzVV1/NIYcckp/97GeZOXNm5syZk1/84he5/PLL85nPfCbJP7789sEHH0xTU1Nef/31JMmHP/zh3HHHHWlsbMzTTz+d448/fo3PxnzpS1/KQw89lPvuu+8DvYTwg6iqqspNN92UP/7xjznggAPy61//On/6058yc+bMfOtb36qc5y677JL6+vqMHz8+L774Yu69995ceeWV632/Y8eOzWOPPZbRo0ensbExL774Yu6+++6MHj36fR+jV69e6dy5c+67777Mnz8/ixYtWuttvvWtb6W+vj6DBw/OT3/60zz//PN58cUXc+ONN2afffapPLP0z9b0d06S3XffPR/72McyduzYHHfccR84mLt27Zpzzz0355xzTn7yk5/kpZdeyowZM/L9738/P/nJT5L84/H11FNP5Te/+U3++Mc/5vzzz8+0adPWeux77rkn3/ve99LY2Jg///nP+elPf5pVq1Zl1113/UAzA7QWsQXQhnXp0iWDBw/O1VdfnQMPPDB77rlnzj///Jx22mn5wQ9+kCS58sorM2nSpNTX11dehnfVVVdlm222ycc//vEcddRRGTZsWPbdd9/Vjv/hD384H//4x7Pbbrtl8ODBG/Xc3umjH/1onnrqqeyyyy457bTTsvvuu+fTn/50nnvuuVxzzTVJ/vHMzs9//vO88MILGTBgQL7zne98oO+DGjBgQCZPnlyJvH322ScXXHDBas82vZcOHTrke9/7Xq6//vrU1dVVwvC99OzZM48//ni++MUv5tJLL80+++yTAw44ID//+c9zxRVXpHv37mu83Zr+zm8bOXJkli1btsGC+ZJLLsn555+fCRMmZPfdd88RRxyRe++9N/369UuSfPnLX85nP/vZfOELX8jgwYPz6quvtniW69306NEjd9xxRw455JDsvvvu+dGPfpSf//zn2WOPPTbI3AAbW1Wxod+NC8BmoyiKfPjDH86ZZ56ZMWPGtPY4rKdLLrkkv/jFLzJz5szWHgVgi+I9WwCs0SuvvJLbbrstTU1NOeWUU1p7HNbD2+/F+8EPfvCBnuUDYP14GSEAa9SrV69cfPHFueGGGyofOPG2d37k9z9fHnnkkXW6n0ceeeQ9j7e52ZC/u7UZPXp0Bg0alIMOOmi1lxB+5Stfedc5vvKVr2zQOQC2VF5GCMA6mz179rvu+9CHPrROH8Lw97//Pf/5n//5rvs/yKcBboo25O/ug1iwYEGam5vXuK9bt27p1avXRpkDYHMmtgAAAErgZYQAAAAlEFsAAAAlEFsAAAAlEFsAAAAlEFsAAAAlEFsAAAAlEFsAAAAlEFsAAAAl+P8Ae938xIHYbH8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "sns.countplot(x = \"Stay_In_Current_City_Years\", data = df, color = \"b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_Category_1', ylabel='count'>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.countplot(x = \"Product_Category_1\", data = df, color = \"g\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "시각화를 진행하면서 User_ID는 사용자들의 고유 번호이기 때문에, 분석 과정에서 영향력이 없음으로 판단되어서 이 또한 삭제하고자 한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop([\"User_ID\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4998, 9)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_ID', ylabel='count'>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.countplot(x =\"Product_ID\", data =df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`정말 많은 고유값을 가지고 있음을 확인할 수 있다.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='count'>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# age, gender\n",
    "\n",
    "sns.countplot(x= \"Age\",  hue = \"Gender\", data = df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='Purchase'>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gender, Purchase\n",
    "sns.boxplot(x = df['Gender'], y = df['Purchase'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1-4 원 핫 인코딩 진행 (One-Hot-Encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>Product_ID_P00000142</th>\n",
       "      <th>Product_ID_P00000242</th>\n",
       "      <th>Product_ID_P00000342</th>\n",
       "      <th>Product_ID_P00000442</th>\n",
       "      <th>Product_ID_P00000642</th>\n",
       "      <th>Product_ID_P00000742</th>\n",
       "      <th>Product_ID_P00001042</th>\n",
       "      <th>Product_ID_P00001142</th>\n",
       "      <th>Product_ID_P00001242</th>\n",
       "      <th>Product_ID_P00001342</th>\n",
       "      <th>...</th>\n",
       "      <th>Product_Category_1_12</th>\n",
       "      <th>Product_Category_1_13</th>\n",
       "      <th>Product_Category_1_14</th>\n",
       "      <th>Product_Category_1_15</th>\n",
       "      <th>Product_Category_1_16</th>\n",
       "      <th>Product_Category_1_17</th>\n",
       "      <th>Product_Category_1_18</th>\n",
       "      <th>Product_Category_1_19</th>\n",
       "      <th>Product_Category_1_20</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>16429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4993</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4994</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4998 rows × 1858 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Product_ID_P00000142  Product_ID_P00000242  Product_ID_P00000342  \\\n",
       "0                      0.0                   0.0                   0.0   \n",
       "1                      0.0                   0.0                   0.0   \n",
       "2                      0.0                   0.0                   0.0   \n",
       "3                      0.0                   0.0                   0.0   \n",
       "4                      0.0                   0.0                   0.0   \n",
       "...                    ...                   ...                   ...   \n",
       "4993                   0.0                   0.0                   0.0   \n",
       "4994                   0.0                   0.0                   0.0   \n",
       "4995                   0.0                   0.0                   0.0   \n",
       "4996                   0.0                   0.0                   0.0   \n",
       "4997                   0.0                   0.0                   0.0   \n",
       "\n",
       "      Product_ID_P00000442  Product_ID_P00000642  Product_ID_P00000742  \\\n",
       "0                      0.0                   0.0                   0.0   \n",
       "1                      0.0                   0.0                   0.0   \n",
       "2                      0.0                   0.0                   0.0   \n",
       "3                      0.0                   0.0                   0.0   \n",
       "4                      0.0                   0.0                   0.0   \n",
       "...                    ...                   ...                   ...   \n",
       "4993                   0.0                   0.0                   0.0   \n",
       "4994                   0.0                   0.0                   0.0   \n",
       "4995                   0.0                   0.0                   0.0   \n",
       "4996                   0.0                   0.0                   0.0   \n",
       "4997                   0.0                   0.0                   0.0   \n",
       "\n",
       "      Product_ID_P00001042  Product_ID_P00001142  Product_ID_P00001242  \\\n",
       "0                      0.0                   0.0                   0.0   \n",
       "1                      0.0                   0.0                   0.0   \n",
       "2                      0.0                   0.0                   0.0   \n",
       "3                      0.0                   0.0                   0.0   \n",
       "4                      0.0                   0.0                   0.0   \n",
       "...                    ...                   ...                   ...   \n",
       "4993                   0.0                   0.0                   0.0   \n",
       "4994                   0.0                   0.0                   0.0   \n",
       "4995                   0.0                   0.0                   0.0   \n",
       "4996                   0.0                   0.0                   0.0   \n",
       "4997                   0.0                   0.0                   0.0   \n",
       "\n",
       "      Product_ID_P00001342  ...  Product_Category_1_12  Product_Category_1_13  \\\n",
       "0                      0.0  ...                    0.0                    0.0   \n",
       "1                      0.0  ...                    0.0                    0.0   \n",
       "2                      0.0  ...                    0.0                    0.0   \n",
       "3                      0.0  ...                    0.0                    0.0   \n",
       "4                      0.0  ...                    0.0                    0.0   \n",
       "...                    ...  ...                    ...                    ...   \n",
       "4993                   0.0  ...                    0.0                    0.0   \n",
       "4994                   0.0  ...                    0.0                    0.0   \n",
       "4995                   0.0  ...                    0.0                    0.0   \n",
       "4996                   0.0  ...                    1.0                    0.0   \n",
       "4997                   0.0  ...                    0.0                    0.0   \n",
       "\n",
       "      Product_Category_1_14  Product_Category_1_15  Product_Category_1_16  \\\n",
       "0                       0.0                    0.0                    0.0   \n",
       "1                       0.0                    0.0                    0.0   \n",
       "2                       0.0                    0.0                    0.0   \n",
       "3                       0.0                    0.0                    0.0   \n",
       "4                       0.0                    0.0                    0.0   \n",
       "...                     ...                    ...                    ...   \n",
       "4993                    0.0                    0.0                    0.0   \n",
       "4994                    0.0                    0.0                    0.0   \n",
       "4995                    0.0                    0.0                    0.0   \n",
       "4996                    0.0                    0.0                    0.0   \n",
       "4997                    0.0                    0.0                    0.0   \n",
       "\n",
       "      Product_Category_1_17  Product_Category_1_18  Product_Category_1_19  \\\n",
       "0                       0.0                    0.0                    0.0   \n",
       "1                       0.0                    0.0                    0.0   \n",
       "2                       0.0                    0.0                    0.0   \n",
       "3                       0.0                    0.0                    0.0   \n",
       "4                       0.0                    0.0                    0.0   \n",
       "...                     ...                    ...                    ...   \n",
       "4993                    0.0                    0.0                    0.0   \n",
       "4994                    0.0                    0.0                    0.0   \n",
       "4995                    0.0                    0.0                    0.0   \n",
       "4996                    0.0                    0.0                    0.0   \n",
       "4997                    0.0                    0.0                    0.0   \n",
       "\n",
       "      Product_Category_1_20  Purchase  \n",
       "0                       0.0      2010  \n",
       "1                       0.0      4483  \n",
       "2                       0.0      7696  \n",
       "3                       0.0     16429  \n",
       "4                       0.0      5780  \n",
       "...                     ...       ...  \n",
       "4993                    0.0     11701  \n",
       "4994                    0.0      2045  \n",
       "4995                    0.0      4505  \n",
       "4996                    0.0      1413  \n",
       "4997                    0.0      7182  \n",
       "\n",
       "[4998 rows x 1858 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "cat_columns = ['Product_ID', 'Gender', 'Age', 'Occupation', 'City_Category','Marital_Status', 'Stay_In_Current_City_Years', 'Product_Category_1']\n",
    "\n",
    "ohe = OneHotEncoder()\n",
    "ohe_fit = ohe.fit(df[cat_columns])\n",
    "\n",
    "ohe_encoded = ohe_fit.transform(df[cat_columns]).toarray()\n",
    "\n",
    "ohe_df = pd.DataFrame(ohe_encoded, columns = ohe_fit.get_feature_names_out(cat_columns))\n",
    "final_df = pd.concat([ohe_df, df['Purchase']], axis = 1)\n",
    "final_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. KNN 구현 & 파라미터 튜닝"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.model_selection import train_test_split, cross_validate, GridSearchCV, cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3998, 1857), (3998,), (1000, 1857), (1000,))"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 분리(train_test_split)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(final_df.iloc[:,:-1], final_df['Purchase'], test_size=0.2, random_state=0)\n",
    "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train score : 0.5323725719454702\n",
      "test score : 0.3174972511629729\n"
     ]
    }
   ],
   "source": [
    "# Knn 구현(기본값으로 k = 5 설정)\n",
    "\n",
    "knn_model = KNeighborsRegressor(n_neighbors = 5) \n",
    "knn_model.fit(X_train, y_train)\n",
    "\n",
    "print(f\"train score : {knn_model.score(X_train, y_train)}\")\n",
    "print(f\"test score : {knn_model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 하이퍼 파라미터 튜닝(1) - K 값"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_k = []\n",
    "\n",
    "for i in range(1,31) : # k값의 범위 1~30으로 제한 ++ 홀수 값만 선택\n",
    "    if i%2 != 0 :\n",
    "        list_k.append(i)\n",
    "        \n",
    "train_acc = []\n",
    "test_acc = []\n",
    "\n",
    "for k in list_k :\n",
    "    reg = KNeighborsRegressor(n_neighbors = k)\n",
    "    reg.fit(X_train, y_train)\n",
    "    train_acc.append(reg.score(X_train,y_train))\n",
    "    test_acc.append(reg.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "\n",
    "plt.subplot(2,1,1) # k값에 따른 train set의 정확도 그래프\n",
    "plt.plot(list_k, train_acc, marker = \"o\", linestyle = \"dashed\")\n",
    "plt.legend(['train scores'], fontsize = 15)\n",
    "plt.xlabel('k value', fontsize=20)\n",
    "plt.ylabel('Accuracy', fontsize=20)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.title('Accuracy Train Score', fontsize=30)\n",
    "\n",
    "plt.subplot(2,1,2) # k값에 따른 test set의 정확도 그래프\n",
    "plt.plot(list_k, test_acc, marker='o', linestyle='dashed')\n",
    "plt.legend(['test scores'], fontsize=15)\n",
    "plt.xlabel('k value', fontsize=20)\n",
    "plt.ylabel('Accuracy', fontsize=20)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.title('Accuracy Test Score', fontsize=30)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K값의 변화에 따른 train data와 test 데이터의 accuracy 값과 그래프를 나타냈다. <br>\n",
    "\n",
    "분석해봤을 때, train data는 k값이 커질 수록 accuracy는 감소하고, test data는 train과 상반된 결과를 확인할 수 있다.\n",
    "\n",
    "또한 처음에 범위를 1~30까지 진행을 했지만, 일반적으로 1~20으로 제한하는 경우가 대부분이기 때문에, 다음 튜닝부터 1~20으로 두고 계산을 하고자 한다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 하이퍼 파라미터 튜닝(2) - GridSearchCV\n",
    "\n",
    "1. K(n_neighbors) : 이웃 데이터 개수\n",
    "2. weight : 가중치 함수\n",
    "3. Distance Measure : 거리 척도 (p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 60 candidates, totalling 600 fits\n",
      "best score : 0.36022487758458344\n",
      "best test score : 0.3847625290565695\n",
      "best parameter : {'n_neighbors': 19, 'p': 1, 'weights': 'distance'}\n",
      "KNeighborsRegressor(n_neighbors=19, p=1, weights='distance')\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_n_neighbors</th>\n",
       "      <th>param_p</th>\n",
       "      <th>param_weights</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>split5_test_score</th>\n",
       "      <th>split6_test_score</th>\n",
       "      <th>split7_test_score</th>\n",
       "      <th>split8_test_score</th>\n",
       "      <th>split9_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.057269</td>\n",
       "      <td>0.008569</td>\n",
       "      <td>0.420858</td>\n",
       "      <td>0.026648</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 1, 'p': 1, 'weights': 'uniform'}</td>\n",
       "      <td>-0.259211</td>\n",
       "      <td>0.021896</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.129314</td>\n",
       "      <td>-0.125610</td>\n",
       "      <td>-0.209228</td>\n",
       "      <td>-0.331042</td>\n",
       "      <td>-0.142395</td>\n",
       "      <td>-0.193722</td>\n",
       "      <td>-0.005320</td>\n",
       "      <td>-0.144834</td>\n",
       "      <td>0.103506</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.060325</td>\n",
       "      <td>0.007089</td>\n",
       "      <td>0.424811</td>\n",
       "      <td>0.021304</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 1, 'p': 1, 'weights': 'distance'}</td>\n",
       "      <td>-0.259211</td>\n",
       "      <td>0.021896</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.129314</td>\n",
       "      <td>-0.125610</td>\n",
       "      <td>-0.209228</td>\n",
       "      <td>-0.331042</td>\n",
       "      <td>-0.142395</td>\n",
       "      <td>-0.193722</td>\n",
       "      <td>-0.005320</td>\n",
       "      <td>-0.144834</td>\n",
       "      <td>0.103506</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.063557</td>\n",
       "      <td>0.011563</td>\n",
       "      <td>0.102691</td>\n",
       "      <td>0.008131</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 1, 'p': 2, 'weights': 'uniform'}</td>\n",
       "      <td>-0.259211</td>\n",
       "      <td>0.021896</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.129314</td>\n",
       "      <td>-0.125610</td>\n",
       "      <td>-0.209228</td>\n",
       "      <td>-0.331042</td>\n",
       "      <td>-0.142395</td>\n",
       "      <td>-0.193722</td>\n",
       "      <td>-0.005320</td>\n",
       "      <td>-0.144834</td>\n",
       "      <td>0.103506</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.061458</td>\n",
       "      <td>0.005247</td>\n",
       "      <td>0.101501</td>\n",
       "      <td>0.007331</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 1, 'p': 2, 'weights': 'distance'}</td>\n",
       "      <td>-0.259211</td>\n",
       "      <td>0.021896</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.129314</td>\n",
       "      <td>-0.125610</td>\n",
       "      <td>-0.209228</td>\n",
       "      <td>-0.331042</td>\n",
       "      <td>-0.142395</td>\n",
       "      <td>-0.193722</td>\n",
       "      <td>-0.005320</td>\n",
       "      <td>-0.144834</td>\n",
       "      <td>0.103506</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.059112</td>\n",
       "      <td>0.005056</td>\n",
       "      <td>2.083627</td>\n",
       "      <td>0.059679</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 1, 'p': 3, 'weights': 'uniform'}</td>\n",
       "      <td>-0.259211</td>\n",
       "      <td>0.021896</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.129314</td>\n",
       "      <td>-0.125610</td>\n",
       "      <td>-0.209228</td>\n",
       "      <td>-0.331042</td>\n",
       "      <td>-0.142395</td>\n",
       "      <td>-0.193722</td>\n",
       "      <td>-0.005320</td>\n",
       "      <td>-0.144834</td>\n",
       "      <td>0.103506</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.061479</td>\n",
       "      <td>0.005075</td>\n",
       "      <td>2.095528</td>\n",
       "      <td>0.042133</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 1, 'p': 3, 'weights': 'distance'}</td>\n",
       "      <td>-0.259211</td>\n",
       "      <td>0.021896</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.129314</td>\n",
       "      <td>-0.125610</td>\n",
       "      <td>-0.209228</td>\n",
       "      <td>-0.331042</td>\n",
       "      <td>-0.142395</td>\n",
       "      <td>-0.193722</td>\n",
       "      <td>-0.005320</td>\n",
       "      <td>-0.144834</td>\n",
       "      <td>0.103506</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.059405</td>\n",
       "      <td>0.004868</td>\n",
       "      <td>0.439541</td>\n",
       "      <td>0.020526</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 3, 'p': 1, 'weights': 'uniform'}</td>\n",
       "      <td>0.104649</td>\n",
       "      <td>0.306832</td>\n",
       "      <td>...</td>\n",
       "      <td>0.250534</td>\n",
       "      <td>0.190145</td>\n",
       "      <td>0.186682</td>\n",
       "      <td>0.146800</td>\n",
       "      <td>0.182642</td>\n",
       "      <td>0.155871</td>\n",
       "      <td>0.313640</td>\n",
       "      <td>0.216173</td>\n",
       "      <td>0.073533</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.060182</td>\n",
       "      <td>0.006664</td>\n",
       "      <td>0.437700</td>\n",
       "      <td>0.017709</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 3, 'p': 1, 'weights': 'distance'}</td>\n",
       "      <td>0.110825</td>\n",
       "      <td>0.318265</td>\n",
       "      <td>...</td>\n",
       "      <td>0.264529</td>\n",
       "      <td>0.203657</td>\n",
       "      <td>0.198830</td>\n",
       "      <td>0.173102</td>\n",
       "      <td>0.205049</td>\n",
       "      <td>0.181049</td>\n",
       "      <td>0.331448</td>\n",
       "      <td>0.232150</td>\n",
       "      <td>0.072336</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.066507</td>\n",
       "      <td>0.007092</td>\n",
       "      <td>0.103839</td>\n",
       "      <td>0.003329</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 3, 'p': 2, 'weights': 'uniform'}</td>\n",
       "      <td>0.104649</td>\n",
       "      <td>0.306832</td>\n",
       "      <td>...</td>\n",
       "      <td>0.250534</td>\n",
       "      <td>0.190145</td>\n",
       "      <td>0.186682</td>\n",
       "      <td>0.146800</td>\n",
       "      <td>0.182642</td>\n",
       "      <td>0.155871</td>\n",
       "      <td>0.313640</td>\n",
       "      <td>0.216173</td>\n",
       "      <td>0.073533</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.063498</td>\n",
       "      <td>0.007257</td>\n",
       "      <td>0.100397</td>\n",
       "      <td>0.004077</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 3, 'p': 2, 'weights': 'distance'}</td>\n",
       "      <td>0.107399</td>\n",
       "      <td>0.314561</td>\n",
       "      <td>...</td>\n",
       "      <td>0.260436</td>\n",
       "      <td>0.198889</td>\n",
       "      <td>0.195848</td>\n",
       "      <td>0.162661</td>\n",
       "      <td>0.195913</td>\n",
       "      <td>0.172302</td>\n",
       "      <td>0.323759</td>\n",
       "      <td>0.226198</td>\n",
       "      <td>0.072771</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.059367</td>\n",
       "      <td>0.003618</td>\n",
       "      <td>2.101230</td>\n",
       "      <td>0.059260</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 3, 'p': 3, 'weights': 'uniform'}</td>\n",
       "      <td>0.104649</td>\n",
       "      <td>0.306832</td>\n",
       "      <td>...</td>\n",
       "      <td>0.250534</td>\n",
       "      <td>0.190145</td>\n",
       "      <td>0.186682</td>\n",
       "      <td>0.146800</td>\n",
       "      <td>0.182642</td>\n",
       "      <td>0.155871</td>\n",
       "      <td>0.313640</td>\n",
       "      <td>0.216173</td>\n",
       "      <td>0.073533</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.063260</td>\n",
       "      <td>0.006082</td>\n",
       "      <td>2.114553</td>\n",
       "      <td>0.125871</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 3, 'p': 3, 'weights': 'distance'}</td>\n",
       "      <td>0.105463</td>\n",
       "      <td>0.312600</td>\n",
       "      <td>...</td>\n",
       "      <td>0.257769</td>\n",
       "      <td>0.196406</td>\n",
       "      <td>0.193810</td>\n",
       "      <td>0.157935</td>\n",
       "      <td>0.192030</td>\n",
       "      <td>0.168041</td>\n",
       "      <td>0.320173</td>\n",
       "      <td>0.223201</td>\n",
       "      <td>0.072970</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.063844</td>\n",
       "      <td>0.004617</td>\n",
       "      <td>0.437346</td>\n",
       "      <td>0.021057</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 5, 'p': 1, 'weights': 'uniform'}</td>\n",
       "      <td>0.232354</td>\n",
       "      <td>0.375891</td>\n",
       "      <td>...</td>\n",
       "      <td>0.279731</td>\n",
       "      <td>0.274170</td>\n",
       "      <td>0.242693</td>\n",
       "      <td>0.285864</td>\n",
       "      <td>0.272978</td>\n",
       "      <td>0.221946</td>\n",
       "      <td>0.338934</td>\n",
       "      <td>0.285530</td>\n",
       "      <td>0.046996</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.060262</td>\n",
       "      <td>0.006182</td>\n",
       "      <td>0.433897</td>\n",
       "      <td>0.014405</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 5, 'p': 1, 'weights': 'distance'}</td>\n",
       "      <td>0.225845</td>\n",
       "      <td>0.387817</td>\n",
       "      <td>...</td>\n",
       "      <td>0.295638</td>\n",
       "      <td>0.293220</td>\n",
       "      <td>0.255924</td>\n",
       "      <td>0.301639</td>\n",
       "      <td>0.286920</td>\n",
       "      <td>0.246034</td>\n",
       "      <td>0.362914</td>\n",
       "      <td>0.300307</td>\n",
       "      <td>0.049480</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>0.008304</td>\n",
       "      <td>0.103273</td>\n",
       "      <td>0.008588</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 5, 'p': 2, 'weights': 'uniform'}</td>\n",
       "      <td>0.232354</td>\n",
       "      <td>0.375891</td>\n",
       "      <td>...</td>\n",
       "      <td>0.279731</td>\n",
       "      <td>0.274170</td>\n",
       "      <td>0.242693</td>\n",
       "      <td>0.285864</td>\n",
       "      <td>0.272978</td>\n",
       "      <td>0.221946</td>\n",
       "      <td>0.338934</td>\n",
       "      <td>0.285530</td>\n",
       "      <td>0.046996</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.060659</td>\n",
       "      <td>0.005303</td>\n",
       "      <td>0.099766</td>\n",
       "      <td>0.005857</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 5, 'p': 2, 'weights': 'distance'}</td>\n",
       "      <td>0.228000</td>\n",
       "      <td>0.381948</td>\n",
       "      <td>...</td>\n",
       "      <td>0.289539</td>\n",
       "      <td>0.284965</td>\n",
       "      <td>0.250396</td>\n",
       "      <td>0.294981</td>\n",
       "      <td>0.281383</td>\n",
       "      <td>0.235719</td>\n",
       "      <td>0.351355</td>\n",
       "      <td>0.293732</td>\n",
       "      <td>0.047924</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.064564</td>\n",
       "      <td>0.006460</td>\n",
       "      <td>2.053233</td>\n",
       "      <td>0.032081</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 5, 'p': 3, 'weights': 'uniform'}</td>\n",
       "      <td>0.232354</td>\n",
       "      <td>0.375891</td>\n",
       "      <td>...</td>\n",
       "      <td>0.279731</td>\n",
       "      <td>0.274170</td>\n",
       "      <td>0.242693</td>\n",
       "      <td>0.285864</td>\n",
       "      <td>0.272978</td>\n",
       "      <td>0.221946</td>\n",
       "      <td>0.338934</td>\n",
       "      <td>0.285530</td>\n",
       "      <td>0.046996</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.064049</td>\n",
       "      <td>0.005410</td>\n",
       "      <td>2.034231</td>\n",
       "      <td>0.022786</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 5, 'p': 3, 'weights': 'distance'}</td>\n",
       "      <td>0.228236</td>\n",
       "      <td>0.379523</td>\n",
       "      <td>...</td>\n",
       "      <td>0.286638</td>\n",
       "      <td>0.281598</td>\n",
       "      <td>0.247844</td>\n",
       "      <td>0.292167</td>\n",
       "      <td>0.279189</td>\n",
       "      <td>0.231501</td>\n",
       "      <td>0.346919</td>\n",
       "      <td>0.290915</td>\n",
       "      <td>0.047473</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.063488</td>\n",
       "      <td>0.006312</td>\n",
       "      <td>0.429880</td>\n",
       "      <td>0.017038</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 7, 'p': 1, 'weights': 'uniform'}</td>\n",
       "      <td>0.291159</td>\n",
       "      <td>0.396967</td>\n",
       "      <td>...</td>\n",
       "      <td>0.294397</td>\n",
       "      <td>0.321696</td>\n",
       "      <td>0.320073</td>\n",
       "      <td>0.299676</td>\n",
       "      <td>0.270257</td>\n",
       "      <td>0.252528</td>\n",
       "      <td>0.363914</td>\n",
       "      <td>0.316582</td>\n",
       "      <td>0.042310</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.062866</td>\n",
       "      <td>0.006556</td>\n",
       "      <td>0.441317</td>\n",
       "      <td>0.018191</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 7, 'p': 1, 'weights': 'distance'}</td>\n",
       "      <td>0.278299</td>\n",
       "      <td>0.407717</td>\n",
       "      <td>...</td>\n",
       "      <td>0.310460</td>\n",
       "      <td>0.334071</td>\n",
       "      <td>0.325266</td>\n",
       "      <td>0.322902</td>\n",
       "      <td>0.287662</td>\n",
       "      <td>0.273859</td>\n",
       "      <td>0.381707</td>\n",
       "      <td>0.329010</td>\n",
       "      <td>0.042718</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.067672</td>\n",
       "      <td>0.005137</td>\n",
       "      <td>0.101200</td>\n",
       "      <td>0.003450</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 7, 'p': 2, 'weights': 'uniform'}</td>\n",
       "      <td>0.291159</td>\n",
       "      <td>0.396967</td>\n",
       "      <td>...</td>\n",
       "      <td>0.294397</td>\n",
       "      <td>0.321696</td>\n",
       "      <td>0.320073</td>\n",
       "      <td>0.299676</td>\n",
       "      <td>0.270257</td>\n",
       "      <td>0.252528</td>\n",
       "      <td>0.363914</td>\n",
       "      <td>0.316582</td>\n",
       "      <td>0.042310</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.066129</td>\n",
       "      <td>0.004626</td>\n",
       "      <td>0.101358</td>\n",
       "      <td>0.003490</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 7, 'p': 2, 'weights': 'distance'}</td>\n",
       "      <td>0.283146</td>\n",
       "      <td>0.401494</td>\n",
       "      <td>...</td>\n",
       "      <td>0.303566</td>\n",
       "      <td>0.328664</td>\n",
       "      <td>0.324087</td>\n",
       "      <td>0.312170</td>\n",
       "      <td>0.279559</td>\n",
       "      <td>0.264109</td>\n",
       "      <td>0.372932</td>\n",
       "      <td>0.323082</td>\n",
       "      <td>0.041925</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.063233</td>\n",
       "      <td>0.005434</td>\n",
       "      <td>2.069988</td>\n",
       "      <td>0.048033</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 7, 'p': 3, 'weights': 'uniform'}</td>\n",
       "      <td>0.291159</td>\n",
       "      <td>0.396967</td>\n",
       "      <td>...</td>\n",
       "      <td>0.294397</td>\n",
       "      <td>0.321696</td>\n",
       "      <td>0.320073</td>\n",
       "      <td>0.299676</td>\n",
       "      <td>0.270257</td>\n",
       "      <td>0.252528</td>\n",
       "      <td>0.363914</td>\n",
       "      <td>0.316582</td>\n",
       "      <td>0.042310</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.061893</td>\n",
       "      <td>0.005710</td>\n",
       "      <td>2.046962</td>\n",
       "      <td>0.018968</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 7, 'p': 3, 'weights': 'distance'}</td>\n",
       "      <td>0.284451</td>\n",
       "      <td>0.399111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.300716</td>\n",
       "      <td>0.326476</td>\n",
       "      <td>0.323326</td>\n",
       "      <td>0.308148</td>\n",
       "      <td>0.276655</td>\n",
       "      <td>0.260373</td>\n",
       "      <td>0.369740</td>\n",
       "      <td>0.320721</td>\n",
       "      <td>0.041774</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.065675</td>\n",
       "      <td>0.004239</td>\n",
       "      <td>0.434826</td>\n",
       "      <td>0.019463</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 9, 'p': 1, 'weights': 'uniform'}</td>\n",
       "      <td>0.278923</td>\n",
       "      <td>0.409796</td>\n",
       "      <td>...</td>\n",
       "      <td>0.319578</td>\n",
       "      <td>0.348084</td>\n",
       "      <td>0.316400</td>\n",
       "      <td>0.314923</td>\n",
       "      <td>0.325265</td>\n",
       "      <td>0.279572</td>\n",
       "      <td>0.369898</td>\n",
       "      <td>0.330351</td>\n",
       "      <td>0.037554</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.064368</td>\n",
       "      <td>0.004851</td>\n",
       "      <td>0.434606</td>\n",
       "      <td>0.020430</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 9, 'p': 1, 'weights': 'distance'}</td>\n",
       "      <td>0.275369</td>\n",
       "      <td>0.417965</td>\n",
       "      <td>...</td>\n",
       "      <td>0.331290</td>\n",
       "      <td>0.359984</td>\n",
       "      <td>0.322459</td>\n",
       "      <td>0.336755</td>\n",
       "      <td>0.333325</td>\n",
       "      <td>0.297268</td>\n",
       "      <td>0.387553</td>\n",
       "      <td>0.341710</td>\n",
       "      <td>0.039218</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.061384</td>\n",
       "      <td>0.004764</td>\n",
       "      <td>0.103037</td>\n",
       "      <td>0.002987</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 9, 'p': 2, 'weights': 'uniform'}</td>\n",
       "      <td>0.278923</td>\n",
       "      <td>0.409796</td>\n",
       "      <td>...</td>\n",
       "      <td>0.319578</td>\n",
       "      <td>0.348084</td>\n",
       "      <td>0.316400</td>\n",
       "      <td>0.314923</td>\n",
       "      <td>0.325265</td>\n",
       "      <td>0.279572</td>\n",
       "      <td>0.369898</td>\n",
       "      <td>0.330351</td>\n",
       "      <td>0.037554</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.062924</td>\n",
       "      <td>0.007336</td>\n",
       "      <td>0.102904</td>\n",
       "      <td>0.002781</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 9, 'p': 2, 'weights': 'distance'}</td>\n",
       "      <td>0.275485</td>\n",
       "      <td>0.413573</td>\n",
       "      <td>...</td>\n",
       "      <td>0.326252</td>\n",
       "      <td>0.354532</td>\n",
       "      <td>0.320692</td>\n",
       "      <td>0.326432</td>\n",
       "      <td>0.328953</td>\n",
       "      <td>0.289292</td>\n",
       "      <td>0.378510</td>\n",
       "      <td>0.336080</td>\n",
       "      <td>0.038216</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.063463</td>\n",
       "      <td>0.002720</td>\n",
       "      <td>2.058562</td>\n",
       "      <td>0.032675</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 9, 'p': 3, 'weights': 'uniform'}</td>\n",
       "      <td>0.278923</td>\n",
       "      <td>0.409796</td>\n",
       "      <td>...</td>\n",
       "      <td>0.319578</td>\n",
       "      <td>0.348084</td>\n",
       "      <td>0.316400</td>\n",
       "      <td>0.314923</td>\n",
       "      <td>0.325265</td>\n",
       "      <td>0.279572</td>\n",
       "      <td>0.369898</td>\n",
       "      <td>0.330351</td>\n",
       "      <td>0.037554</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.060344</td>\n",
       "      <td>0.004615</td>\n",
       "      <td>2.056797</td>\n",
       "      <td>0.019010</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 9, 'p': 3, 'weights': 'distance'}</td>\n",
       "      <td>0.275232</td>\n",
       "      <td>0.411892</td>\n",
       "      <td>...</td>\n",
       "      <td>0.324172</td>\n",
       "      <td>0.352464</td>\n",
       "      <td>0.319845</td>\n",
       "      <td>0.322681</td>\n",
       "      <td>0.327388</td>\n",
       "      <td>0.286368</td>\n",
       "      <td>0.375406</td>\n",
       "      <td>0.333951</td>\n",
       "      <td>0.037967</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.062361</td>\n",
       "      <td>0.002771</td>\n",
       "      <td>0.440203</td>\n",
       "      <td>0.014516</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 11, 'p': 1, 'weights': 'uniform'}</td>\n",
       "      <td>0.301840</td>\n",
       "      <td>0.416162</td>\n",
       "      <td>...</td>\n",
       "      <td>0.360662</td>\n",
       "      <td>0.343191</td>\n",
       "      <td>0.315503</td>\n",
       "      <td>0.326682</td>\n",
       "      <td>0.331428</td>\n",
       "      <td>0.266485</td>\n",
       "      <td>0.379371</td>\n",
       "      <td>0.339683</td>\n",
       "      <td>0.039672</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.060420</td>\n",
       "      <td>0.005530</td>\n",
       "      <td>0.440661</td>\n",
       "      <td>0.009095</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 11, 'p': 1, 'weights': 'distan...</td>\n",
       "      <td>0.297206</td>\n",
       "      <td>0.423236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.368263</td>\n",
       "      <td>0.356918</td>\n",
       "      <td>0.323929</td>\n",
       "      <td>0.345750</td>\n",
       "      <td>0.338862</td>\n",
       "      <td>0.286698</td>\n",
       "      <td>0.394259</td>\n",
       "      <td>0.350354</td>\n",
       "      <td>0.039570</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.064205</td>\n",
       "      <td>0.007501</td>\n",
       "      <td>0.101754</td>\n",
       "      <td>0.005064</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 11, 'p': 2, 'weights': 'uniform'}</td>\n",
       "      <td>0.301840</td>\n",
       "      <td>0.416162</td>\n",
       "      <td>...</td>\n",
       "      <td>0.360662</td>\n",
       "      <td>0.343191</td>\n",
       "      <td>0.315503</td>\n",
       "      <td>0.326682</td>\n",
       "      <td>0.331428</td>\n",
       "      <td>0.266485</td>\n",
       "      <td>0.379371</td>\n",
       "      <td>0.339683</td>\n",
       "      <td>0.039672</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.059461</td>\n",
       "      <td>0.005098</td>\n",
       "      <td>0.102530</td>\n",
       "      <td>0.003054</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 11, 'p': 2, 'weights': 'distan...</td>\n",
       "      <td>0.297725</td>\n",
       "      <td>0.418967</td>\n",
       "      <td>...</td>\n",
       "      <td>0.364962</td>\n",
       "      <td>0.350389</td>\n",
       "      <td>0.321229</td>\n",
       "      <td>0.336368</td>\n",
       "      <td>0.335102</td>\n",
       "      <td>0.277092</td>\n",
       "      <td>0.386751</td>\n",
       "      <td>0.344928</td>\n",
       "      <td>0.039335</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.061975</td>\n",
       "      <td>0.007441</td>\n",
       "      <td>2.095907</td>\n",
       "      <td>0.025535</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 11, 'p': 3, 'weights': 'uniform'}</td>\n",
       "      <td>0.301840</td>\n",
       "      <td>0.416162</td>\n",
       "      <td>...</td>\n",
       "      <td>0.360662</td>\n",
       "      <td>0.343191</td>\n",
       "      <td>0.315503</td>\n",
       "      <td>0.326682</td>\n",
       "      <td>0.331428</td>\n",
       "      <td>0.266485</td>\n",
       "      <td>0.379371</td>\n",
       "      <td>0.339683</td>\n",
       "      <td>0.039672</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.064357</td>\n",
       "      <td>0.005654</td>\n",
       "      <td>2.083763</td>\n",
       "      <td>0.057888</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 11, 'p': 3, 'weights': 'distan...</td>\n",
       "      <td>0.297667</td>\n",
       "      <td>0.417476</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363607</td>\n",
       "      <td>0.348036</td>\n",
       "      <td>0.320099</td>\n",
       "      <td>0.333137</td>\n",
       "      <td>0.333825</td>\n",
       "      <td>0.273693</td>\n",
       "      <td>0.384257</td>\n",
       "      <td>0.342971</td>\n",
       "      <td>0.039352</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.061765</td>\n",
       "      <td>0.005625</td>\n",
       "      <td>0.460317</td>\n",
       "      <td>0.029786</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 13, 'p': 1, 'weights': 'uniform'}</td>\n",
       "      <td>0.298986</td>\n",
       "      <td>0.412389</td>\n",
       "      <td>...</td>\n",
       "      <td>0.337130</td>\n",
       "      <td>0.334890</td>\n",
       "      <td>0.344559</td>\n",
       "      <td>0.312110</td>\n",
       "      <td>0.355341</td>\n",
       "      <td>0.290012</td>\n",
       "      <td>0.375809</td>\n",
       "      <td>0.342536</td>\n",
       "      <td>0.035057</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.061129</td>\n",
       "      <td>0.006636</td>\n",
       "      <td>0.442265</td>\n",
       "      <td>0.014847</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 13, 'p': 1, 'weights': 'distan...</td>\n",
       "      <td>0.297834</td>\n",
       "      <td>0.421556</td>\n",
       "      <td>...</td>\n",
       "      <td>0.350792</td>\n",
       "      <td>0.349452</td>\n",
       "      <td>0.350221</td>\n",
       "      <td>0.334100</td>\n",
       "      <td>0.359736</td>\n",
       "      <td>0.307918</td>\n",
       "      <td>0.391384</td>\n",
       "      <td>0.353750</td>\n",
       "      <td>0.034833</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.064506</td>\n",
       "      <td>0.004357</td>\n",
       "      <td>0.101450</td>\n",
       "      <td>0.003358</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 13, 'p': 2, 'weights': 'uniform'}</td>\n",
       "      <td>0.298986</td>\n",
       "      <td>0.412389</td>\n",
       "      <td>...</td>\n",
       "      <td>0.337130</td>\n",
       "      <td>0.334890</td>\n",
       "      <td>0.344559</td>\n",
       "      <td>0.312110</td>\n",
       "      <td>0.355341</td>\n",
       "      <td>0.290012</td>\n",
       "      <td>0.375809</td>\n",
       "      <td>0.342536</td>\n",
       "      <td>0.035057</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.058982</td>\n",
       "      <td>0.007077</td>\n",
       "      <td>0.097296</td>\n",
       "      <td>0.003693</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 13, 'p': 2, 'weights': 'distan...</td>\n",
       "      <td>0.296606</td>\n",
       "      <td>0.416150</td>\n",
       "      <td>...</td>\n",
       "      <td>0.344351</td>\n",
       "      <td>0.342270</td>\n",
       "      <td>0.348511</td>\n",
       "      <td>0.322932</td>\n",
       "      <td>0.357811</td>\n",
       "      <td>0.299412</td>\n",
       "      <td>0.383790</td>\n",
       "      <td>0.347961</td>\n",
       "      <td>0.034770</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.062410</td>\n",
       "      <td>0.005854</td>\n",
       "      <td>2.116790</td>\n",
       "      <td>0.053013</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 13, 'p': 3, 'weights': 'uniform'}</td>\n",
       "      <td>0.298986</td>\n",
       "      <td>0.412389</td>\n",
       "      <td>...</td>\n",
       "      <td>0.337130</td>\n",
       "      <td>0.334890</td>\n",
       "      <td>0.344559</td>\n",
       "      <td>0.312110</td>\n",
       "      <td>0.355341</td>\n",
       "      <td>0.290012</td>\n",
       "      <td>0.375809</td>\n",
       "      <td>0.342536</td>\n",
       "      <td>0.035057</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.063771</td>\n",
       "      <td>0.007513</td>\n",
       "      <td>2.094942</td>\n",
       "      <td>0.073213</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 13, 'p': 3, 'weights': 'distan...</td>\n",
       "      <td>0.295983</td>\n",
       "      <td>0.414324</td>\n",
       "      <td>...</td>\n",
       "      <td>0.342000</td>\n",
       "      <td>0.339809</td>\n",
       "      <td>0.347772</td>\n",
       "      <td>0.319253</td>\n",
       "      <td>0.357186</td>\n",
       "      <td>0.296491</td>\n",
       "      <td>0.381306</td>\n",
       "      <td>0.345958</td>\n",
       "      <td>0.034848</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.063189</td>\n",
       "      <td>0.006313</td>\n",
       "      <td>0.445448</td>\n",
       "      <td>0.028125</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 15, 'p': 1, 'weights': 'uniform'}</td>\n",
       "      <td>0.307148</td>\n",
       "      <td>0.419131</td>\n",
       "      <td>...</td>\n",
       "      <td>0.355319</td>\n",
       "      <td>0.339375</td>\n",
       "      <td>0.355621</td>\n",
       "      <td>0.326344</td>\n",
       "      <td>0.350207</td>\n",
       "      <td>0.291383</td>\n",
       "      <td>0.354950</td>\n",
       "      <td>0.346157</td>\n",
       "      <td>0.032874</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.063672</td>\n",
       "      <td>0.004989</td>\n",
       "      <td>0.465162</td>\n",
       "      <td>0.021257</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 15, 'p': 1, 'weights': 'distan...</td>\n",
       "      <td>0.304904</td>\n",
       "      <td>0.427399</td>\n",
       "      <td>...</td>\n",
       "      <td>0.365021</td>\n",
       "      <td>0.353401</td>\n",
       "      <td>0.362457</td>\n",
       "      <td>0.345694</td>\n",
       "      <td>0.355482</td>\n",
       "      <td>0.309656</td>\n",
       "      <td>0.374831</td>\n",
       "      <td>0.357256</td>\n",
       "      <td>0.032780</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.062696</td>\n",
       "      <td>0.004799</td>\n",
       "      <td>0.105371</td>\n",
       "      <td>0.003810</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 15, 'p': 2, 'weights': 'uniform'}</td>\n",
       "      <td>0.307148</td>\n",
       "      <td>0.419131</td>\n",
       "      <td>...</td>\n",
       "      <td>0.355319</td>\n",
       "      <td>0.339375</td>\n",
       "      <td>0.355621</td>\n",
       "      <td>0.326344</td>\n",
       "      <td>0.350207</td>\n",
       "      <td>0.291383</td>\n",
       "      <td>0.354950</td>\n",
       "      <td>0.346157</td>\n",
       "      <td>0.032874</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.061148</td>\n",
       "      <td>0.005207</td>\n",
       "      <td>0.105211</td>\n",
       "      <td>0.004196</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 15, 'p': 2, 'weights': 'distan...</td>\n",
       "      <td>0.304133</td>\n",
       "      <td>0.422626</td>\n",
       "      <td>...</td>\n",
       "      <td>0.360367</td>\n",
       "      <td>0.346312</td>\n",
       "      <td>0.360187</td>\n",
       "      <td>0.335729</td>\n",
       "      <td>0.353283</td>\n",
       "      <td>0.300845</td>\n",
       "      <td>0.365460</td>\n",
       "      <td>0.351518</td>\n",
       "      <td>0.032731</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.062799</td>\n",
       "      <td>0.004495</td>\n",
       "      <td>2.140977</td>\n",
       "      <td>0.060701</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 15, 'p': 3, 'weights': 'uniform'}</td>\n",
       "      <td>0.307148</td>\n",
       "      <td>0.419131</td>\n",
       "      <td>...</td>\n",
       "      <td>0.355319</td>\n",
       "      <td>0.339375</td>\n",
       "      <td>0.355621</td>\n",
       "      <td>0.326344</td>\n",
       "      <td>0.350207</td>\n",
       "      <td>0.291383</td>\n",
       "      <td>0.354950</td>\n",
       "      <td>0.346157</td>\n",
       "      <td>0.032874</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.063692</td>\n",
       "      <td>0.004204</td>\n",
       "      <td>2.106110</td>\n",
       "      <td>0.046579</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 15, 'p': 3, 'weights': 'distan...</td>\n",
       "      <td>0.303695</td>\n",
       "      <td>0.421079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.358701</td>\n",
       "      <td>0.343964</td>\n",
       "      <td>0.359275</td>\n",
       "      <td>0.332512</td>\n",
       "      <td>0.352602</td>\n",
       "      <td>0.297859</td>\n",
       "      <td>0.362426</td>\n",
       "      <td>0.349584</td>\n",
       "      <td>0.032813</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.058940</td>\n",
       "      <td>0.004311</td>\n",
       "      <td>0.433495</td>\n",
       "      <td>0.012284</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 17, 'p': 1, 'weights': 'uniform'}</td>\n",
       "      <td>0.297760</td>\n",
       "      <td>0.415456</td>\n",
       "      <td>...</td>\n",
       "      <td>0.344409</td>\n",
       "      <td>0.345739</td>\n",
       "      <td>0.360064</td>\n",
       "      <td>0.326790</td>\n",
       "      <td>0.343762</td>\n",
       "      <td>0.312135</td>\n",
       "      <td>0.352352</td>\n",
       "      <td>0.347882</td>\n",
       "      <td>0.031714</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.062557</td>\n",
       "      <td>0.005165</td>\n",
       "      <td>0.434494</td>\n",
       "      <td>0.018151</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 17, 'p': 1, 'weights': 'distan...</td>\n",
       "      <td>0.299378</td>\n",
       "      <td>0.424917</td>\n",
       "      <td>...</td>\n",
       "      <td>0.355840</td>\n",
       "      <td>0.358079</td>\n",
       "      <td>0.365477</td>\n",
       "      <td>0.345908</td>\n",
       "      <td>0.351045</td>\n",
       "      <td>0.326578</td>\n",
       "      <td>0.371293</td>\n",
       "      <td>0.358712</td>\n",
       "      <td>0.032039</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.065176</td>\n",
       "      <td>0.005818</td>\n",
       "      <td>0.096697</td>\n",
       "      <td>0.005587</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 17, 'p': 2, 'weights': 'uniform'}</td>\n",
       "      <td>0.297760</td>\n",
       "      <td>0.415456</td>\n",
       "      <td>...</td>\n",
       "      <td>0.344409</td>\n",
       "      <td>0.345739</td>\n",
       "      <td>0.360064</td>\n",
       "      <td>0.326790</td>\n",
       "      <td>0.343762</td>\n",
       "      <td>0.312135</td>\n",
       "      <td>0.352352</td>\n",
       "      <td>0.347882</td>\n",
       "      <td>0.031714</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.060282</td>\n",
       "      <td>0.005102</td>\n",
       "      <td>0.104028</td>\n",
       "      <td>0.004055</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 17, 'p': 2, 'weights': 'distan...</td>\n",
       "      <td>0.296583</td>\n",
       "      <td>0.419510</td>\n",
       "      <td>...</td>\n",
       "      <td>0.350214</td>\n",
       "      <td>0.351788</td>\n",
       "      <td>0.363555</td>\n",
       "      <td>0.336013</td>\n",
       "      <td>0.348103</td>\n",
       "      <td>0.319582</td>\n",
       "      <td>0.361780</td>\n",
       "      <td>0.352998</td>\n",
       "      <td>0.031820</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.060799</td>\n",
       "      <td>0.004229</td>\n",
       "      <td>2.101888</td>\n",
       "      <td>0.030839</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 17, 'p': 3, 'weights': 'uniform'}</td>\n",
       "      <td>0.297760</td>\n",
       "      <td>0.415456</td>\n",
       "      <td>...</td>\n",
       "      <td>0.344409</td>\n",
       "      <td>0.345739</td>\n",
       "      <td>0.360064</td>\n",
       "      <td>0.326790</td>\n",
       "      <td>0.343762</td>\n",
       "      <td>0.312135</td>\n",
       "      <td>0.352352</td>\n",
       "      <td>0.347882</td>\n",
       "      <td>0.031714</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.059861</td>\n",
       "      <td>0.006150</td>\n",
       "      <td>2.104696</td>\n",
       "      <td>0.039006</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 17, 'p': 3, 'weights': 'distan...</td>\n",
       "      <td>0.295502</td>\n",
       "      <td>0.417735</td>\n",
       "      <td>...</td>\n",
       "      <td>0.348274</td>\n",
       "      <td>0.349730</td>\n",
       "      <td>0.362845</td>\n",
       "      <td>0.332848</td>\n",
       "      <td>0.347203</td>\n",
       "      <td>0.317225</td>\n",
       "      <td>0.358672</td>\n",
       "      <td>0.351095</td>\n",
       "      <td>0.031814</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0.062315</td>\n",
       "      <td>0.004585</td>\n",
       "      <td>0.434447</td>\n",
       "      <td>0.021116</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 19, 'p': 1, 'weights': 'uniform'}</td>\n",
       "      <td>0.304402</td>\n",
       "      <td>0.423559</td>\n",
       "      <td>...</td>\n",
       "      <td>0.355508</td>\n",
       "      <td>0.342567</td>\n",
       "      <td>0.367152</td>\n",
       "      <td>0.341182</td>\n",
       "      <td>0.338117</td>\n",
       "      <td>0.316882</td>\n",
       "      <td>0.347100</td>\n",
       "      <td>0.348963</td>\n",
       "      <td>0.030321</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.061513</td>\n",
       "      <td>0.006239</td>\n",
       "      <td>0.428713</td>\n",
       "      <td>0.016682</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 19, 'p': 1, 'weights': 'distan...</td>\n",
       "      <td>0.303624</td>\n",
       "      <td>0.432351</td>\n",
       "      <td>...</td>\n",
       "      <td>0.366071</td>\n",
       "      <td>0.357205</td>\n",
       "      <td>0.372621</td>\n",
       "      <td>0.358487</td>\n",
       "      <td>0.347291</td>\n",
       "      <td>0.329078</td>\n",
       "      <td>0.369219</td>\n",
       "      <td>0.360225</td>\n",
       "      <td>0.031402</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.062999</td>\n",
       "      <td>0.006467</td>\n",
       "      <td>0.100507</td>\n",
       "      <td>0.006500</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 19, 'p': 2, 'weights': 'uniform'}</td>\n",
       "      <td>0.304402</td>\n",
       "      <td>0.423559</td>\n",
       "      <td>...</td>\n",
       "      <td>0.355508</td>\n",
       "      <td>0.342567</td>\n",
       "      <td>0.367152</td>\n",
       "      <td>0.341182</td>\n",
       "      <td>0.338117</td>\n",
       "      <td>0.316882</td>\n",
       "      <td>0.347100</td>\n",
       "      <td>0.348963</td>\n",
       "      <td>0.030321</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.061858</td>\n",
       "      <td>0.006112</td>\n",
       "      <td>0.102360</td>\n",
       "      <td>0.003964</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 19, 'p': 2, 'weights': 'distan...</td>\n",
       "      <td>0.301832</td>\n",
       "      <td>0.427833</td>\n",
       "      <td>...</td>\n",
       "      <td>0.360922</td>\n",
       "      <td>0.349801</td>\n",
       "      <td>0.371041</td>\n",
       "      <td>0.349401</td>\n",
       "      <td>0.343721</td>\n",
       "      <td>0.322735</td>\n",
       "      <td>0.359129</td>\n",
       "      <td>0.354444</td>\n",
       "      <td>0.031103</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0.062260</td>\n",
       "      <td>0.005344</td>\n",
       "      <td>2.140215</td>\n",
       "      <td>0.055445</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'n_neighbors': 19, 'p': 3, 'weights': 'uniform'}</td>\n",
       "      <td>0.304402</td>\n",
       "      <td>0.423559</td>\n",
       "      <td>...</td>\n",
       "      <td>0.355508</td>\n",
       "      <td>0.342567</td>\n",
       "      <td>0.367152</td>\n",
       "      <td>0.341182</td>\n",
       "      <td>0.338117</td>\n",
       "      <td>0.316882</td>\n",
       "      <td>0.347100</td>\n",
       "      <td>0.348963</td>\n",
       "      <td>0.030321</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.063367</td>\n",
       "      <td>0.005247</td>\n",
       "      <td>2.111956</td>\n",
       "      <td>0.076704</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'n_neighbors': 19, 'p': 3, 'weights': 'distan...</td>\n",
       "      <td>0.301175</td>\n",
       "      <td>0.426388</td>\n",
       "      <td>...</td>\n",
       "      <td>0.359127</td>\n",
       "      <td>0.347362</td>\n",
       "      <td>0.370418</td>\n",
       "      <td>0.346552</td>\n",
       "      <td>0.342602</td>\n",
       "      <td>0.320683</td>\n",
       "      <td>0.355851</td>\n",
       "      <td>0.352543</td>\n",
       "      <td>0.031064</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.057269      0.008569         0.420858        0.026648   \n",
       "1        0.060325      0.007089         0.424811        0.021304   \n",
       "2        0.063557      0.011563         0.102691        0.008131   \n",
       "3        0.061458      0.005247         0.101501        0.007331   \n",
       "4        0.059112      0.005056         2.083627        0.059679   \n",
       "5        0.061479      0.005075         2.095528        0.042133   \n",
       "6        0.059405      0.004868         0.439541        0.020526   \n",
       "7        0.060182      0.006664         0.437700        0.017709   \n",
       "8        0.066507      0.007092         0.103839        0.003329   \n",
       "9        0.063498      0.007257         0.100397        0.004077   \n",
       "10       0.059367      0.003618         2.101230        0.059260   \n",
       "11       0.063260      0.006082         2.114553        0.125871   \n",
       "12       0.063844      0.004617         0.437346        0.021057   \n",
       "13       0.060262      0.006182         0.433897        0.014405   \n",
       "14       0.065255      0.008304         0.103273        0.008588   \n",
       "15       0.060659      0.005303         0.099766        0.005857   \n",
       "16       0.064564      0.006460         2.053233        0.032081   \n",
       "17       0.064049      0.005410         2.034231        0.022786   \n",
       "18       0.063488      0.006312         0.429880        0.017038   \n",
       "19       0.062866      0.006556         0.441317        0.018191   \n",
       "20       0.067672      0.005137         0.101200        0.003450   \n",
       "21       0.066129      0.004626         0.101358        0.003490   \n",
       "22       0.063233      0.005434         2.069988        0.048033   \n",
       "23       0.061893      0.005710         2.046962        0.018968   \n",
       "24       0.065675      0.004239         0.434826        0.019463   \n",
       "25       0.064368      0.004851         0.434606        0.020430   \n",
       "26       0.061384      0.004764         0.103037        0.002987   \n",
       "27       0.062924      0.007336         0.102904        0.002781   \n",
       "28       0.063463      0.002720         2.058562        0.032675   \n",
       "29       0.060344      0.004615         2.056797        0.019010   \n",
       "30       0.062361      0.002771         0.440203        0.014516   \n",
       "31       0.060420      0.005530         0.440661        0.009095   \n",
       "32       0.064205      0.007501         0.101754        0.005064   \n",
       "33       0.059461      0.005098         0.102530        0.003054   \n",
       "34       0.061975      0.007441         2.095907        0.025535   \n",
       "35       0.064357      0.005654         2.083763        0.057888   \n",
       "36       0.061765      0.005625         0.460317        0.029786   \n",
       "37       0.061129      0.006636         0.442265        0.014847   \n",
       "38       0.064506      0.004357         0.101450        0.003358   \n",
       "39       0.058982      0.007077         0.097296        0.003693   \n",
       "40       0.062410      0.005854         2.116790        0.053013   \n",
       "41       0.063771      0.007513         2.094942        0.073213   \n",
       "42       0.063189      0.006313         0.445448        0.028125   \n",
       "43       0.063672      0.004989         0.465162        0.021257   \n",
       "44       0.062696      0.004799         0.105371        0.003810   \n",
       "45       0.061148      0.005207         0.105211        0.004196   \n",
       "46       0.062799      0.004495         2.140977        0.060701   \n",
       "47       0.063692      0.004204         2.106110        0.046579   \n",
       "48       0.058940      0.004311         0.433495        0.012284   \n",
       "49       0.062557      0.005165         0.434494        0.018151   \n",
       "50       0.065176      0.005818         0.096697        0.005587   \n",
       "51       0.060282      0.005102         0.104028        0.004055   \n",
       "52       0.060799      0.004229         2.101888        0.030839   \n",
       "53       0.059861      0.006150         2.104696        0.039006   \n",
       "54       0.062315      0.004585         0.434447        0.021116   \n",
       "55       0.061513      0.006239         0.428713        0.016682   \n",
       "56       0.062999      0.006467         0.100507        0.006500   \n",
       "57       0.061858      0.006112         0.102360        0.003964   \n",
       "58       0.062260      0.005344         2.140215        0.055445   \n",
       "59       0.063367      0.005247         2.111956        0.076704   \n",
       "\n",
       "   param_n_neighbors param_p param_weights  \\\n",
       "0                  1       1       uniform   \n",
       "1                  1       1      distance   \n",
       "2                  1       2       uniform   \n",
       "3                  1       2      distance   \n",
       "4                  1       3       uniform   \n",
       "5                  1       3      distance   \n",
       "6                  3       1       uniform   \n",
       "7                  3       1      distance   \n",
       "8                  3       2       uniform   \n",
       "9                  3       2      distance   \n",
       "10                 3       3       uniform   \n",
       "11                 3       3      distance   \n",
       "12                 5       1       uniform   \n",
       "13                 5       1      distance   \n",
       "14                 5       2       uniform   \n",
       "15                 5       2      distance   \n",
       "16                 5       3       uniform   \n",
       "17                 5       3      distance   \n",
       "18                 7       1       uniform   \n",
       "19                 7       1      distance   \n",
       "20                 7       2       uniform   \n",
       "21                 7       2      distance   \n",
       "22                 7       3       uniform   \n",
       "23                 7       3      distance   \n",
       "24                 9       1       uniform   \n",
       "25                 9       1      distance   \n",
       "26                 9       2       uniform   \n",
       "27                 9       2      distance   \n",
       "28                 9       3       uniform   \n",
       "29                 9       3      distance   \n",
       "30                11       1       uniform   \n",
       "31                11       1      distance   \n",
       "32                11       2       uniform   \n",
       "33                11       2      distance   \n",
       "34                11       3       uniform   \n",
       "35                11       3      distance   \n",
       "36                13       1       uniform   \n",
       "37                13       1      distance   \n",
       "38                13       2       uniform   \n",
       "39                13       2      distance   \n",
       "40                13       3       uniform   \n",
       "41                13       3      distance   \n",
       "42                15       1       uniform   \n",
       "43                15       1      distance   \n",
       "44                15       2       uniform   \n",
       "45                15       2      distance   \n",
       "46                15       3       uniform   \n",
       "47                15       3      distance   \n",
       "48                17       1       uniform   \n",
       "49                17       1      distance   \n",
       "50                17       2       uniform   \n",
       "51                17       2      distance   \n",
       "52                17       3       uniform   \n",
       "53                17       3      distance   \n",
       "54                19       1       uniform   \n",
       "55                19       1      distance   \n",
       "56                19       2       uniform   \n",
       "57                19       2      distance   \n",
       "58                19       3       uniform   \n",
       "59                19       3      distance   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0    {'n_neighbors': 1, 'p': 1, 'weights': 'uniform'}          -0.259211   \n",
       "1   {'n_neighbors': 1, 'p': 1, 'weights': 'distance'}          -0.259211   \n",
       "2    {'n_neighbors': 1, 'p': 2, 'weights': 'uniform'}          -0.259211   \n",
       "3   {'n_neighbors': 1, 'p': 2, 'weights': 'distance'}          -0.259211   \n",
       "4    {'n_neighbors': 1, 'p': 3, 'weights': 'uniform'}          -0.259211   \n",
       "5   {'n_neighbors': 1, 'p': 3, 'weights': 'distance'}          -0.259211   \n",
       "6    {'n_neighbors': 3, 'p': 1, 'weights': 'uniform'}           0.104649   \n",
       "7   {'n_neighbors': 3, 'p': 1, 'weights': 'distance'}           0.110825   \n",
       "8    {'n_neighbors': 3, 'p': 2, 'weights': 'uniform'}           0.104649   \n",
       "9   {'n_neighbors': 3, 'p': 2, 'weights': 'distance'}           0.107399   \n",
       "10   {'n_neighbors': 3, 'p': 3, 'weights': 'uniform'}           0.104649   \n",
       "11  {'n_neighbors': 3, 'p': 3, 'weights': 'distance'}           0.105463   \n",
       "12   {'n_neighbors': 5, 'p': 1, 'weights': 'uniform'}           0.232354   \n",
       "13  {'n_neighbors': 5, 'p': 1, 'weights': 'distance'}           0.225845   \n",
       "14   {'n_neighbors': 5, 'p': 2, 'weights': 'uniform'}           0.232354   \n",
       "15  {'n_neighbors': 5, 'p': 2, 'weights': 'distance'}           0.228000   \n",
       "16   {'n_neighbors': 5, 'p': 3, 'weights': 'uniform'}           0.232354   \n",
       "17  {'n_neighbors': 5, 'p': 3, 'weights': 'distance'}           0.228236   \n",
       "18   {'n_neighbors': 7, 'p': 1, 'weights': 'uniform'}           0.291159   \n",
       "19  {'n_neighbors': 7, 'p': 1, 'weights': 'distance'}           0.278299   \n",
       "20   {'n_neighbors': 7, 'p': 2, 'weights': 'uniform'}           0.291159   \n",
       "21  {'n_neighbors': 7, 'p': 2, 'weights': 'distance'}           0.283146   \n",
       "22   {'n_neighbors': 7, 'p': 3, 'weights': 'uniform'}           0.291159   \n",
       "23  {'n_neighbors': 7, 'p': 3, 'weights': 'distance'}           0.284451   \n",
       "24   {'n_neighbors': 9, 'p': 1, 'weights': 'uniform'}           0.278923   \n",
       "25  {'n_neighbors': 9, 'p': 1, 'weights': 'distance'}           0.275369   \n",
       "26   {'n_neighbors': 9, 'p': 2, 'weights': 'uniform'}           0.278923   \n",
       "27  {'n_neighbors': 9, 'p': 2, 'weights': 'distance'}           0.275485   \n",
       "28   {'n_neighbors': 9, 'p': 3, 'weights': 'uniform'}           0.278923   \n",
       "29  {'n_neighbors': 9, 'p': 3, 'weights': 'distance'}           0.275232   \n",
       "30  {'n_neighbors': 11, 'p': 1, 'weights': 'uniform'}           0.301840   \n",
       "31  {'n_neighbors': 11, 'p': 1, 'weights': 'distan...           0.297206   \n",
       "32  {'n_neighbors': 11, 'p': 2, 'weights': 'uniform'}           0.301840   \n",
       "33  {'n_neighbors': 11, 'p': 2, 'weights': 'distan...           0.297725   \n",
       "34  {'n_neighbors': 11, 'p': 3, 'weights': 'uniform'}           0.301840   \n",
       "35  {'n_neighbors': 11, 'p': 3, 'weights': 'distan...           0.297667   \n",
       "36  {'n_neighbors': 13, 'p': 1, 'weights': 'uniform'}           0.298986   \n",
       "37  {'n_neighbors': 13, 'p': 1, 'weights': 'distan...           0.297834   \n",
       "38  {'n_neighbors': 13, 'p': 2, 'weights': 'uniform'}           0.298986   \n",
       "39  {'n_neighbors': 13, 'p': 2, 'weights': 'distan...           0.296606   \n",
       "40  {'n_neighbors': 13, 'p': 3, 'weights': 'uniform'}           0.298986   \n",
       "41  {'n_neighbors': 13, 'p': 3, 'weights': 'distan...           0.295983   \n",
       "42  {'n_neighbors': 15, 'p': 1, 'weights': 'uniform'}           0.307148   \n",
       "43  {'n_neighbors': 15, 'p': 1, 'weights': 'distan...           0.304904   \n",
       "44  {'n_neighbors': 15, 'p': 2, 'weights': 'uniform'}           0.307148   \n",
       "45  {'n_neighbors': 15, 'p': 2, 'weights': 'distan...           0.304133   \n",
       "46  {'n_neighbors': 15, 'p': 3, 'weights': 'uniform'}           0.307148   \n",
       "47  {'n_neighbors': 15, 'p': 3, 'weights': 'distan...           0.303695   \n",
       "48  {'n_neighbors': 17, 'p': 1, 'weights': 'uniform'}           0.297760   \n",
       "49  {'n_neighbors': 17, 'p': 1, 'weights': 'distan...           0.299378   \n",
       "50  {'n_neighbors': 17, 'p': 2, 'weights': 'uniform'}           0.297760   \n",
       "51  {'n_neighbors': 17, 'p': 2, 'weights': 'distan...           0.296583   \n",
       "52  {'n_neighbors': 17, 'p': 3, 'weights': 'uniform'}           0.297760   \n",
       "53  {'n_neighbors': 17, 'p': 3, 'weights': 'distan...           0.295502   \n",
       "54  {'n_neighbors': 19, 'p': 1, 'weights': 'uniform'}           0.304402   \n",
       "55  {'n_neighbors': 19, 'p': 1, 'weights': 'distan...           0.303624   \n",
       "56  {'n_neighbors': 19, 'p': 2, 'weights': 'uniform'}           0.304402   \n",
       "57  {'n_neighbors': 19, 'p': 2, 'weights': 'distan...           0.301832   \n",
       "58  {'n_neighbors': 19, 'p': 3, 'weights': 'uniform'}           0.304402   \n",
       "59  {'n_neighbors': 19, 'p': 3, 'weights': 'distan...           0.301175   \n",
       "\n",
       "    split1_test_score  ...  split3_test_score  split4_test_score  \\\n",
       "0            0.021896  ...          -0.129314          -0.125610   \n",
       "1            0.021896  ...          -0.129314          -0.125610   \n",
       "2            0.021896  ...          -0.129314          -0.125610   \n",
       "3            0.021896  ...          -0.129314          -0.125610   \n",
       "4            0.021896  ...          -0.129314          -0.125610   \n",
       "5            0.021896  ...          -0.129314          -0.125610   \n",
       "6            0.306832  ...           0.250534           0.190145   \n",
       "7            0.318265  ...           0.264529           0.203657   \n",
       "8            0.306832  ...           0.250534           0.190145   \n",
       "9            0.314561  ...           0.260436           0.198889   \n",
       "10           0.306832  ...           0.250534           0.190145   \n",
       "11           0.312600  ...           0.257769           0.196406   \n",
       "12           0.375891  ...           0.279731           0.274170   \n",
       "13           0.387817  ...           0.295638           0.293220   \n",
       "14           0.375891  ...           0.279731           0.274170   \n",
       "15           0.381948  ...           0.289539           0.284965   \n",
       "16           0.375891  ...           0.279731           0.274170   \n",
       "17           0.379523  ...           0.286638           0.281598   \n",
       "18           0.396967  ...           0.294397           0.321696   \n",
       "19           0.407717  ...           0.310460           0.334071   \n",
       "20           0.396967  ...           0.294397           0.321696   \n",
       "21           0.401494  ...           0.303566           0.328664   \n",
       "22           0.396967  ...           0.294397           0.321696   \n",
       "23           0.399111  ...           0.300716           0.326476   \n",
       "24           0.409796  ...           0.319578           0.348084   \n",
       "25           0.417965  ...           0.331290           0.359984   \n",
       "26           0.409796  ...           0.319578           0.348084   \n",
       "27           0.413573  ...           0.326252           0.354532   \n",
       "28           0.409796  ...           0.319578           0.348084   \n",
       "29           0.411892  ...           0.324172           0.352464   \n",
       "30           0.416162  ...           0.360662           0.343191   \n",
       "31           0.423236  ...           0.368263           0.356918   \n",
       "32           0.416162  ...           0.360662           0.343191   \n",
       "33           0.418967  ...           0.364962           0.350389   \n",
       "34           0.416162  ...           0.360662           0.343191   \n",
       "35           0.417476  ...           0.363607           0.348036   \n",
       "36           0.412389  ...           0.337130           0.334890   \n",
       "37           0.421556  ...           0.350792           0.349452   \n",
       "38           0.412389  ...           0.337130           0.334890   \n",
       "39           0.416150  ...           0.344351           0.342270   \n",
       "40           0.412389  ...           0.337130           0.334890   \n",
       "41           0.414324  ...           0.342000           0.339809   \n",
       "42           0.419131  ...           0.355319           0.339375   \n",
       "43           0.427399  ...           0.365021           0.353401   \n",
       "44           0.419131  ...           0.355319           0.339375   \n",
       "45           0.422626  ...           0.360367           0.346312   \n",
       "46           0.419131  ...           0.355319           0.339375   \n",
       "47           0.421079  ...           0.358701           0.343964   \n",
       "48           0.415456  ...           0.344409           0.345739   \n",
       "49           0.424917  ...           0.355840           0.358079   \n",
       "50           0.415456  ...           0.344409           0.345739   \n",
       "51           0.419510  ...           0.350214           0.351788   \n",
       "52           0.415456  ...           0.344409           0.345739   \n",
       "53           0.417735  ...           0.348274           0.349730   \n",
       "54           0.423559  ...           0.355508           0.342567   \n",
       "55           0.432351  ...           0.366071           0.357205   \n",
       "56           0.423559  ...           0.355508           0.342567   \n",
       "57           0.427833  ...           0.360922           0.349801   \n",
       "58           0.423559  ...           0.355508           0.342567   \n",
       "59           0.426388  ...           0.359127           0.347362   \n",
       "\n",
       "    split5_test_score  split6_test_score  split7_test_score  \\\n",
       "0           -0.209228          -0.331042          -0.142395   \n",
       "1           -0.209228          -0.331042          -0.142395   \n",
       "2           -0.209228          -0.331042          -0.142395   \n",
       "3           -0.209228          -0.331042          -0.142395   \n",
       "4           -0.209228          -0.331042          -0.142395   \n",
       "5           -0.209228          -0.331042          -0.142395   \n",
       "6            0.186682           0.146800           0.182642   \n",
       "7            0.198830           0.173102           0.205049   \n",
       "8            0.186682           0.146800           0.182642   \n",
       "9            0.195848           0.162661           0.195913   \n",
       "10           0.186682           0.146800           0.182642   \n",
       "11           0.193810           0.157935           0.192030   \n",
       "12           0.242693           0.285864           0.272978   \n",
       "13           0.255924           0.301639           0.286920   \n",
       "14           0.242693           0.285864           0.272978   \n",
       "15           0.250396           0.294981           0.281383   \n",
       "16           0.242693           0.285864           0.272978   \n",
       "17           0.247844           0.292167           0.279189   \n",
       "18           0.320073           0.299676           0.270257   \n",
       "19           0.325266           0.322902           0.287662   \n",
       "20           0.320073           0.299676           0.270257   \n",
       "21           0.324087           0.312170           0.279559   \n",
       "22           0.320073           0.299676           0.270257   \n",
       "23           0.323326           0.308148           0.276655   \n",
       "24           0.316400           0.314923           0.325265   \n",
       "25           0.322459           0.336755           0.333325   \n",
       "26           0.316400           0.314923           0.325265   \n",
       "27           0.320692           0.326432           0.328953   \n",
       "28           0.316400           0.314923           0.325265   \n",
       "29           0.319845           0.322681           0.327388   \n",
       "30           0.315503           0.326682           0.331428   \n",
       "31           0.323929           0.345750           0.338862   \n",
       "32           0.315503           0.326682           0.331428   \n",
       "33           0.321229           0.336368           0.335102   \n",
       "34           0.315503           0.326682           0.331428   \n",
       "35           0.320099           0.333137           0.333825   \n",
       "36           0.344559           0.312110           0.355341   \n",
       "37           0.350221           0.334100           0.359736   \n",
       "38           0.344559           0.312110           0.355341   \n",
       "39           0.348511           0.322932           0.357811   \n",
       "40           0.344559           0.312110           0.355341   \n",
       "41           0.347772           0.319253           0.357186   \n",
       "42           0.355621           0.326344           0.350207   \n",
       "43           0.362457           0.345694           0.355482   \n",
       "44           0.355621           0.326344           0.350207   \n",
       "45           0.360187           0.335729           0.353283   \n",
       "46           0.355621           0.326344           0.350207   \n",
       "47           0.359275           0.332512           0.352602   \n",
       "48           0.360064           0.326790           0.343762   \n",
       "49           0.365477           0.345908           0.351045   \n",
       "50           0.360064           0.326790           0.343762   \n",
       "51           0.363555           0.336013           0.348103   \n",
       "52           0.360064           0.326790           0.343762   \n",
       "53           0.362845           0.332848           0.347203   \n",
       "54           0.367152           0.341182           0.338117   \n",
       "55           0.372621           0.358487           0.347291   \n",
       "56           0.367152           0.341182           0.338117   \n",
       "57           0.371041           0.349401           0.343721   \n",
       "58           0.367152           0.341182           0.338117   \n",
       "59           0.370418           0.346552           0.342602   \n",
       "\n",
       "    split8_test_score  split9_test_score  mean_test_score  std_test_score  \\\n",
       "0           -0.193722          -0.005320        -0.144834        0.103506   \n",
       "1           -0.193722          -0.005320        -0.144834        0.103506   \n",
       "2           -0.193722          -0.005320        -0.144834        0.103506   \n",
       "3           -0.193722          -0.005320        -0.144834        0.103506   \n",
       "4           -0.193722          -0.005320        -0.144834        0.103506   \n",
       "5           -0.193722          -0.005320        -0.144834        0.103506   \n",
       "6            0.155871           0.313640         0.216173        0.073533   \n",
       "7            0.181049           0.331448         0.232150        0.072336   \n",
       "8            0.155871           0.313640         0.216173        0.073533   \n",
       "9            0.172302           0.323759         0.226198        0.072771   \n",
       "10           0.155871           0.313640         0.216173        0.073533   \n",
       "11           0.168041           0.320173         0.223201        0.072970   \n",
       "12           0.221946           0.338934         0.285530        0.046996   \n",
       "13           0.246034           0.362914         0.300307        0.049480   \n",
       "14           0.221946           0.338934         0.285530        0.046996   \n",
       "15           0.235719           0.351355         0.293732        0.047924   \n",
       "16           0.221946           0.338934         0.285530        0.046996   \n",
       "17           0.231501           0.346919         0.290915        0.047473   \n",
       "18           0.252528           0.363914         0.316582        0.042310   \n",
       "19           0.273859           0.381707         0.329010        0.042718   \n",
       "20           0.252528           0.363914         0.316582        0.042310   \n",
       "21           0.264109           0.372932         0.323082        0.041925   \n",
       "22           0.252528           0.363914         0.316582        0.042310   \n",
       "23           0.260373           0.369740         0.320721        0.041774   \n",
       "24           0.279572           0.369898         0.330351        0.037554   \n",
       "25           0.297268           0.387553         0.341710        0.039218   \n",
       "26           0.279572           0.369898         0.330351        0.037554   \n",
       "27           0.289292           0.378510         0.336080        0.038216   \n",
       "28           0.279572           0.369898         0.330351        0.037554   \n",
       "29           0.286368           0.375406         0.333951        0.037967   \n",
       "30           0.266485           0.379371         0.339683        0.039672   \n",
       "31           0.286698           0.394259         0.350354        0.039570   \n",
       "32           0.266485           0.379371         0.339683        0.039672   \n",
       "33           0.277092           0.386751         0.344928        0.039335   \n",
       "34           0.266485           0.379371         0.339683        0.039672   \n",
       "35           0.273693           0.384257         0.342971        0.039352   \n",
       "36           0.290012           0.375809         0.342536        0.035057   \n",
       "37           0.307918           0.391384         0.353750        0.034833   \n",
       "38           0.290012           0.375809         0.342536        0.035057   \n",
       "39           0.299412           0.383790         0.347961        0.034770   \n",
       "40           0.290012           0.375809         0.342536        0.035057   \n",
       "41           0.296491           0.381306         0.345958        0.034848   \n",
       "42           0.291383           0.354950         0.346157        0.032874   \n",
       "43           0.309656           0.374831         0.357256        0.032780   \n",
       "44           0.291383           0.354950         0.346157        0.032874   \n",
       "45           0.300845           0.365460         0.351518        0.032731   \n",
       "46           0.291383           0.354950         0.346157        0.032874   \n",
       "47           0.297859           0.362426         0.349584        0.032813   \n",
       "48           0.312135           0.352352         0.347882        0.031714   \n",
       "49           0.326578           0.371293         0.358712        0.032039   \n",
       "50           0.312135           0.352352         0.347882        0.031714   \n",
       "51           0.319582           0.361780         0.352998        0.031820   \n",
       "52           0.312135           0.352352         0.347882        0.031714   \n",
       "53           0.317225           0.358672         0.351095        0.031814   \n",
       "54           0.316882           0.347100         0.348963        0.030321   \n",
       "55           0.329078           0.369219         0.360225        0.031402   \n",
       "56           0.316882           0.347100         0.348963        0.030321   \n",
       "57           0.322735           0.359129         0.354444        0.031103   \n",
       "58           0.316882           0.347100         0.348963        0.030321   \n",
       "59           0.320683           0.355851         0.352543        0.031064   \n",
       "\n",
       "    rank_test_score  \n",
       "0                55  \n",
       "1                55  \n",
       "2                55  \n",
       "3                55  \n",
       "4                55  \n",
       "5                55  \n",
       "6                52  \n",
       "7                49  \n",
       "8                52  \n",
       "9                50  \n",
       "10               52  \n",
       "11               51  \n",
       "12               46  \n",
       "13               43  \n",
       "14               46  \n",
       "15               44  \n",
       "16               46  \n",
       "17               45  \n",
       "18               40  \n",
       "19               37  \n",
       "20               40  \n",
       "21               38  \n",
       "22               40  \n",
       "23               39  \n",
       "24               34  \n",
       "25               28  \n",
       "26               34  \n",
       "27               32  \n",
       "28               34  \n",
       "29               33  \n",
       "30               29  \n",
       "31               10  \n",
       "32               29  \n",
       "33               23  \n",
       "34               29  \n",
       "35               24  \n",
       "36               25  \n",
       "37                5  \n",
       "38               25  \n",
       "39               15  \n",
       "40               25  \n",
       "41               22  \n",
       "42               19  \n",
       "43                3  \n",
       "44               19  \n",
       "45                8  \n",
       "46               19  \n",
       "47               11  \n",
       "48               16  \n",
       "49                2  \n",
       "50               16  \n",
       "51                6  \n",
       "52               16  \n",
       "53                9  \n",
       "54               12  \n",
       "55                1  \n",
       "56               12  \n",
       "57                4  \n",
       "58               12  \n",
       "59                7  \n",
       "\n",
       "[60 rows x 21 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = list(range(1,20, 2)) # 1~19까지 홀수\n",
    "\n",
    "grid_parameters = {'n_neighbors' : k,\n",
    "                   'weights' : ['uniform', 'distance'],\n",
    "                   'p': [1, 2, 3]} \n",
    "# p=1 : 맨하튼 거리, p=2 : 유클리디안 거리, p=3 : 마할라노비스 거리\n",
    "                                   \n",
    "\n",
    "gs = GridSearchCV(knn_model, param_grid = grid_parameters, cv=10, verbose = 1)\n",
    "\n",
    "result = gs.fit(X_train, y_train)\n",
    "print(f\"best score : {result.best_score_}\")\n",
    "print(f\"best test score : {gs.score(X_test,y_test)}\")\n",
    "print(f\"best parameter : {result.best_params_}\")\n",
    "print(gs.best_estimator_) \n",
    "pd.DataFrame(result.cv_results_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "결과적으로 최적의 파라미터는 <br>\n",
    "* `k 값 : 19개`\n",
    "* `가중치 : distance`\n",
    "* `거리 척도(p) : 맨하튼 거리` <br>\n",
    "일 때, <br> \n",
    "`best score : 0.360224...` <br>\n",
    "`best test score : 0.384762...` <br>\n",
    "의 결과가 나왔다. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 위와 같은 하이퍼파라미터 튜닝을 진행했을 때, 전반적으로 정확도가 낮은 결과가 나왔음을 확인할 수 있다.\n",
    "\n",
    "- 범주형 설명 변수들을 원 핫 인코딩하는 과정에서 각 범주에 대해 새로운 이진 변수가 생성되기 때문에, 데이터의 차원이 증가하게 되어서 정확도도 낮아지게 되고, 연산 시간이 증가되었던 것 같다.\n",
    "\n",
    "- 따라서 knn 알고리즘 이외에 다른 모델을 이용하는 것이 바람직할 것 같다는 생각이 든다."
   ]
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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   "pygments_lexer": "ipython3",
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