jeju_recommendation_system_final.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "9d945e28",
      "metadata": {
        "id": "9d945e28"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from IPython.display import Image\n",
        "import random"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "4264a7b8",
      "metadata": {
        "id": "4264a7b8"
      },
      "outputs": [],
      "source": [
        "personal = pd.read_excel('jeju_personal.xlsx')\n",
        "place = pd.read_excel('jeju_place.xlsx')\n",
        "travel = pd.read_excel('jeju_travel.xlsx')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "38bdf55b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "38bdf55b",
        "outputId": "5fe341b4-db06-44ab-a94c-7d216fc522f7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1. 성별을 입력해주세요.(남자1/여자2 입력)\n",
            "2\n",
            "2. 나이를 입력해주세요.(15~20대 1입력/ 21~30대 2입력/ 31~40대 3입력/ 41~50대 4입력/ 51~60대 5입력/ 61 이상 6입력)\n",
            "2\n",
            "3. 혼인 여부를 입력해주세요. (미혼1/ 기혼2 입력)\n",
            "1\n",
            "4. 몇번째 제주도 방문인가요?(1,2,3,4)\n",
            "3\n",
            "5. 여행의 주 목적이 무엇입니까? (해당되는 번호 하나를 입력해주세요.)\n",
            "77) 자연경관감상                  60) 식도락(맛집여행)                 57) 산/오름/올레길 트래픽\n",
            "80) 쇼핑                       156) 박물관/테마공원 방문             174) 영화/드라마촬영지 방문\n",
            "37) 해변활동                     70) 레포츠(승마, 골프, ATV 등)        17) 전통문화체험\n",
            "104) 역사/문화유적지 방문           90) 종교/순례활동\n",
            "77\n",
            "6. 혼자여행 하실건가요?(아니면1 맞으면2를 입력해주세요.)\n",
            "1\n",
            "7. 본인을 제외한 함께 여행할 일행의 수는 몇 명입니까?\n",
            "2\n",
            "8. 일행과의 관계를 골라주세요.(1-6까지 해당되는 번호 하나를 입력해주세요.단, 동행인 모두의 관계를 입력하시오.)\n",
            "ex) 동행인 세 명이 모두 친구일 경우에는 1 을 한3번만 입력하시고 0을 입력해주세요 \n",
            "1) 친구/연인                          2) (직장)동료\n",
            "3) 단체모임                           4) 비동거가족\n",
            "5) 친척                               6) 기타\n",
            "1\n",
            "1\n",
            "[15151515, 2, 2, 1, 3, 77, 1, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "pid               100101.0\n",
            "sex                    2.0\n",
            "age                    3.0\n",
            "marry                  1.0\n",
            "count                  2.0\n",
            "inform                 1.0\n",
            "num_people             1.0\n",
            "act_nature             2.0\n",
            "act_food               1.0\n",
            "act_sports             0.0\n",
            "act_shop               0.0\n",
            "act_museum             0.0\n",
            "act_movie              0.0\n",
            "act_bitch              0.0\n",
            "act_report             0.0\n",
            "act_culture            0.0\n",
            "act_history            0.0\n",
            "act_religion           0.0\n",
            "revisit                4.0\n",
            "recommend              4.0\n",
            "relation               1.0\n",
            "purpose_jplace     60004.0\n",
            "Name: 0, dtype: float64\n",
            "\n",
            "pid               100102.0\n",
            "sex                    1.0\n",
            "age                    4.0\n",
            "marry                  1.0\n",
            "count                  3.0\n",
            "inform                 2.0\n",
            "num_people             0.0\n",
            "act_nature             3.0\n",
            "act_food               0.0\n",
            "act_sports             0.0\n",
            "act_shop               0.0\n",
            "act_museum             0.0\n",
            "act_movie              0.0\n",
            "act_bitch              0.0\n",
            "act_report             0.0\n",
            "act_culture            0.0\n",
            "act_history            0.0\n",
            "act_religion           0.0\n",
            "revisit                3.0\n",
            "recommend              3.0\n",
            "relation               0.0\n",
            "purpose_jplace    174004.0\n",
            "Name: 1, dtype: float64\n",
            "\n",
            "pid               100103.0\n",
            "sex                    1.0\n",
            "age                    2.0\n",
            "marry                  1.0\n",
            "count                  2.0\n",
            "inform                 1.0\n",
            "num_people             4.0\n",
            "act_nature             5.0\n",
            "act_food               1.0\n",
            "act_sports             0.0\n",
            "act_shop               0.0\n",
            "act_museum             0.0\n",
            "act_movie              0.0\n",
            "act_bitch              0.0\n",
            "act_report             0.0\n",
            "act_culture            0.0\n",
            "act_history            0.0\n",
            "act_religion           0.0\n",
            "revisit                4.0\n",
            "recommend              4.0\n",
            "relation               1.0\n",
            "purpose_jplace     77001.0\n",
            "Name: 2, dtype: float64\n",
            "\n",
            "pid               15151515.0\n",
            "sex                      2.0\n",
            "age                      2.0\n",
            "marry                    1.0\n",
            "count                    3.0\n",
            "inform                   1.0\n",
            "num_people               2.0\n",
            "act_nature               0.0\n",
            "act_food                 0.0\n",
            "act_sports               0.0\n",
            "act_shop                 0.0\n",
            "act_museum               0.0\n",
            "act_movie                0.0\n",
            "act_bitch                0.0\n",
            "act_report               0.0\n",
            "act_culture              0.0\n",
            "act_history              0.0\n",
            "act_religion             0.0\n",
            "revisit                  0.0\n",
            "recommend                0.0\n",
            "relation                 2.0\n",
            "purpose_jplace       77000.0\n",
            "Name: 3, dtype: float64\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# 액셀의 칼럼을 받아옴\n",
        "\n",
        "personal = personal[[ \"pid\", \"sex\", \"age\", \"marry\"]]\n",
        "\n",
        "place = place[[\"pid\", \"q3_1\", \"q3_2\", \"q3_3\", \"q3_4\", \"q3_5\", \n",
        "                   \"q3_6\", \"q3_7\", \"q3_8\", \"q3_9\", \"q3_10\", \"q3_11\",\"q4\",\n",
        "                   \"q4_1\", \"q4_2\"]]\n",
        "\n",
        "travel = travel[[\"pid\",\"count\",\"q1\",\"q2\",\"q2_1\",\"q2_2_1\",\"q2_2_2\",\"q2_2_3\",\"q2_2_4\",\"q2_2_5\"\n",
        "                     ,\"q2_2_6\"]]\n",
        "\n",
        "\n",
        "personal.columns = [\"pid\", \"sex\", \"age\", \"marry\"]\n",
        "\n",
        "\n",
        "\n",
        "place.columns = [\"pid\", \"act_nature\", \"act_food\", \"act_sports\", \"act_shop\", \"act_museum\",\n",
        "                   \"act_movie\", \"act_bitch\",  \"act_report\", \"act_culture\",\n",
        "                   \"act_history\", \"act_religion\", \"jplace\",\"revisit\", \"recommend\"]\n",
        "\n",
        "\n",
        "travel.columns = [\"pid\",\"count\",\"purpose\",\"inform\",\"num_people\",\n",
        "                    \"relation1\",\"relation2\",\"relation3\",\"relation4\",\"relation5\",\n",
        "                    \"relation6\"]\n",
        "\n",
        "travel = travel.drop_duplicates('pid',keep='first')\n",
        "place = place.drop_duplicates('pid',keep='first')\n",
        "\n",
        "\n",
        "data = pd.merge(personal, travel, on='pid')\n",
        "data = pd.merge(data, place, on='pid')\n",
        "#아래 데이터 항목에 맞춰 리스트 값 부여 예정\n",
        "data = data[[\"pid\",\"sex\",\"age\",\"marry\",\"count\",\"purpose\",\"inform\",\"num_people\",\n",
        "                    \"relation1\",\"relation2\",\"relation3\",\"relation4\",\"relation5\",\n",
        "                    \"relation6\",\"act_nature\", \"act_food\", \"act_sports\", \"act_shop\", \"act_museum\",\n",
        "                   \"act_movie\", \"act_bitch\",  \"act_report\", \"act_culture\",\n",
        "                   \"act_history\", \"act_religion\", \"jplace\",\"revisit\", \"recommend\"]]\n",
        "\n",
        "#list1을 생성하고 0번째 행에 15151515 저장\n",
        "list1 = [15151515]\n",
        "#설문지(사용자의 입력값을 받아서 list1에 저장)\n",
        "print(\"1. 성별을 입력해주세요.(남자1/여자2 입력)\")\n",
        "s=int(input())\n",
        "list1.append(s)\n",
        "print(\"2. 나이를 입력해주세요.(15~20대 1입력/ 21~30대 2입력/ 31~40대 3입력/ 41~50대 4입력/ 51~60대 5입력/ 61 이상 6입력)\")\n",
        "s=int(input())\n",
        "list1.append(s)\n",
        "print(\"3. 혼인 여부를 입력해주세요. (미혼1/ 기혼2 입력)\")\n",
        "s=int(input())\n",
        "list1.append(s)\n",
        "print(\"4. 몇번째 제주도 방문인가요?(1,2,3,4)\")\n",
        "s=int(input())\n",
        "list1.append(s)\n",
        "print(\"5. 여행의 주 목적이 무엇입니까? (해당되는 번호 하나를 입력해주세요.)\")\n",
        "print(\"77) 자연경관감상                  60) 식도락(맛집여행)                 57) 산/오름/올레길 트래픽\")\n",
        "print(\"80) 쇼핑                       156) 박물관/테마공원 방문             174) 영화/드라마촬영지 방문\")\n",
        "print(\"37) 해변활동                     70) 레포츠(승마, 골프, ATV 등)        17) 전통문화체험\")\n",
        "print(\"104) 역사/문화유적지 방문           90) 종교/순례활동\")\n",
        "s=int(input())\n",
        "list1.append(s)\n",
        "print(\"6. 혼자여행 하실건가요?(아니면1 맞으면2를 입력해주세요.)\")\n",
        "s=int(input())\n",
        "if s==2 :\n",
        "    list1.append(s)\n",
        "    \n",
        "    for i in range(7):\n",
        "        list1.append(0)\n",
        "\n",
        "else:\n",
        "    list1.append(s)\n",
        "    print(\"7. 본인을 제외한 함께 여행할 일행의 수는 몇 명입니까?\")\n",
        "    s=int(input())\n",
        "    num = s\n",
        "    list1.append(s)\n",
        "        \n",
        "    for i in range(7):\n",
        "        list1.append(0)\n",
        "    \n",
        "    print(\"8. 일행과의 관계를 골라주세요.(1-6까지 해당되는 번호 하나를 입력해주세요.단, 동행인 모두의 관계를 입력하시오.)\")\n",
        "    print(\"ex) 동행인 세 명이 모두 친구일 경우에는 1 을 한3번만 입력하시고 0을 입력해주세요 \")\n",
        "    print(\"1) 친구/연인                          2) (직장)동료\")\n",
        "    print(\"3) 단체모임                           4) 비동거가족\")\n",
        "    print(\"5) 친척                               6) 기타\")\n",
        "    for i in range(num):\n",
        "        s=int(input())\n",
        "        if s==0:\n",
        "            break\n",
        "        if s==1:\n",
        "            list1.pop(14)\n",
        "            list1.insert(8,1)\n",
        "        if s==2:\n",
        "            list1.pop(14)\n",
        "            list1.insert(9,2)\n",
        "        if s==3:\n",
        "            list1.pop(14)\n",
        "            list1.insert(10,3)\n",
        "        if s==4:\n",
        "            list1.pop(14)\n",
        "            list1.insert(11,4)\n",
        "        if s==5:\n",
        "            list1.pop(14)\n",
        "            list1.insert(12,5)\n",
        "        if s==6:\n",
        "            list1.pop(14)\n",
        "            list1.insert(13,6)\n",
        "          \n",
        "    list1.pop(14)\n",
        "for i in range(1,15):\n",
        "    list1.append(0)\n",
        "#사용자에게 입력받은 데이터로만 여행지를 추천해줘야 하므로 만족도 값은 랜덤하게 받음\n",
        "\n",
        "\n",
        "data.loc[3] = list1\n",
        "#데이터가 들어왔는지 확인하고 분석하기 위하여 list1 값을 3행에 받아옴, \n",
        "print(list1)\n",
        "\n",
        "\n",
        "\n",
        "data[\"num_people\"] = data[\"num_people\"].fillna(0)\n",
        "data[\"relation1\"] = data[\"relation1\"].fillna(0)\n",
        "data[\"relation2\"] = data[\"relation2\"].fillna(0)\n",
        "data[\"relation3\"] = data[\"relation3\"].fillna(0)\n",
        "data[\"relation4\"] = data[\"relation4\"].fillna(0)\n",
        "data[\"relation5\"] = data[\"relation5\"].fillna(0)\n",
        "data[\"relation6\"] = data[\"relation6\"].fillna(0)\n",
        "data.isnull().sum()\n",
        "data['relation'] = data[['relation1', 'relation2', 'relation3',\n",
        "                            'relation4', 'relation5', 'relation6']].sum(axis=1)\n",
        "data.drop(labels = ['relation1', 'relation2', 'relation3',\n",
        "                    'relation4', 'relation5', 'relation6'], \n",
        "            axis = 1, inplace = True)\n",
        "data['purpose'] = data['purpose'].astype(int)\n",
        "data = data.fillna(0)\n",
        "\n",
        "\n",
        "data[\"act_nature\"] = data[\"act_nature\"].map(lambda i: i/1 if i > 0 else 0).astype(int)\n",
        "data[\"act_food\"] = data[\"act_food\"].map(lambda i: i/2 if i > 0 else 0).astype(int)\n",
        "data[\"act_sports\"] = data[\"act_sports\"].map(lambda i: i/3 if i > 0 else 0).astype(int)\n",
        "data[\"act_shop\"] = data[\"act_shop\"].map(lambda i: i/4 if i > 0 else 0).astype(int)\n",
        "data[\"act_museum\"] = data[\"act_museum\"].map(lambda i: i/5 if i > 0 else 0).astype(int)\n",
        "data[\"act_movie\"] = data[\"act_movie\"].map(lambda i: i/6 if i > 0 else 0).astype(int)\n",
        "data[\"act_bitch\"] = data[\"act_bitch\"].map(lambda i: i/7 if i > 0 else 0).astype(int)\n",
        "data[\"act_report\"] = data[\"act_report\"].map(lambda i: i/8 if i > 0 else 0).astype(int)\n",
        "data[\"act_culture\"] = data[\"act_culture\"].map(lambda i: i/9 if i > 0 else 0).astype(int)\n",
        "data[\"act_history\"] = data[\"act_history\"].map(lambda i: i/10 if i > 0 else 0).astype(int)\n",
        "data[\"act_religion\"] = data[\"act_religion\"].map(lambda i: i/11 if i > 0 else 0).astype(int)\n",
        "\n",
        "#purpose_jplace값 구하기\n",
        "data['purpose_jplace'] = data['purpose'].astype(int)*1000 + data['jplace'].astype(int)\n",
        "data.drop(labels = ['purpose'], axis = 1, inplace = True)\n",
        "data.drop(labels = ['jplace'], axis = 1, inplace = True)\n",
        "\n",
        "\n",
        "\n",
        "#데이터 확인 \n",
        "count = 4\n",
        "for index, row in data.iterrows():\n",
        "    print(row,end = \"\\n\\n\")\n",
        "    if index == count -1:\n",
        "        break\n",
        "        \n",
        "        \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "bb7142f7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bb7142f7",
        "outputId": "887e06ab-e8d2-43f5-9602-bbca2600e16c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           pid  act_nature  act_food  act_sports  act_shop  act_museum  \\\n",
            "0       100101           2         1           0         0           0   \n",
            "1       100102           3         0           0         0           0   \n",
            "2       100103           5         1           0         0           0   \n",
            "3     15151515           0         0           0         0           0   \n",
            "4       100105           0         0           0         0           0   \n",
            "...        ...         ...       ...         ...       ...         ...   \n",
            "6129    106230           2         1           0         0           0   \n",
            "6130    106231           3         0           1         0           0   \n",
            "6131    106232           2         1           1         0           0   \n",
            "6132    106233           1         1           0         0           0   \n",
            "6133    106234           0         1           0         0           0   \n",
            "\n",
            "      act_movie  act_bitch  act_report  act_culture  act_history  \\\n",
            "0             0          0           0            0            0   \n",
            "1             0          0           0            0            0   \n",
            "2             0          0           0            0            0   \n",
            "3             0          0           0            0            0   \n",
            "4             0          0           0            0            0   \n",
            "...         ...        ...         ...          ...          ...   \n",
            "6129          0          0           0            0            0   \n",
            "6130          0          0           0            0            0   \n",
            "6131          0          0           0            0            0   \n",
            "6132          0          0           0            0            0   \n",
            "6133          0          0           0            0            0   \n",
            "\n",
            "      act_religion  purpose_jplace  \n",
            "0                0           60004  \n",
            "1                0          174004  \n",
            "2                0           77001  \n",
            "3                0           77000  \n",
            "4                0           37005  \n",
            "...            ...             ...  \n",
            "6129             0           70008  \n",
            "6130             0           57003  \n",
            "6131             0           90009  \n",
            "6132             0           37005  \n",
            "6133             0          174004  \n",
            "\n",
            "[6134 rows x 13 columns]\n"
          ]
        }
      ],
      "source": [
        "act_data_pid = data[[\"pid\", \"act_nature\", \"act_food\", \"act_sports\", \"act_shop\", \"act_museum\",\n",
        "                   \"act_movie\", \"act_bitch\",  \"act_report\", \"act_culture\",\n",
        "                   \"act_history\", \"act_religion\",\"purpose_jplace\"]]\n",
        "\n",
        "act_data = data[[ \"act_nature\", \"act_food\", \"act_sports\", \"act_shop\", \"act_museum\",\n",
        "                   \"act_movie\", \"act_bitch\",  \"act_report\", \"act_culture\",\n",
        "                   \"act_history\", \"act_religion\", \"purpose_jplace\"]]\n",
        "\n",
        "noact_data_pid = data[[\"pid\", \"sex\", \"age\",\"marry\",  \"recommend\", \"num_people\", \n",
        "                 \"relation\", \"purpose_jplace\"\n",
        "                      ]]\n",
        "\n",
        "noact_data = data[[\"sex\", \"age\", \"marry\", \"recommend\", \"num_people\", \n",
        "                 \"relation\",  \"purpose_jplace\"\n",
        "                      ]]\n",
        "\n",
        "print(act_data_pid)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "7143a23f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 928
        },
        "id": "7143a23f",
        "outputId": "8a50c10e-26b6-484e-eb16-7e457ba54365"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           pid  act_nature  act_food  act_sports  act_shop  act_museum  \\\n",
            "0       100101           2         5           0         0           0   \n",
            "1       100102           1         0           0         0           0   \n",
            "2       100103           3         1           0         0           0   \n",
            "3     15151515           0         0           0         0           0   \n",
            "4       100105           0         0           0         0           0   \n",
            "...        ...         ...       ...         ...       ...         ...   \n",
            "6129    106230           2         4           0         0           0   \n",
            "6130    106231           4         0           5         0           0   \n",
            "6131    106232           3         5           4         0           0   \n",
            "6132    106233           1         3           0         0           0   \n",
            "6133    106234           0         2           0         0           0   \n",
            "\n",
            "      act_movie  act_bitch  act_report  act_culture  act_history  \\\n",
            "0             0          0           0            0            0   \n",
            "1             0          0           0            0            0   \n",
            "2             0          0           0            0            0   \n",
            "3             0          0           0            0            0   \n",
            "4             0          0           0            0            0   \n",
            "...         ...        ...         ...          ...          ...   \n",
            "6129          0          0           0            0            0   \n",
            "6130          0          0           0            0            0   \n",
            "6131          0          0           0            0            0   \n",
            "6132          0          0           0            0            0   \n",
            "6133          0          0           0            0            0   \n",
            "\n",
            "      act_religion  purpose_jplace  \n",
            "0                0           60004  \n",
            "1                0          174004  \n",
            "2                0           77001  \n",
            "3                0           77000  \n",
            "4                0           37005  \n",
            "...            ...             ...  \n",
            "6129             0           70008  \n",
            "6130             0           57003  \n",
            "6131             0           90009  \n",
            "6132             0           37005  \n",
            "6133             0          174004  \n",
            "\n",
            "[6134 rows x 13 columns]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        pid  act_nature  act_food  act_sports  act_shop  act_museum  \\\n",
              "0    100101           2         5           0         0           0   \n",
              "1    100102           1         0           0         0           0   \n",
              "2    100103           3         1           0         0           0   \n",
              "3  15151515           0         0           0         0           0   \n",
              "4    100105           0         0           0         0           0   \n",
              "\n",
              "   act_movie  act_bitch  act_report  act_culture  act_history  act_religion  \\\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",
              "   purpose_jplace  \n",
              "0           60004  \n",
              "1          174004  \n",
              "2           77001  \n",
              "3           77000  \n",
              "4           37005  "
            ],
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "import random\n",
        "random.seed(1)\n",
        "for i in range(6134):\n",
        "    for j in range(0,11):\n",
        "        if act_data.values[i][j] != 0:\n",
        "            act_data.values[i][j] = random.randrange(1,6)\n",
        "\n",
        "act_data_pid = pd.concat([data['pid'], act_data], axis=1)\n",
        "print(act_data_pid)\n",
        "\n",
        "#유사도 측정 TEST\n",
        "# act_data_pid.loc[0] = [1714257,0,5,0,0,0,0,0,0,0,0,0,60004]\n",
        "# act_data_pid.loc[1] = [1613413,4,0,0,0,0,0,0,0,0,0,0,77001]\n",
        "# act_data_pid.loc[2] = [1712377,0,0,5,0,0,0,0,0,0,0,0,57003]\n",
        "\n",
        "act_data_pid.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "994ec9c5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "994ec9c5",
        "outputId": "c4b8ffd5-3460-466b-fc48-c91dd31dc775"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--유사도 결과--\n",
            "[77001]\n",
            "결과 데이터 표를 참고하세요! \n",
            "\n"
          ]
        }
      ],
      "source": [
        "# 평균제곱차이 유사도\n",
        "# act 데이터\n",
        "\n",
        "from collections import OrderedDict\n",
        "\n",
        "def sim_msd(data, name1, name2):\n",
        "    s = 0\n",
        "    count = 0\n",
        "    for i in data[data.pid == name1]:\n",
        "        if i == \"pid\":\n",
        "            continue\n",
        "        s += pow(int(data[data.pid == name1][i]) - int(data[data.pid == name2][i]), 2)\n",
        "        count += 1\n",
        "        \n",
        "    return 1 / (1 + (s / count))\n",
        "\n",
        "def top_match(data, name, index = 10, sim_function = sim_msd):\n",
        "    li = []\n",
        "    for i in data[\"pid\"]:\n",
        "        if (name != i):\n",
        "            li.append((sim_function(data, name, i), i))\n",
        "    li.sort()\n",
        "    li.reverse()\n",
        "    \n",
        "    c = []\n",
        "    for i in range(index):\n",
        "        if int(data.loc[data.pid == li[i][1], \"purpose_jplace\"]) is not int(data.loc[data.pid == name, \"purpose_jplace\"]):\n",
        "            c.append(int(data.loc[data.pid == li[i][1], \"purpose_jplace\"]))\n",
        "            c = list(OrderedDict.fromkeys(c))\n",
        "    \n",
        "    return c\n",
        "\n",
        "print(\"--유사도 결과--\")\n",
        "print(top_match(act_data_pid, 15151515))\n",
        "print(\"결과 데이터 표를 참고하세요! \\n\")\n",
        "\n",
        "#inputdata함수를 불러와 사용자의 만족도조사\n",
        "# inpudata()\n",
        "\n",
        "#data.loc[4] = list1\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "84S2-zWv8KdW"
      },
      "id": "84S2-zWv8KdW"
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "1d4edee8",
      "metadata": {
        "id": "1d4edee8",
        "outputId": "7fba674e-1f03-4747-968f-b212eb40659a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[104008, 156021, 17006, 70008, 174004, 37005]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "# 코사인 유사도\n",
        "# act 데이터\n",
        "\n",
        "from math import sqrt\n",
        "from collections import OrderedDict\n",
        "\n",
        "def sim_cosine(data, name1, name2):\n",
        "    sum_name1 = 0\n",
        "    sum_name2 = 0\n",
        "    sum_name1_name2 = 0\n",
        "\n",
        "    for i in data[data.pid == name1]:\n",
        "        if i == \"pid\":\n",
        "            continue\n",
        "        sum_name1 += pow(int(data[data.pid == name1][i]), 2)\n",
        "        sum_name2 += pow(int(data[data.pid == name2][i]), 2)\n",
        "        sum_name1_name2 += int(data[data.pid == name1][i]) * int(data[data.pid == name2][i])\n",
        "        \n",
        "    return sum_name1_name2 / (sqrt(sum_name1)*sqrt(sum_name2))\n",
        "\n",
        "def top_match(data, name, index = 10, sim_function = sim_cosine):\n",
        "    li = []\n",
        "    for i in data[\"pid\"]:\n",
        "        if (name != i):\n",
        "            li.append((sim_function(data, name, i), i))\n",
        "    li.sort()\n",
        "    li.reverse()\n",
        "    \n",
        "    c = []\n",
        "    for i in range(index):\n",
        "        if int(data.loc[data.pid == li[i][1], \"purpose_jplace\"]) is not int(data.loc[data.pid == name, \"purpose_jplace\"]):\n",
        "            c.append(int(data.loc[data.pid == li[i][1], \"purpose_jplace\"]))\n",
        "            c = list(OrderedDict.fromkeys(c))\n",
        "    \n",
        "    return c\n",
        "# top_match(act_data_pid, 1714257)\n",
        "# top_match(act_data_pid, 1613413)\n",
        "# top_match(act_data_pid, 1712377)\n",
        "top_match(act_data_pid, 15151515)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "c38f0e52",
      "metadata": {
        "id": "c38f0e52",
        "outputId": "64ca91af-a417-4920-e2ad-194181c2f9c1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[37005, 104008, 156021]"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "# 피어슨 유사도\n",
        "# act 데이터\n",
        "\n",
        "from collections import OrderedDict\n",
        "\n",
        "def sim_pearson(data, name1, name2):\n",
        "    avg_name1 = 0\n",
        "    avg_name2 = 0\n",
        "    count = 0\n",
        "    for i in data[data.pid == name1]:\n",
        "        if i == \"pid\":\n",
        "            continue\n",
        "        avg_name1 = int(data[data.pid == name1][i])\n",
        "        avg_name2 = int(data[data.pid == name2][i])\n",
        "        count += 1\n",
        "        \n",
        "    avg_name1 = avg_name1 / count\n",
        "    avg_name2 = avg_name2 / count\n",
        "    \n",
        "    sum_name1 = 0\n",
        "    sum_name2 = 0\n",
        "    sum_name1_name2 = 0\n",
        "    count = 0\n",
        "    for i in data[data.pid == name1]:\n",
        "        if i == \"pid\":\n",
        "            continue\n",
        "        sum_name1 += pow(int(data[data.pid == name1][i]) - avg_name1, 2)\n",
        "        sum_name2 += pow(int(data[data.pid == name2][i]) - avg_name2, 2)\n",
        "        sum_name1_name2 += (int(data[data.pid == name1][i]) - avg_name1) * (int(data[data.pid == name2][i]) - avg_name2)\n",
        "        \n",
        "    return sum_name1_name2 / (sqrt(sum_name1)*sqrt(sum_name2))\n",
        "\n",
        "def top_match(data, name, index = 10, sim_function = sim_pearson):\n",
        "    li = []\n",
        "    for i in data[\"pid\"]:\n",
        "        if (name != i):\n",
        "            li.append((sim_function(data, name, i), i))\n",
        "    li.sort()\n",
        "    li.reverse()\n",
        "    \n",
        "    c = []\n",
        "    for i in range(index):\n",
        "        if int(data.loc[data.pid == li[i][1], \"purpose_jplace\"]) is not int(data.loc[data.pid == name, \"purpose_jplace\"]):\n",
        "            c.append(int(data.loc[data.pid == li[i][1], \"purpose_jplace\"]))\n",
        "            c = list(OrderedDict.fromkeys(c))\n",
        "    \n",
        "    return c\n",
        "\n",
        "# top_match(act_data_pid, 1712377)\n",
        "# print(top_match(act_data_pid, 1714257))\n",
        "# print(top_match(act_data_pid, 1613413))\n",
        "# print(top_match(act_data_pid, 1712377))\n",
        "top_match(act_data_pid, 15151515)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "b0d40584",
      "metadata": {
        "id": "b0d40584",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "outputId": "3172643d-fa2a-475d-cf45-7250b934fdff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "Image(\"/content/ouput_data.png\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "29f2b542",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29f2b542",
        "outputId": "99f29623-ec2b-45e8-8757-6b65966fd06f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1. 자연경관감상에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\n",
            "5\n",
            "2. 식도락(맛집여행)에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\n",
            "5\n",
            "3. 산/오름/올레길 트래픽에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\n",
            "5\n",
            "4. 쇼핑에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\n",
            "5\n",
            "5. 박물관/테마공원 방문에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\n",
            "5\n",
            "6. 영화/드라마촬영지 방문에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\n",
            "5\n",
            "7. 해변활동에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\n",
            "5\n",
            "8. 레포츠(승마, 골프, ATV 등)에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\n",
            "5\n",
            "9. 전통문화체험에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\n",
            "5\n",
            "10. 역사/문화유적지 방문에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\n",
            "5\n",
            "11. 종교/순례활동에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\n",
            "5\n",
            "12. 여행추천 서비스에 대한 전반적 만족도에 대해 입력해주세요.(1아주불만족-10매우만족)\n",
            "5\n",
            "13. 재방문 의향이 있으신가요?(1매우아님-5아주많음)\n",
            "5\n",
            "14. 다른사람에게 추천할 의향이 있으신가요?(1매우아님-5아주많음)\n",
            "5\n",
            "[408292, 2, 2, 1, 3, 77, 1, 2, 1, 1, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n"
          ]
        }
      ],
      "source": [
        "#만족도 설문지 함수. \n",
        "#추후에 list1의 만족도 데이터 부분을 덮어씌울 예정\n",
        "def inpudata():\n",
        "    global data\n",
        "    global list1\n",
        "    print(\"1. 자연경관감상에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\")\n",
        "    s=int(input())\n",
        "    list1.insert(14,s)\n",
        "    list1.pop()\n",
        "    print(\"2. 식도락(맛집여행)에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\")\n",
        "    s=int(input())\n",
        "    list1.insert(15,s)\n",
        "    list1.pop()\n",
        "    print(\"3. 산/오름/올레길 트래픽에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\")\n",
        "    s=int(input())\n",
        "    list1.insert(16,s)\n",
        "    list1.pop()\n",
        "    print(\"4. 쇼핑에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\")\n",
        "    s=int(input())\n",
        "    list1.insert(17,s)\n",
        "    list1.pop()\n",
        "    print(\"5. 박물관/테마공원 방문에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\")\n",
        "    s=int(input())\n",
        "    list1.insert(18,s)\n",
        "    list1.pop()\n",
        "    print(\"6. 영화/드라마촬영지 방문에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\")\n",
        "    s=int(input())\n",
        "    list1.insert(19,s)\n",
        "    list1.pop()\n",
        "    print(\"7. 해변활동에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\")\n",
        "    s=int(input())\n",
        "    list1.insert(20,s)\n",
        "    list1.pop()\n",
        "    print(\"8. 레포츠(승마, 골프, ATV 등)에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\")\n",
        "    s=int(input())\n",
        "    list1.insert(21,s)\n",
        "    list1.pop()\n",
        "    print(\"9. 전통문화체험에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\")\n",
        "    s=int(input())\n",
        "    list1.insert(22,s)\n",
        "    list1.pop()\n",
        "    print(\"10. 역사/문화유적지 방문에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\")\n",
        "    s=int(input())\n",
        "    list1.insert(23,s)\n",
        "    list1.pop()\n",
        "    print(\"11. 종교/순례활동에 대한 만족도를 입력해주세요.(1아주불만족-5매우만족)\")\n",
        "    s=int(input())\n",
        "    list1.insert(24,s)\n",
        "    list1.pop()\n",
        "    print(\"12. 여행추천 서비스에 대한 전반적 만족도에 대해 입력해주세요.(1아주불만족-10매우만족)\")\n",
        "    s=int(input())\n",
        "    list1.insert(25,s)\n",
        "    list1.pop()\n",
        "    print(\"13. 재방문 의향이 있으신가요?(1매우아님-5아주많음)\")\n",
        "    s=int(input())\n",
        "    list1.insert(26,s)\n",
        "    list1.pop()\n",
        "    print(\"14. 다른사람에게 추천할 의향이 있으신가요?(1매우아님-5아주많음)\")\n",
        "    s=int(input())\n",
        "    list1.insert(27,s)\n",
        "    list1.pop()\n",
        "\n",
        "    #list1의 pid값을 랜덤하게 재생성(학습하고 추천할때 pid가 겹치는 것을 방지)\n",
        "    list1.insert(0,random.randint(110000,550000))\n",
        "    list1.pop(1)\n",
        "\n",
        "    print(list1)\n",
        "    data = data.append(list1,ignore_index=True)\n",
        "#     new_data = data.append(data.iloc[-1],ignore_index=True)\n",
        "#     new_data.at[-1] = list1\n",
        "#     print(data.iloc[-1])\n",
        "\n",
        "#     new_data = data.append(pd.Series(list1, index=data.columns), ignore_index=True)\n",
        "\n",
        "\n",
        "#     data= pd.concat([data['pid'], list1], axis=1)\n",
        "#     data = data.concat([data,list1])\n",
        "#    # data.loc[6137] = list1\n",
        "\n",
        "\n",
        "\n",
        "inpudata()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "00a86a3c",
      "metadata": {
        "id": "00a86a3c"
      },
      "outputs": [],
      "source": []
    }
  ],
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    "kernelspec": {
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      "language": "python",
      "name": "python3"
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      "name": "python",
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      "pygments_lexer": "ipython3",
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