ch9-predict-future-sales.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":8587,"databundleVersionId":868304,"sourceType":"competition"}],"dockerImageVersionId":30123,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Chapter 9 향후 판매량 예측\n\n[Predict Future Sales competition link](https://www.kaggle.com/competitions/competitive-data-science-predict-future-sales/data)  \n[탐색적 데이터 분석 코드 참고 링크](https://www.kaggle.com/dimitreoliveira/model-stacking-feature-engineering-and-eda)","metadata":{}},{"cell_type":"markdown","source":"# 9.1 경진대회 이해\n---\n\n### 🔍 학습 목표\n\n- EDA는 간단하게 다룰 예정\n- 다양한 피처 엔지니어링 기법\n\n\n### 🔍 학습 키워드  \n\n- 유형 및 평가지표: 회귀, RMSE\n- EDA: 데이터 병합, 그룹화\n- 머신러닝 모델: LightGBM\n- 피처 엔지이너링: 피처명 한글화, 데이터 다운캐스팅, 조합, 이어붙이기, 병합, 이상치 제거, 파생 피처 생성\n\n\n### 🔍 데이터 설명\n\n|데이터|내용|\n|:---|:---|\n|sales_train.csv|2023년 1월~ 2015년 10월 일별 판매 내역|\n|test.csv|테스트 데이터|\n|sample_submission.csv|샘플 제출 파일|\n|items.csv|상품 정보|\n|item_categories.csv|상품 분류 정보|\n|shops.csv|상점 정보|\n\n\n### 🔍 주의 사항  \n\n- 각 상점읜 상품별 판매량 즉 타깃값은 0에서 20개 사이여야 함\n- 시계열 데이터이므로 가장 최근인 2015년 10월 판매 내역을 검증 데이터로 사용할 예정","metadata":{}},{"cell_type":"markdown","source":"# 9.2 탐색적 데이터 분석  \n---\n\n## 9.2.1 데이터 둘러보기","metadata":{"papermill":{"duration":0.025716,"end_time":"2021-09-11T07:42:47.503023","exception":false,"start_time":"2021-09-11T07:42:47.477307","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\n\nimport time as time\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport lightgbm as lgb\n\nimport warnings\n\nwarnings.filterwarnings(action='ignore')\n\n# 데이터 경로 지정\ndata_path = '/kaggle/input/competitive-data-science-predict-future-sales/'\n\nsales_train = pd.read_csv(data_path + 'sales_train.csv')\nshops = pd.read_csv(data_path + 'shops.csv')\nitems = pd.read_csv(data_path + 'items.csv')\nitem_categories = pd.read_csv(data_path + 'item_categories.csv')\ntest = pd.read_csv(data_path + 'test.csv')\nsubmission = pd.read_csv(data_path + 'sample_submission.csv')","metadata":{"papermill":{"duration":2.855857,"end_time":"2021-09-11T07:42:50.493438","exception":false,"start_time":"2021-09-11T07:42:47.637581","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-11-27T09:46:05.421967Z","iopub.execute_input":"2024-11-27T09:46:05.422353Z","iopub.status.idle":"2024-11-27T09:46:10.373763Z","shell.execute_reply.started":"2024-11-27T09:46:05.422316Z","shell.execute_reply":"2024-11-27T09:46:10.372602Z"},"trusted":true},"outputs":[],"execution_count":1},{"cell_type":"code","source":"# version checking\n\nprint(pd.__version__)\nprint(np.__version__)\nprint(sns.__version__)\nprint(lgb.__version__)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:10.375731Z","iopub.execute_input":"2024-11-27T09:46:10.376178Z","iopub.status.idle":"2024-11-27T09:46:10.382404Z","shell.execute_reply.started":"2024-11-27T09:46:10.376133Z","shell.execute_reply":"2024-11-27T09:46:10.381477Z"}},"outputs":[{"name":"stdout","text":"2.2.3\n1.26.4\n0.12.2\n4.2.0\n","output_type":"stream"}],"execution_count":2},{"cell_type":"markdown","source":"### 🔍 `sales_train` 데이터","metadata":{"papermill":{"duration":0.023678,"end_time":"2021-09-11T07:42:50.54258","exception":false,"start_time":"2021-09-11T07:42:50.518902","status":"completed"},"tags":[]}},{"cell_type":"code","source":"sales_train.head()","metadata":{"papermill":{"duration":0.050468,"end_time":"2021-09-11T07:42:50.617023","exception":false,"start_time":"2021-09-11T07:42:50.566555","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:10.383493Z","iopub.execute_input":"2024-11-27T09:46:10.383779Z","iopub.status.idle":"2024-11-27T09:46:10.415282Z","shell.execute_reply.started":"2024-11-27T09:46:10.383751Z","shell.execute_reply":"2024-11-27T09:46:10.414096Z"}},"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"         date  date_block_num  shop_id  item_id  item_price  item_cnt_day\n0  02.01.2013               0       59    22154      999.00           1.0\n1  03.01.2013               0       25     2552      899.00           1.0\n2  05.01.2013               0       25     2552      899.00          -1.0\n3  06.01.2013               0       25     2554     1709.05           1.0\n4  15.01.2013               0       25     2555     1099.00           1.0","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>date</th>\n      <th>date_block_num</th>\n      <th>shop_id</th>\n      <th>item_id</th>\n      <th>item_price</th>\n      <th>item_cnt_day</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>02.01.2013</td>\n      <td>0</td>\n      <td>59</td>\n      <td>22154</td>\n      <td>999.00</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>03.01.2013</td>\n      <td>0</td>\n      <td>25</td>\n      <td>2552</td>\n      <td>899.00</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>05.01.2013</td>\n      <td>0</td>\n      <td>25</td>\n      <td>2552</td>\n      <td>899.00</td>\n      <td>-1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>06.01.2013</td>\n      <td>0</td>\n      <td>25</td>\n      <td>2554</td>\n      <td>1709.05</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>15.01.2013</td>\n      <td>0</td>\n      <td>25</td>\n      <td>2555</td>\n      <td>1099.00</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":3},{"cell_type":"code","source":"# sales_train 내 월을 구분짓는 컬럼 date_block_num의 유니크 값 개수\nsales_train['date_block_num'].unique()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:10.417480Z","iopub.execute_input":"2024-11-27T09:46:10.417817Z","iopub.status.idle":"2024-11-27T09:46:10.446940Z","shell.execute_reply.started":"2024-11-27T09:46:10.417784Z","shell.execute_reply":"2024-11-27T09:46:10.445921Z"}},"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33])"},"metadata":{}}],"execution_count":4},{"cell_type":"markdown","source":"- 월별 판매량이 필요하므로 date 피처는 제거하고 월 구분자만 남기도록 함 : date_block_num 컬럼이 월을 차례대로 구분지어주고 있음","metadata":{}},{"cell_type":"code","source":"## 상점별 아이템별 가격의 unique 값 갯수\nsales_train.groupby(['shop_id','item_id'])['item_price'].nunique().sort_values(ascending = False).head(10)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:10.448113Z","iopub.execute_input":"2024-11-27T09:46:10.448475Z","iopub.status.idle":"2024-11-27T09:46:10.947963Z","shell.execute_reply.started":"2024-11-27T09:46:10.448443Z","shell.execute_reply":"2024-11-27T09:46:10.946822Z"}},"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"shop_id  item_id\n12       11373      427\n         11369      344\n         11370      335\n31       17717      288\n27       17717      229\n42       17717      209\n12       11365      207\n7        17717      177\n6        17717      173\n12       13753      151\nName: item_price, dtype: int64"},"metadata":{}}],"execution_count":5},{"cell_type":"markdown","source":"- 동일한 물건에 대해서 판매 가격이 달라질 수 있음","metadata":{}},{"cell_type":"code","source":"## 상점별 상품별 월별 판매 수량 합계\nsales_train.groupby(['shop_id', 'item_id','date_block_num'])['item_cnt_day'].sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:10.949327Z","iopub.execute_input":"2024-11-27T09:46:10.950062Z","iopub.status.idle":"2024-11-27T09:46:11.664954Z","shell.execute_reply.started":"2024-11-27T09:46:10.950014Z","shell.execute_reply":"2024-11-27T09:46:11.663860Z"}},"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"shop_id  item_id  date_block_num\n0        30       1                 31.0\n         31       1                 11.0\n         32       0                  6.0\n                  1                 10.0\n         33       0                  3.0\n                                    ... \n59       22164    27                 2.0\n                  30                 1.0\n         22167    9                  1.0\n                  11                 2.0\n                  17                 1.0\nName: item_cnt_day, Length: 1609124, dtype: float64"},"metadata":{}}],"execution_count":6},{"cell_type":"code","source":"## 상점별 아이템별 판매기간\nsales_train.groupby(['shop_id', 'item_id'])['date_block_num'].count()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:11.666128Z","iopub.execute_input":"2024-11-27T09:46:11.666443Z","iopub.status.idle":"2024-11-27T09:46:11.923858Z","shell.execute_reply.started":"2024-11-27T09:46:11.666414Z","shell.execute_reply":"2024-11-27T09:46:11.922685Z"}},"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"shop_id  item_id\n0        30          9\n         31          7\n         32         11\n         33          6\n         35         12\n                    ..\n59       22154       1\n         22155       1\n         22162      16\n         22164       6\n         22167       4\nName: date_block_num, Length: 424124, dtype: int64"},"metadata":{}}],"execution_count":7},{"cell_type":"code","source":"test.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:11.925102Z","iopub.execute_input":"2024-11-27T09:46:11.925526Z","iopub.status.idle":"2024-11-27T09:46:11.935471Z","shell.execute_reply.started":"2024-11-27T09:46:11.925483Z","shell.execute_reply":"2024-11-27T09:46:11.934275Z"}},"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"   ID  shop_id  item_id\n0   0        5     5037\n1   1        5     5320\n2   2        5     5233\n3   3        5     5232\n4   4        5     5268","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>shop_id</th>\n      <th>item_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>5</td>\n      <td>5037</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>5</td>\n      <td>5320</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>5</td>\n      <td>5233</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>5</td>\n      <td>5232</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>5</td>\n      <td>5268</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":8},{"cell_type":"markdown","source":"- item_cnt_day의 값은 판매 수량이므로 월별 판매량은 상점의 상품별 일일 판매량을 월별로 합쳐야 **각 상점의 상품별 월간 판매량**\n- 타깃값 : date_block_num 피처 값이 같은 데이터들의 item_cnt_day 값의 합","metadata":{}},{"cell_type":"code","source":"sales_train.info(show_counts=True)  # 비결측값 개수를 표기하는 옵션","metadata":{"papermill":{"duration":0.397857,"end_time":"2021-09-11T07:42:51.039998","exception":false,"start_time":"2021-09-11T07:42:50.642141","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:11.937013Z","iopub.execute_input":"2024-11-27T09:46:11.937462Z","iopub.status.idle":"2024-11-27T09:46:12.110571Z","shell.execute_reply.started":"2024-11-27T09:46:11.937417Z","shell.execute_reply":"2024-11-27T09:46:12.109434Z"}},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2935849 entries, 0 to 2935848\nData columns (total 6 columns):\n #   Column          Non-Null Count    Dtype  \n---  ------          --------------    -----  \n 0   date            2935849 non-null  object \n 1   date_block_num  2935849 non-null  int64  \n 2   shop_id         2935849 non-null  int64  \n 3   item_id         2935849 non-null  int64  \n 4   item_price      2935849 non-null  float64\n 5   item_cnt_day    2935849 non-null  float64\ndtypes: float64(2), int64(3), object(1)\nmemory usage: 134.4+ MB\n","output_type":"stream"}],"execution_count":9},{"cell_type":"markdown","source":"- 모든 데이터에 결측값이 없는 정돈된 데이터\n- 메모리가 134MB 정도로 메모리 관리 필요","metadata":{}},{"cell_type":"markdown","source":"### 🔍 `shops` 데이터","metadata":{"papermill":{"duration":0.024635,"end_time":"2021-09-11T07:42:51.089586","exception":false,"start_time":"2021-09-11T07:42:51.064951","status":"completed"},"tags":[]}},{"cell_type":"code","source":"shops.head()","metadata":{"papermill":{"duration":0.038858,"end_time":"2021-09-11T07:42:51.15363","exception":false,"start_time":"2021-09-11T07:42:51.114772","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:12.115061Z","iopub.execute_input":"2024-11-27T09:46:12.115507Z","iopub.status.idle":"2024-11-27T09:46:12.124505Z","shell.execute_reply.started":"2024-11-27T09:46:12.115473Z","shell.execute_reply":"2024-11-27T09:46:12.123450Z"}},"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"                        shop_name  shop_id\n0   !Якутск Орджоникидзе, 56 фран        0\n1   !Якутск ТЦ \"Центральный\" фран        1\n2                Адыгея ТЦ \"Мега\"        2\n3  Балашиха ТРК \"Октябрь-Киномир\"        3\n4        Волжский ТЦ \"Волга Молл\"        4","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>shop_name</th>\n      <th>shop_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>!Якутск Орджоникидзе, 56 фран</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>!Якутск ТЦ \"Центральный\" фран</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Адыгея ТЦ \"Мега\"</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Балашиха ТРК \"Октябрь-Киномир\"</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Волжский ТЦ \"Волга Молл\"</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":10},{"cell_type":"markdown","source":"- `shop_name` 컬럼의 첫 단어를 추출해 피처를 만들 예정: 현재 데이터에서 상점명의 첫 단어는 상점이 위치한 도시를 나타냄  \n- `shop_id` 컬럼을 key로 train 데이터에 병합 가능  ","metadata":{}},{"cell_type":"code","source":"shops.info()","metadata":{"papermill":{"duration":0.040148,"end_time":"2021-09-11T07:42:51.219816","exception":false,"start_time":"2021-09-11T07:42:51.179668","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:12.125742Z","iopub.execute_input":"2024-11-27T09:46:12.126092Z","iopub.status.idle":"2024-11-27T09:46:12.139046Z","shell.execute_reply.started":"2024-11-27T09:46:12.126060Z","shell.execute_reply":"2024-11-27T09:46:12.137962Z"}},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 60 entries, 0 to 59\nData columns (total 2 columns):\n #   Column     Non-Null Count  Dtype \n---  ------     --------------  ----- \n 0   shop_name  60 non-null     object\n 1   shop_id    60 non-null     int64 \ndtypes: int64(1), object(1)\nmemory usage: 1.1+ KB\n","output_type":"stream"}],"execution_count":11},{"cell_type":"markdown","source":"- 상점은 60개로 결측값 없음","metadata":{}},{"cell_type":"markdown","source":"### 🔍 `items` 데이터","metadata":{"papermill":{"duration":0.02549,"end_time":"2021-09-11T07:42:51.272867","exception":false,"start_time":"2021-09-11T07:42:51.247377","status":"completed"},"tags":[]}},{"cell_type":"code","source":"items.head()","metadata":{"papermill":{"duration":0.039263,"end_time":"2021-09-11T07:42:51.338491","exception":false,"start_time":"2021-09-11T07:42:51.299228","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:12.140254Z","iopub.execute_input":"2024-11-27T09:46:12.140569Z","iopub.status.idle":"2024-11-27T09:46:12.155560Z","shell.execute_reply.started":"2024-11-27T09:46:12.140526Z","shell.execute_reply":"2024-11-27T09:46:12.154553Z"}},"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"                                           item_name  item_id  \\\n0          ! ВО ВЛАСТИ НАВАЖДЕНИЯ (ПЛАСТ.)         D        0   \n1  !ABBYY FineReader 12 Professional Edition Full...        1   \n2      ***В ЛУЧАХ СЛАВЫ   (UNV)                    D        2   \n3    ***ГОЛУБАЯ ВОЛНА  (Univ)                      D        3   \n4        ***КОРОБКА (СТЕКЛО)                       D        4   \n\n   item_category_id  \n0                40  \n1                76  \n2                40  \n3                40  \n4                40  ","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>item_name</th>\n      <th>item_id</th>\n      <th>item_category_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>! ВО ВЛАСТИ НАВАЖДЕНИЯ (ПЛАСТ.)         D</td>\n      <td>0</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>!ABBYY FineReader 12 Professional Edition Full...</td>\n      <td>1</td>\n      <td>76</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>***В ЛУЧАХ СЛАВЫ   (UNV)                    D</td>\n      <td>2</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>***ГОЛУБАЯ ВОЛНА  (Univ)                      D</td>\n      <td>3</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>***КОРОБКА (СТЕКЛО)                       D</td>\n      <td>4</td>\n      <td>40</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":12},{"cell_type":"markdown","source":"- `item_name` 컬럼은 제거 예정: 상품명에서는 유용한 정보를 얻기 힘듦  \n- `item_id` 컬럼을 key로 train 데이터에 병합 가능  ","metadata":{}},{"cell_type":"code","source":"items.info()","metadata":{"papermill":{"duration":0.045408,"end_time":"2021-09-11T07:42:51.409909","exception":false,"start_time":"2021-09-11T07:42:51.364501","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:12.156795Z","iopub.execute_input":"2024-11-27T09:46:12.157463Z","iopub.status.idle":"2024-11-27T09:46:12.171713Z","shell.execute_reply.started":"2024-11-27T09:46:12.157414Z","shell.execute_reply":"2024-11-27T09:46:12.170637Z"}},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 22170 entries, 0 to 22169\nData columns (total 3 columns):\n #   Column            Non-Null Count  Dtype \n---  ------            --------------  ----- \n 0   item_name         22170 non-null  object\n 1   item_id           22170 non-null  int64 \n 2   item_category_id  22170 non-null  int64 \ndtypes: int64(2), object(1)\nmemory usage: 519.7+ KB\n","output_type":"stream"}],"execution_count":13},{"cell_type":"markdown","source":"- 상품은 22,170개로 결측값 없음","metadata":{}},{"cell_type":"markdown","source":"### 🔍 `item_categories` 데이터","metadata":{"papermill":{"duration":0.026293,"end_time":"2021-09-11T07:42:51.464626","exception":false,"start_time":"2021-09-11T07:42:51.438333","status":"completed"},"tags":[]}},{"cell_type":"code","source":"item_categories.head()","metadata":{"papermill":{"duration":0.038888,"end_time":"2021-09-11T07:42:51.529988","exception":false,"start_time":"2021-09-11T07:42:51.4911","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:12.172978Z","iopub.execute_input":"2024-11-27T09:46:12.173316Z","iopub.status.idle":"2024-11-27T09:46:12.182940Z","shell.execute_reply.started":"2024-11-27T09:46:12.173283Z","shell.execute_reply":"2024-11-27T09:46:12.181879Z"}},"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"        item_category_name  item_category_id\n0  PC - Гарнитуры/Наушники                 0\n1         Аксессуары - PS2                 1\n2         Аксессуары - PS3                 2\n3         Аксессуары - PS4                 3\n4         Аксессуары - PSP                 4","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>item_category_name</th>\n      <th>item_category_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>PC - Гарнитуры/Наушники</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Аксессуары - PS2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Аксессуары - PS3</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Аксессуары - PS4</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Аксессуары - PSP</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":14},{"cell_type":"markdown","source":"- `item_category_name` 컬럼의 첫 단어를 추출해 피처를 만들 예정: 첫 단어는 대분류를 뜻함  \n- `item_category_id` 컬럼을 key로 items 데이터에 이어 train 데이터에 병합 가능  ","metadata":{}},{"cell_type":"code","source":"item_categories.info()","metadata":{"papermill":{"duration":0.042862,"end_time":"2021-09-11T07:42:51.599736","exception":false,"start_time":"2021-09-11T07:42:51.556874","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:12.184703Z","iopub.execute_input":"2024-11-27T09:46:12.185048Z","iopub.status.idle":"2024-11-27T09:46:12.199914Z","shell.execute_reply.started":"2024-11-27T09:46:12.185015Z","shell.execute_reply":"2024-11-27T09:46:12.198856Z"}},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 84 entries, 0 to 83\nData columns (total 2 columns):\n #   Column              Non-Null Count  Dtype \n---  ------              --------------  ----- \n 0   item_category_name  84 non-null     object\n 1   item_category_id    84 non-null     int64 \ndtypes: int64(1), object(1)\nmemory usage: 1.4+ KB\n","output_type":"stream"}],"execution_count":15},{"cell_type":"markdown","source":"- 상품 분류는 84개로 결측값 없음","metadata":{}},{"cell_type":"markdown","source":"### 🔍 `test` 데이터","metadata":{"papermill":{"duration":0.027033,"end_time":"2021-09-11T07:42:51.654956","exception":false,"start_time":"2021-09-11T07:42:51.627923","status":"completed"},"tags":[]}},{"cell_type":"code","source":"test.head()","metadata":{"papermill":{"duration":0.039968,"end_time":"2021-09-11T07:42:51.722226","exception":false,"start_time":"2021-09-11T07:42:51.682258","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:12.201256Z","iopub.execute_input":"2024-11-27T09:46:12.201611Z","iopub.status.idle":"2024-11-27T09:46:12.214665Z","shell.execute_reply.started":"2024-11-27T09:46:12.201577Z","shell.execute_reply":"2024-11-27T09:46:12.213553Z"}},"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"   ID  shop_id  item_id\n0   0        5     5037\n1   1        5     5320\n2   2        5     5233\n3   3        5     5232\n4   4        5     5268","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>shop_id</th>\n      <th>item_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>5</td>\n      <td>5037</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>5</td>\n      <td>5320</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>5</td>\n      <td>5233</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>5</td>\n      <td>5232</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>5</td>\n      <td>5268</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":16},{"cell_type":"markdown","source":"- 식별자인 id, shop_id, item_id로 각 상점의 상품별 월간 판매량이 y","metadata":{}},{"cell_type":"markdown","source":"### 🔍 데이터 병합: 병합 데이터 variable `train`","metadata":{"papermill":{"duration":0.027577,"end_time":"2021-09-11T07:42:51.777761","exception":false,"start_time":"2021-09-11T07:42:51.750184","status":"completed"},"tags":[]}},{"cell_type":"code","source":"train = sales_train.merge(shops, on='shop_id', how='left')\ntrain = train.merge(items, on='item_id', how='left')\ntrain = train.merge(item_categories, on='item_category_id', how='left')\n\ntrain.head()","metadata":{"papermill":{"duration":1.410432,"end_time":"2021-09-11T07:42:53.216199","exception":false,"start_time":"2021-09-11T07:42:51.805767","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:12.216175Z","iopub.execute_input":"2024-11-27T09:46:12.217002Z","iopub.status.idle":"2024-11-27T09:46:13.790611Z","shell.execute_reply.started":"2024-11-27T09:46:12.216951Z","shell.execute_reply":"2024-11-27T09:46:13.789479Z"}},"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"         date  date_block_num  shop_id  item_id  item_price  item_cnt_day  \\\n0  02.01.2013               0       59    22154      999.00           1.0   \n1  03.01.2013               0       25     2552      899.00           1.0   \n2  05.01.2013               0       25     2552      899.00          -1.0   \n3  06.01.2013               0       25     2554     1709.05           1.0   \n4  15.01.2013               0       25     2555     1099.00           1.0   \n\n                shop_name                                 item_name  \\\n0  Ярославль ТЦ \"Альтаир\"                         ЯВЛЕНИЕ 2012 (BD)   \n1     Москва ТРК \"Атриум\"  DEEP PURPLE  The House Of Blue Light  LP   \n2     Москва ТРК \"Атриум\"  DEEP PURPLE  The House Of Blue Light  LP   \n3     Москва ТРК \"Атриум\"  DEEP PURPLE  Who Do You Think We Are  LP   \n4     Москва ТРК \"Атриум\"   DEEP PURPLE 30 Very Best Of 2CD (Фирм.)   \n\n   item_category_id                   item_category_name  \n0                37                       Кино - Blu-Ray  \n1                58                       Музыка - Винил  \n2                58                       Музыка - Винил  \n3                58                       Музыка - Винил  \n4                56  Музыка - CD фирменного производства  ","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>date</th>\n      <th>date_block_num</th>\n      <th>shop_id</th>\n      <th>item_id</th>\n      <th>item_price</th>\n      <th>item_cnt_day</th>\n      <th>shop_name</th>\n      <th>item_name</th>\n      <th>item_category_id</th>\n      <th>item_category_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>02.01.2013</td>\n      <td>0</td>\n      <td>59</td>\n      <td>22154</td>\n      <td>999.00</td>\n      <td>1.0</td>\n      <td>Ярославль ТЦ \"Альтаир\"</td>\n      <td>ЯВЛЕНИЕ 2012 (BD)</td>\n      <td>37</td>\n      <td>Кино - Blu-Ray</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>03.01.2013</td>\n      <td>0</td>\n      <td>25</td>\n      <td>2552</td>\n      <td>899.00</td>\n      <td>1.0</td>\n      <td>Москва ТРК \"Атриум\"</td>\n      <td>DEEP PURPLE  The House Of Blue Light  LP</td>\n      <td>58</td>\n      <td>Музыка - Винил</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>05.01.2013</td>\n      <td>0</td>\n      <td>25</td>\n      <td>2552</td>\n      <td>899.00</td>\n      <td>-1.0</td>\n      <td>Москва ТРК \"Атриум\"</td>\n      <td>DEEP PURPLE  The House Of Blue Light  LP</td>\n      <td>58</td>\n      <td>Музыка - Винил</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>06.01.2013</td>\n      <td>0</td>\n      <td>25</td>\n      <td>2554</td>\n      <td>1709.05</td>\n      <td>1.0</td>\n      <td>Москва ТРК \"Атриум\"</td>\n      <td>DEEP PURPLE  Who Do You Think We Are  LP</td>\n      <td>58</td>\n      <td>Музыка - Винил</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>15.01.2013</td>\n      <td>0</td>\n      <td>25</td>\n      <td>2555</td>\n      <td>1099.00</td>\n      <td>1.0</td>\n      <td>Москва ТРК \"Атриум\"</td>\n      <td>DEEP PURPLE 30 Very Best Of 2CD (Фирм.)</td>\n      <td>56</td>\n      <td>Музыка - CD фирменного производства</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":17},{"cell_type":"markdown","source":"### 🔍 피처 요약표 만들기\n","metadata":{"papermill":{"duration":0.028144,"end_time":"2021-09-11T07:42:53.273543","exception":false,"start_time":"2021-09-11T07:42:53.245399","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def resumetable(df):\n    print(f'데이터 세트 형상: {df.shape}')\n    summary = pd.DataFrame(df.dtypes, columns=['데이터 타입'])\n    summary = summary.reset_index()\n    summary = summary.rename(columns={'index': '피처'})\n    summary['결측값 개수'] = df.isnull().sum().values\n    summary['고윳값 개수'] = df.nunique().values\n    summary['첫 번째 값'] = df.loc[0].values\n    summary['두 번째 값'] = df.loc[1].values\n    \n    return summary\n\nresumetable(train)","metadata":{"papermill":{"duration":0.045257,"end_time":"2021-09-11T07:42:53.347191","exception":false,"start_time":"2021-09-11T07:42:53.301934","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:13.791743Z","iopub.execute_input":"2024-11-27T09:46:13.792034Z","iopub.status.idle":"2024-11-27T09:46:15.641921Z","shell.execute_reply.started":"2024-11-27T09:46:13.792006Z","shell.execute_reply":"2024-11-27T09:46:15.640706Z"}},"outputs":[{"name":"stdout","text":"데이터 세트 형상: (2935849, 10)\n","output_type":"stream"},{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"                   피처   데이터 타입  결측값 개수  고윳값 개수                  첫 번째 값  \\\n0                date   object       0    1034              02.01.2013   \n1      date_block_num    int64       0      34                       0   \n2             shop_id    int64       0      60                      59   \n3             item_id    int64       0   21807                   22154   \n4          item_price  float64       0   19993                   999.0   \n5        item_cnt_day  float64       0     198                     1.0   \n6           shop_name   object       0      60  Ярославль ТЦ \"Альтаир\"   \n7           item_name   object       0   21807       ЯВЛЕНИЕ 2012 (BD)   \n8    item_category_id    int64       0      84                      37   \n9  item_category_name   object       0      84          Кино - Blu-Ray   \n\n                                     두 번째 값  \n0                                03.01.2013  \n1                                         0  \n2                                        25  \n3                                      2552  \n4                                     899.0  \n5                                       1.0  \n6                       Москва ТРК \"Атриум\"  \n7  DEEP PURPLE  The House Of Blue Light  LP  \n8                                        58  \n9                            Музыка - Винил  ","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>피처</th>\n      <th>데이터 타입</th>\n      <th>결측값 개수</th>\n      <th>고윳값 개수</th>\n      <th>첫 번째 값</th>\n      <th>두 번째 값</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>date</td>\n      <td>object</td>\n      <td>0</td>\n      <td>1034</td>\n      <td>02.01.2013</td>\n      <td>03.01.2013</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>date_block_num</td>\n      <td>int64</td>\n      <td>0</td>\n      <td>34</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>shop_id</td>\n      <td>int64</td>\n      <td>0</td>\n      <td>60</td>\n      <td>59</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>item_id</td>\n      <td>int64</td>\n      <td>0</td>\n      <td>21807</td>\n      <td>22154</td>\n      <td>2552</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>item_price</td>\n      <td>float64</td>\n      <td>0</td>\n      <td>19993</td>\n      <td>999.0</td>\n      <td>899.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>item_cnt_day</td>\n      <td>float64</td>\n      <td>0</td>\n      <td>198</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>shop_name</td>\n      <td>object</td>\n      <td>0</td>\n      <td>60</td>\n      <td>Ярославль ТЦ \"Альтаир\"</td>\n      <td>Москва ТРК \"Атриум\"</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>item_name</td>\n      <td>object</td>\n      <td>0</td>\n      <td>21807</td>\n      <td>ЯВЛЕНИЕ 2012 (BD)</td>\n      <td>DEEP PURPLE  The House Of Blue Light  LP</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>item_category_id</td>\n      <td>int64</td>\n      <td>0</td>\n      <td>84</td>\n      <td>37</td>\n      <td>58</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>item_category_name</td>\n      <td>object</td>\n      <td>0</td>\n      <td>84</td>\n      <td>Кино - Blu-Ray</td>\n      <td>Музыка - Винил</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":18},{"cell_type":"markdown","source":"- id와 name 둘 중 하나만 있어도 됨: 상점, 상품, 상품 분류의 id와 name 고윳값 개수가 동일하므로 1:1 관계","metadata":{}},{"cell_type":"markdown","source":"## 9.2.2 데이터 시각화","metadata":{"papermill":{"duration":0.029142,"end_time":"2021-09-11T07:42:56.660787","exception":false,"start_time":"2021-09-11T07:42:56.631645","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"### 🔍 일별 판매량","metadata":{"papermill":{"duration":0.029501,"end_time":"2021-09-11T07:42:56.719566","exception":false,"start_time":"2021-09-11T07:42:56.690065","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import seaborn as sns\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nsns.boxplot(y='item_cnt_day', data=train);","metadata":{"papermill":{"duration":2.263614,"end_time":"2021-09-11T07:42:59.013178","exception":false,"start_time":"2021-09-11T07:42:56.749564","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:15.643403Z","iopub.execute_input":"2024-11-27T09:46:15.643740Z","iopub.status.idle":"2024-11-27T09:46:17.114984Z","shell.execute_reply.started":"2024-11-27T09:46:15.643708Z","shell.execute_reply":"2024-11-27T09:46:17.113850Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}],"execution_count":19},{"cell_type":"markdown","source":"- 1000 이상의 데이터를 제거할 계획","metadata":{}},{"cell_type":"markdown","source":"### 🔍 상품 가격","metadata":{"papermill":{"duration":0.030111,"end_time":"2021-09-11T07:42:59.074223","exception":false,"start_time":"2021-09-11T07:42:59.044112","status":"completed"},"tags":[]}},{"cell_type":"code","source":"sns.boxplot(y='item_price', data=train);","metadata":{"papermill":{"duration":1.291327,"end_time":"2021-09-11T07:43:00.401743","exception":false,"start_time":"2021-09-11T07:42:59.110416","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:17.116488Z","iopub.execute_input":"2024-11-27T09:46:17.116935Z","iopub.status.idle":"2024-11-27T09:46:18.397316Z","shell.execute_reply.started":"2024-11-27T09:46:17.116889Z","shell.execute_reply":"2024-11-27T09:46:18.396145Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}],"execution_count":20},{"cell_type":"markdown","source":"- 50000 이상의 데이터를 제거할 계획","metadata":{}},{"cell_type":"markdown","source":"### 🔍 그룹화\n\n월 별 월간 판매량: `date_block_num` 피처를 기준으로 그룹화 하여 `item_cnt_day` 피처 값을 구하는 코드","metadata":{"papermill":{"duration":0.030393,"end_time":"2021-09-11T07:43:00.462796","exception":false,"start_time":"2021-09-11T07:43:00.432403","status":"completed"},"tags":[]}},{"cell_type":"code","source":"group = train.groupby('date_block_num').agg({'item_cnt_day': 'sum'})\ngroup.reset_index()","metadata":{"papermill":{"duration":0.103389,"end_time":"2021-09-11T07:43:00.596827","exception":false,"start_time":"2021-09-11T07:43:00.493438","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:18.398529Z","iopub.execute_input":"2024-11-27T09:46:18.398886Z","iopub.status.idle":"2024-11-27T09:46:18.470747Z","shell.execute_reply.started":"2024-11-27T09:46:18.398854Z","shell.execute_reply":"2024-11-27T09:46:18.469627Z"}},"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"    date_block_num  item_cnt_day\n0                0      131479.0\n1                1      128090.0\n2                2      147142.0\n3                3      107190.0\n4                4      106970.0\n5                5      125381.0\n6                6      116966.0\n7                7      125291.0\n8                8      133332.0\n9                9      127541.0\n10              10      130009.0\n11              11      183342.0\n12              12      116899.0\n13              13      109687.0\n14              14      115297.0\n15              15       96556.0\n16              16       97790.0\n17              17       97429.0\n18              18       91280.0\n19              19      102721.0\n20              20       99208.0\n21              21      107422.0\n22              22      117845.0\n23              23      168755.0\n24              24      110971.0\n25              25       84198.0\n26              26       82014.0\n27              27       77827.0\n28              28       72295.0\n29              29       64114.0\n30              30       63187.0\n31              31       66079.0\n32              32       72843.0\n33              33       71056.0","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>date_block_num</th>\n      <th>item_cnt_day</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>131479.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>128090.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>147142.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>107190.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>106970.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5</td>\n      <td>125381.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>6</td>\n      <td>116966.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>7</td>\n      <td>125291.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>8</td>\n      <td>133332.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>9</td>\n      <td>127541.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>10</td>\n      <td>130009.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>11</td>\n      <td>183342.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>12</td>\n      <td>116899.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>13</td>\n      <td>109687.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>14</td>\n      <td>115297.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>15</td>\n      <td>96556.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>16</td>\n      <td>97790.0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>17</td>\n      <td>97429.0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>18</td>\n      <td>91280.0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>19</td>\n      <td>102721.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>20</td>\n      <td>99208.0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>21</td>\n      <td>107422.0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>22</td>\n      <td>117845.0</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>23</td>\n      <td>168755.0</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>24</td>\n      <td>110971.0</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>25</td>\n      <td>84198.0</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>26</td>\n      <td>82014.0</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>27</td>\n      <td>77827.0</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>28</td>\n      <td>72295.0</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>29</td>\n      <td>64114.0</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>30</td>\n      <td>63187.0</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>31</td>\n      <td>66079.0</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>32</td>\n      <td>72843.0</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>33</td>\n      <td>71056.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":21},{"cell_type":"markdown","source":"#### `groupby()`에 적용할 수 있는 집계 함수 예시\n\n- 결측값이 모든 값에 대한 집계\n\n|함수|의미|\n|---|---|\n|sum|합|\n|mean|평균|\n|median|중간값|\n|std|표준편차|\n|var|분산|\n|count|개수|\n|min|최솟값|\n|max|최댓값|\n","metadata":{}},{"cell_type":"markdown","source":"### 🔍 월별 판매량","metadata":{"papermill":{"duration":0.031113,"end_time":"2021-09-11T07:43:00.659695","exception":false,"start_time":"2021-09-11T07:43:00.628582","status":"completed"},"tags":[]}},{"cell_type":"code","source":"mpl.rc('font', size=13)\nfigure, ax = plt.subplots() \nfigure.set_size_inches(11, 5)\n\n# 월별 총 상품 판매량\ngroup_month_sum = train.groupby('date_block_num').agg({'item_cnt_day': 'sum'})\ngroup_month_sum = group_month_sum.reset_index()\n\n# 월별 총 상품 판매량 막대그래프\nsns.barplot(x='date_block_num', y='item_cnt_day', data=group_month_sum)\n\n# 그래프 제목, x축 라벨, y축 라벨명 설정\nax.set(title='Distribution of monthly item counts by date block number',\n       xlabel='Date block number', \n       ylabel='Monthly item counts');","metadata":{"papermill":{"duration":0.625596,"end_time":"2021-09-11T07:43:01.316916","exception":false,"start_time":"2021-09-11T07:43:00.69132","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:18.471959Z","iopub.execute_input":"2024-11-27T09:46:18.472435Z","iopub.status.idle":"2024-11-27T09:46:19.039345Z","shell.execute_reply.started":"2024-11-27T09:46:18.472379Z","shell.execute_reply":"2024-11-27T09:46:19.038211Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1100x500 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":22},{"cell_type":"markdown","source":"- 12월에 판매량이 높음: 11과 23이 각각 2013년 12월과 2014년 12월로 급증세를 보임","metadata":{}},{"cell_type":"markdown","source":"### 🔍 상품분류별 판매량\n\n판매량이 10000개를 초과하는 상품에 대해서만 상품 분류별 판매량 탐색","metadata":{"papermill":{"duration":0.032102,"end_time":"2021-09-11T07:43:01.381471","exception":false,"start_time":"2021-09-11T07:43:01.349369","status":"completed"},"tags":[]}},{"cell_type":"code","source":"figure, ax= plt.subplots() \nfigure.set_size_inches(11, 5)\n\n# 상품분류별 총 상품 판매량\ngroup_cat_sum = train.groupby('item_category_id').agg({'item_cnt_day': 'sum'})\ngroup_cat_sum = group_cat_sum.reset_index()\n\n# 총 판매량이 10,000개를 초과하는 상품분류만 추출\ngroup_cat_sum = group_cat_sum[group_cat_sum['item_cnt_day'] > 10000]\n\n# 상품분류별 총 상품 판매량 막대그래프\nsns.barplot(x='item_category_id', y='item_cnt_day', data=group_cat_sum)\nax.set(title='Distribution of total item counts by item category id',\n       xlabel='Item category ID', \n       ylabel='Total item counts')\nax.tick_params(axis='x', labelrotation=90)","metadata":{"papermill":{"duration":0.909324,"end_time":"2021-09-11T07:43:02.323389","exception":false,"start_time":"2021-09-11T07:43:01.414065","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:19.040683Z","iopub.execute_input":"2024-11-27T09:46:19.041026Z","iopub.status.idle":"2024-11-27T09:46:19.685368Z","shell.execute_reply.started":"2024-11-27T09:46:19.040993Z","shell.execute_reply":"2024-11-27T09:46:19.684248Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1100x500 with 1 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"},"metadata":{}}],"execution_count":23},{"cell_type":"code","source":"# 내림차순\n# y값 내림차순 정렬 및 xtick order로 활용\ngroup_cat_sum1 = group_cat_sum.sort_values(by='item_cnt_day', ascending=False)\ngroup_cat_sum1['item_category_id'] = group_cat_sum1['item_category_id'].astype(str)\n\nfigure, ax = plt.subplots()\nfigure.set_size_inches(11, 5)\n\nsns.barplot(x='item_category_id', y='item_cnt_day', data=group_cat_sum1, order=group_cat_sum1['item_category_id'], ax=ax)\nax.set(title='Distribution of total item counts by item category id (orderd)',\n       xlabel='Item category ID', \n       ylabel='Total item counts')\nax.tick_params(axis='x', labelrotation=90)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:19.686695Z","iopub.execute_input":"2024-11-27T09:46:19.687015Z","iopub.status.idle":"2024-11-27T09:46:20.281525Z","shell.execute_reply.started":"2024-11-27T09:46:19.686983Z","shell.execute_reply":"2024-11-27T09:46:20.280421Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1100x500 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":24},{"cell_type":"markdown","source":"- id 40, 30, 35 등의 순서로 판매량이 높음","metadata":{}},{"cell_type":"markdown","source":"### 🔍 상점별 판매량","metadata":{"papermill":{"duration":0.033112,"end_time":"2021-09-11T07:43:02.389763","exception":false,"start_time":"2021-09-11T07:43:02.356651","status":"completed"},"tags":[]}},{"cell_type":"code","source":"figure, ax= plt.subplots() \nfigure.set_size_inches(11, 5)\n\n# 상점별 총 상품 판매량\ngroup_shop_sum = train.groupby('shop_id').agg({'item_cnt_day': 'sum'})\ngroup_shop_sum = group_shop_sum.reset_index()\n\ngroup_shop_sum = group_shop_sum[group_shop_sum['item_cnt_day'] > 10000]\n\n# 상점별 총 상품 판매량 막대그래프\nsns.barplot(x='shop_id', y='item_cnt_day', data=group_shop_sum)\nax.set(title='Distribution of total item counts by shop id',\n       xlabel='Shop ID', \n       ylabel='Total item counts')\nax.tick_params(axis='x', labelrotation=90)","metadata":{"papermill":{"duration":0.891601,"end_time":"2021-09-11T07:43:03.314547","exception":false,"start_time":"2021-09-11T07:43:02.422946","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:20.283117Z","iopub.execute_input":"2024-11-27T09:46:20.283585Z","iopub.status.idle":"2024-11-27T09:46:21.121593Z","shell.execute_reply.started":"2024-11-27T09:46:20.283538Z","shell.execute_reply":"2024-11-27T09:46:21.120419Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1100x500 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAA84AAAHlCAYAAAAk4aWEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAACOF0lEQVR4nOzdd3QU1fvH8c+G9EoJNUDoVaQGKSK9SBEBIQJKBwsGKeoXEBQQxUoXla4gqAiIUmw0kaIRKYoovSUIhJIYQhIg9/cHZ/fHspslWYIJ+H6dkwN755m7z8zO7s6zM3PHYowxAgAAAAAATnlkdwIAAAAAAORkFM4AAAAAALhA4QwAAAAAgAsUzgAAAAAAuEDhDAAAAACACxTOAAAAAAC4QOEMAAAAAIALFM4AAAAAALhA4QwAAAAAgAsUzgDuCiVKlJDFYrH9eXh4KCQkRCVKlFCbNm306quv6tixY+nOP3/+fFksFvXq1evfSzodvXr1ksVi0fz58zPUnl1yWj5ZJS4uTn369FFYWJg8PT1lsVg0ZsyY7E4rS40ZMybTy3W3vt64dTnp8zOjGjVqJIvFog0bNmR3Kpl25MgRWSwWlShRIlPzbdiwQRaLRY0aNboteQF3OwpnAHeVli1bqmfPnurRo4datGihokWLasOGDRo1apRKliypZ599VsnJybflud3dmcmJ7qZlyax+/fpp3rx58vf3V5cuXdSzZ09Vq1btpvNZf7TJSnfCji4Fdc51O7ZJAPiv8szuBAAgKw0fPtyhyEhJSdGCBQv0/PPPa+rUqTp06JBWrFghD4///+2wQ4cOqlOnjkJCQv7ljB1NmDBBw4cPV+HChbM7FZfulDwzIzU1VatWrZKfn5927NihwMDA7E4px7gbX2/gThQWFqa9e/fKy8sru1MB/lMonAHc9Xx8fNSvXz/dd999uu+++7Ry5UrNmTNH/fv3t8WEhITkiKJZkgoXLnxHFCd3Sp6Z8ffff+vKlSsKCwujaL7B3fh6A3ciLy8vVahQIbvTAP5zOFUbwH9GlSpVNHjwYEnSxIkT7aa5ukZvxYoVat68uYoWLSofHx8VKFBA1apV09ChQ3XmzBlJ164ZLVmypCTp6NGjdtdbX3+68/Wntf766696+OGHVaBAAXl4eOiLL75wiEnPr7/+qnbt2ilfvnwKCAhQnTp19NlnnzmNdXW6prNTst1ZlhulpqZq8uTJqlWrloKCguTv7697771Xr7zyihITEx3ir1//8fHxevbZZ1WsWDH5+PiodOnSGjt2rK5cuZLu+kjPP//8o7Fjx6pKlSry9/dXUFCQIiIiNHXqVF2+fNlhPYWHhztdblesuV/fT3rzpqWlaf78+WrQoIFy584tX19flS9fXs8//7zi4uLsYnv16qXGjRtLkjZu3GjX5/VnVezZs0ejR49W3bp1VbhwYXl7e6tQoULq0KGDNm/enOl1lh5nr7fFYtGHH34oSerdu7ddjjduF2fOnNHw4cNVuXJl22tRp04dzZ49W8YYh+e7/hrUjRs3qlmzZgoJCVGePHn08MMPa//+/ZKurdN33nlHlStXlp+fn8LCwvS///1PqampmV7GtLQ0ffzxx2revLlCQ0Pl4+OjYsWKqXXr1vr4448d4jOzfaW3DtNb5vTat27dqlatWil37tzy9/fX/fffr7Vr19rFZ3SbvHr1qj766CPdf//9Kly4sHx8fFSoUCHdd999evHFF926rOXMmTN64oknFBYWZtu+X3vtNYfXo0WLFrJYLPr888/T7evhhx+WxWJxuu6dOXDggJ544gmVL19eAQEBCg4OVunSpRUZGemwjq6XkXV6vcOHD2vAgAEqUaKEfHx8lC9fPrVs2VIrV650Gn/967d27Vo1adJEISEhCgoKUuPGjbVu3boMLZ/VzS6lWbt2rRo3bqygoCDlzp1bTZo0cbk8ADKGI84A/lO6deumCRMm6M8//1RsbKyKFCniMv6ll17SK6+8Ii8vL91///164IEHdO7cOR08eFCTJk1Sly5dlD9/flWrVk2dOnXS0qVLFRAQoEceecTWR2hoqEO/P/74o5544gmVKFFCTZs2VVxcXIZPu9u2bZuefPJJFS9eXC1atFBsbKx+/PFHRUZG6sCBAxo5cmTmVsoNMrssN7p06ZJatmypTZs2KSgoSE2aNJGXl5c2bNigl156SUuWLNG6deuc9nXhwgXVrVtX586dU4MGDXThwgVt2rRJY8aMUUxMjGbOnJnh5Th9+rQaN26sP/74Q6GhoWrdurUuX76sdevW6dlnn9Xy5cu1Zs0a+fr6SpJ69uypxMREp8vtSpkyZdSzZ09bAdmzZ0+nccYYde3aVZ999pl8fHzUuHFjBQcHa/PmzXr77bf16aefat26dSpTpowk6f7779fff/+tb775RgULFlSrVq1sfV1/tGnSpEmaO3euKlWqpBo1asjf31/79u3TF198oa+++koLFy7Uo48+muH1lhk9e/bUjz/+qIMHD6p+/fq23CXZ/X/Xrl1q1aqV/v77b4WHh6tFixZKSkrStm3b1L9/f61fvz7d4mjFihWaNm2aateurZYtW2r79u1asWKFfvrpJ+3evVtPPfWUvvnmGzVu3FglS5bUhg0b9Oabb+rMmTOaO3duhpclJSVFHTt21OrVq+Xl5aV69eqpSJEiio2N1bZt2/THH3+oe/futvjMbl9ZYdWqVZo8ebKqVq2qVq1aac+ePdq8ebNatWqltWvX6oEHHpCU8W2yd+/eWrBgga1YzJcvn06fPq19+/bptddeU1RUlAoVKpTh/M6dO6f77rtPCQkJatSokVJSUrRu3Tq9+OKL2rRpk1auXKlcuXJJkgYOHKjvvvtO77//vtP32okTJ7Ry5UqFhoZm6L24e/du1a9fX4mJiapUqZIefPBBGWN0/PhxLV++XHny5FHTpk3dXqdWW7Zs0YMPPqiEhASVLVtWHTt21N9//621a9fq22+/1fDhwzVhwgSnOS5btkzvvvuuqlSpojZt2ujgwYO2H4Y++ugjPfbYYxlZzS4tWLBAPXv2lDFGtWvXVunSpbVnzx61aNFCAwcOvOX+gf80AwB3gfDwcCPJrF+/3mXc1atXjbe3t5FkvvvuO1v7vHnzjCTTs2dPW9ulS5eMr6+vCQwMNPv373foa+fOnebUqVO2x4cPHzaSTHh4eLrP37NnTyPJSDJjx441aWlp6cbMmzcv3XmHDh1qrl69apv2/fffGx8fH+Ph4WF+/fVXu/ms8ziTXs6ZWZYb8xw2bJiRZKpWrWq3fuLj403jxo2NJNOlSxe7eazrX5Lp0KGDuXTpkm3atm3bTK5cuYzFYjGHDx9ON58bderUyUgyLVq0MAkJCbb22NhYU7lyZSPJvPDCC5le7vS4Ws/GGDNt2jQjyRQrVsxue0pOTjbdunUzkkzt2rXt5lm/fr2RZBo2bJhuvxs2bDBHjhxxaF+1apXx8vIyefLkMRcvXrSb9vLLLxtJ5uWXX87Ywpmbb5c3tltdvHjRlChRwkgyEydOtNtuT5w4YWrUqGEkmTlz5tjN17BhQyPJeHh4mOXLl9vak5OTbdtR5cqVTcWKFc3Jkydt03fv3m28vLwyvb0888wzRpKpUqWKOXTokN205ORks3r1ars2d7avm60r6zLf+DlmbbdYLGbx4sW29rS0NFvejRs3dujP1TZ55MgRI8kUL17cnD592mH65s2bHbab9Fz//n3ggQdMfHy8bdrRo0dNqVKljCQzZcoUW/uVK1dMeHi4sVgsZt++fQ59jh492uk6TE+vXr2MJPP66687TDt79qzZvn27XZs76/TSpUumaNGiRpIZOXKk3ef35s2bTWBgoJHksK1Yn0uSmTx5st20BQsWGEkmICDAxMTEZGhZ0/ucOnHihAkICDCSzMKFC+2mvf3227YcXH2eAEgfhTOAu0JGC2djjClUqJCRZD755BNbm7PC+fTp07YCMCMyU2xWrFjRroBwFpNegVK0aFGTkpLiMN+TTz5pJJk+ffrYtf+bhXNSUpJtx+3HH390mGf//v0mV65cxsPDwxw9etTWbl3/QUFBTnfi27RpYySZ+fPnp5vP9Y4cOWIsFovx8vJyWjxZC9LAwEC7Iv12Fs4lS5Z0ukNrjDHnz583ISEhRpLZtGmTQ57u7uhaC/KVK1fatf+bhfO7775rJJkePXo4nb59+3YjyVSvXt2u3VpsdO/e3WGeL774wra+r/8BzOrhhx/O1Pby999/Gy8vL+Pp6WkOHjx403h3t69bLZwjIyMd5jlz5oyRZLy9vU1qaqrdNFfb5M8//2wkmfbt27tc1oywvn8tFov5/fffHaZ/8sknRpIpVaqUXfuECROMJDNs2DC79suXL5siRYoYi8WSodfDGGNat25tJJkdO3ZkKN6ddfrhhx8aSaZ8+fJOP7+t76umTZs6fa4bfxizatWqlZFkxo0bl6Hc0/ucGjt2rJFkHnzwQafz1axZk8IZuAVc4wzgPyctLU2Sbnrtav78+VW8eHHt2rVLL7zwgvbt25dlOTz00EN2o3pnxiOPPCJvb2+Hdutpfj/88MMt5XYrtm/frosXL6p06dKqX7++w/QyZcrogQceUFpamjZt2uQwvWbNmsqfP79De/ny5SVJsbGxGcpj06ZNMsbogQcecHodYKNGjVSyZEklJiZq+/btGerzVpw4cUKHDx+Wt7e309Omc+fOrY4dO0q6dj1zZsXHx+vjjz/WCy+8oP79+6tXr17q1auXfv/9d0nK0m03s9asWSNJ6ty5s9Pp1atXV2BgoHbt2uX0mtoWLVo4tJUuXVrStUGSrNeBO5ue0e1l3bp1unz5sho1aqRSpUrdND67tq8HH3zQoS00NFR58+ZVamqqw3XyrlSoUEGBgYFatWqV3njjDR0/fvyW86tataoqV67s0N65c2f5+Pjo0KFDiomJsbX369dPPj4+mj9/vlJSUmztK1asUGxsrFq0aJGh10OSatWqJUl6+umntXbt2gxf456ZdWr9bH3sscecfn736dNHkrR582ZdvXrVYXq3bt2c5pBVn93Wz47rLylw9jwA3EPhDOA/5erVq7pw4YIkKW/evDeNX7hwoYoUKaK33npL5cuXV6FChdSxY0fNnj1bSUlJbudhHYTKHekNCGNtP3HihNt93yrrTrF1cDFnrDvC1+9AWxUrVszpPEFBQZJkt3N9O/PIatbnKF68uO0az6zKZ/ny5SpRooQee+wxvfXWW5o9e7Y+/PBDffjhh9q9e7ckKSEh4RayvzWHDh2SJLVr185hkCqLxSIPDw8lJiYqLS1NZ8+edZi/aNGiDm3WEc8LFSrkdH1ap2d0ezl27Jik//+B5maya/vKqveHdZ758+crMDBQw4cPV/HixVW8eHF169ZNn376qVuD8aX32eTh4WHL/frPp9DQUEVGRurs2bNasmSJrf29996TJD311FMZfu4XXnhBrVq10tatW9WsWTMFBwerfv36Gj16tG0gOWcys05v9roXLVpU3t7eSk5Odrot3+7Pbmt+N3seAO5hcDAA/yl79uyxHYm45557bhrfoEED7d+/X998842++eYbbdq0ScuXL9fy5cs1btw4bdq0ya0i2M/PL9Pz3A7Wo+85hbtH4f+rjh8/rm7duik5OVkvvviiunbtqhIlSsjf318Wi0UjR47UhAkTnI5a/W+xHnl76KGHlCdPHpexPj4+Dm2utoms2l5udvbJv+Vm78esfn906tRJTZs21apVq/Tdd99p06ZNWrx4sRYvXqwqVapo06ZNt/02fQMHDtRHH32k999/X4899pj279+vdevWqWjRomrbtm2G+wkICNCaNWv0yy+/aNWqVdq4caO2bdumLVu2aMKECXrvvffsbkFoxWcOgIyicAbwn7J48WJJUuXKlTM8Wqy/v786dOigDh06SLp2q6Inn3xSX3/9tYYPH27r899y9OhRp+1HjhyRJIWFhdm1e3l56fLly0pMTHS4N3FWnJ55PetzW48yOmOddmOed2MeN+Zz7NgxXb161elRUnfyWbVqlZKTk9WpUyeNHz/eYfqBAwfczDjrFCtWTH/99ZcGDRrkdFTjnKB48eKSMn5Ku7vbl/USC2e3ZJOy/v2YEblz51b37t1tp/f+8ccf6tmzp3755Re9/vrr6Y4Q7Ux6n01paWm2Zbtx+65du7YiIiK0efNm/fbbb5o/f76MMRowYEC6Z2e4UqtWLdtp28nJyZo5c6YGDx6sQYMGqUuXLrf0Q8DNXvcTJ04oNTVVvr6+Ts9oyuxntzv5/fXXXzp69KjTS2WszwPAPfzMBuA/47ffftPUqVMlScOGDXO7n/DwcI0ePVqSbKfCSv+/U+zOKY6Z8fnnnzu9R+yiRYskyeH2KdZbbv31118O83z77bdOn8PdZalZs6YCAgJ06NAhp/cQPnjwoDZt2iQPDw81aNAgU31nRoMGDWSxWPTDDz843VncuHGjDh8+rMDAQNWsWTNLntN6OzFn66xo0aIqWbKkUlNT9cknnzhMj4+P1/LlyyVJDRs2tLXf7HU4d+6cJOenm8bFxem7777L5FJk3s1ytN5Gy9X9erNb48aN5eXlpfXr1+vw4cM3jXd3+3L1Xvzzzz9tp4xnFVfbZHoqVaqkIUOGSLL/fMuInTt3au/evQ7tS5cuVUpKikqWLOn01HvrbZImT56s+fPny9PTU/369cvUczvj6+urQYMGqUyZMkpOTr7la/2tn60ff/yx07MD5s2bJ0mqX7++PD0dj02l9yNrep/d7uZn7e9GGb0fNgDnKJwB3PVSUlI0Z84cPfDAA0pKSlL79u3Tva/p9Y4ePao5c+bon3/+cZi2cuVKSf9/pEq6NpiYt7e3Tp06pfPnz2fdAtzg+PHjevHFF+1Ov92wYYPmzp0rDw8Ph3t1WgdPevXVV+12oL/99ltNmjTJ6XO4uyx+fn564oknJEnPPPOMzpw5Y5v2zz//6IknntCVK1f0yCOP2K27rBYeHq4OHTroypUrevLJJ+2O8J06dUpRUVGSrg0klFX32bUeLXJWOEiyFSMjRozQwYMHbe2pqal65plndOHCBdWuXVv333+/Q58HDhxwWvxY7+e8dOlSnTp1ytZ+8eJF9evXz3Y9/+10s+UeMGCAihYtqg8++ECvv/660+tw//jjDy1btuy25ulKwYIFNWDAAF25ckUdO3Z0ODKYkpJiG+RMcn/7sr4XFyxYYLcNnDp1Sn379s3ySydcvTY7duzQZ5995jAgmzFGq1evlqRMv0eNMRo4cKDdZ+aJEyc0YsQISbKtlxs9+uijCg0N1dy5c3Xu3Dm1b99ehQsXztRzz5gxw+m1zL/99puOHj0qDw8Pp0V7ZnTu3Nl2VPfll1+2+wz+6aef9M4770iShg4d6nT+bdu2afr06XZtixcv1urVq+Xv728bXMxdffv2lb+/v1atWuVQpE+ePFm//PLLLfUP/NdxqjaAu8rrr7+u+fPnS5KSkpL0999/69dff9XFixfl4eGhwYMHa8KECRm6ru38+fPq16+fBg4cqOrVq6tEiRK6cuWKdu/erX379ikwMFBjx461xXt5ealNmzZavny5qlevrvr168vPz0+hoaF6/fXXs2wZn3jiCU2ZMkUrVqxQzZo1dfLkSf3www9KS0vTuHHjHI6gDh8+XEuWLNHy5ctVsWJFVatWTUeOHNH27dv1v//9z2lut7Is48ePV3R0tDZt2qQyZcrYjuZt2LBBcXFxuueee/Tuu+9m2fpIz3vvvae9e/fqm2++UalSpdSwYUNdvnxZ69at0z///KNGjRrZvX63qkOHDpo0aZKaNm2qJk2a2E6Lnz17tqRrR9U2bdqkJUuW6J577lHjxo0VHByszZs368SJEypatKjDEaHw8HBVr15dO3bs0L333quaNWvKx8dH5cuX1/PPP6927dqpatWq2rVrl8qVK6dGjRrJ09NTP/zwgzw8PNS7d2/bUbDbpX379ho3bpwmT56s33//XUWLFpXFYlGfPn1Ur149BQUFaeXKlWrbtq1GjBihiRMn6t5771WhQoV04cIF/fbbbzp27JgiIyNtI4tnh7ffflv79+/Xt99+q3Llyql+/foqVKiQTp48qV27dik4ONju6LI729cDDzyg5s2b67vvvlP16tX1wAMP6PLly/rpp59UtWpV1atXT1u2bMmyZXK1TR49elSRkZEKCAhQzZo1FRYWpuTkZP3yyy86fvy4ChYsqBdeeCFTz9euXTv99ttvKlWqlBo1aqTU1FStXbtWFy9eVPPmzTVo0CCn8/n4+Khv37564403JGVuUDCrmTNnauDAgSpTpozuuece+fv7KyYmRps3b9aVK1f0/PPPZ7oYv5Gfn58+/fRTtW7dWuPHj9eSJUtUo0YNnTp1Shs3btTVq1c1fPhwtW7d2un8zzzzjAYNGqQ5c+aoYsWKOnTokH766SdZLBbNmDHjlgv7okWLasaMGerdu7e6deumKVOmqFSpUvrjjz+0e/duRUVFadq0abf0HMB/WvbdCQsAso71Ps7WP4vFYoKCgkx4eLhp3bq1efXVV82xY8fSnd/ZfZwTEhLMxIkTzUMPPWRKlSplAgICTHBwsKlUqZIZPHiw0/u3xsXFmb59+5qiRYsaT09Ph3tt3uw+rq5irm+Pjo42Dz74oMmdO7fx8/MzERERZvHixen2uX37dtOyZUsTFBRk/P39Td26dc2XX37p8r7Ft7IsKSkpZuLEiaZGjRomICDA+Pr6msqVK5uxY8eaf/75xyHe2fq/njv3HTbm2mv48ssvm8qVKxtfX18TEBBgatasaSZPnuz0Xti3ch/npKQkM3ToUFOyZEnj5eXl9B66V69eNXPnzjX169c3QUFBxtvb25QpU8YMGzbM6f2rrTl16dLFFCxY0OTKlcvhPqzx8fFmyJAhpkyZMsbHx8eEhYWZPn36mBMnTqS73rLyPs7GGLNkyRJz3333mcDAQNty3xh37tw588orr5hatWqZoKAg4+PjY4oXL24eeOAB89prr5kDBw7Yxad3T2PrOnH1Orm7vVy5csXMnTvXNGzY0OTOndt4e3ubYsWKmTZt2jh9f2V2+zLGmMTERDN48GATFhZmvL29TYkSJczIkSPNpUuXbnof5/TuU2/9/LvxM8nVNnny5Enz2muvmZYtW5rw8HDj6+tr8uTJY6pWrWpGjx5tTp06leH1dv379++//zZ9+vQxhQoVMt7e3qZs2bLmlVdeMcnJyS77+P77740kU65cOZOWlpbh57b66quvzIABA0zVqlVNvnz5jI+PjwkPDzdt27Y1q1evdoh3d50aY8zBgwdNv379TPHixY2Xl5fJkyePad68uVmxYoXTvq5/rm+++cY0bNjQBAUFmYCAANOwYUPz7bffZmpZb7b9f/vtt6Zhw4YmICDABAUF2Z7jVu8LD/zXWYzJxqE2AQAA8J/Xv39/zZ49WxMnTrRd1nC3aNSokTZu3Kj169erUaNG2Z0OADdxjTMAAACyzf79+7VgwQIFBwff8nW+AHC7cI0zAAAA/nXDhw/X8ePH9c033yglJUXjxo277feNBgB3UTgDAADgX/fJJ5/o2LFjKlq0qKKiovTcc89ld0oAkC6ucQYAAAAAwAWucQYAAAAAwAUKZwAAAAAAXOAa5ztUWlqaYmNjFRQUJIvFkt3pAAAAAMAdxRijf/75R0WKFJGHh+tjyhTOd6jY2FgVK1Ysu9MAAAAAgDva8ePHVbRoUZcxFM53qKCgIEnXXuTg4OBszgYAAAAA7iwJCQkqVqyYrbZyhcL5DmU9PTs4OJjCGQAAAADclJFLXxkcDAAAAAAAFyicAQAAAABwgcIZAAAAAAAXKJwBAAAAAHCBwhkAAAAAABconAEAAAAAcIHCGQAAAAAAFyicAQAAAABwgcIZAAAAAAAXKJwBAAAAAHCBwhkAAAAAABconAEAAAAAcIHCGQAAAAAAFyicAQAAAABwgcIZAAAAAAAXPLM7AQAAgJtp+/lnbs238pEuWZwJAOC/iCPOAAAAAAC4QOEMAAAAAIALFM4AAAAAALhA4QwAAAAAgAsUzgAAAAAAuEDhDAAAAACACzmqcP7rr7/UvXt3VaxYUSEhIfL391eFChU0dOhQnTx50mn8ww8/rDx58iggIEANGjTQunXrnPYdHx+vqKgohYWFydfXV5UrV9Z7770nY4xDbFpamiZNmqQKFSrI19dXxYoV07Bhw3Tx4kWnfa9evVr16tVTQECA8ubNq86dO+vw4cPpLmNGcwYAAAAAZL8cdR/nEydO6OTJk+rQoYOKFi0qT09P/fbbb5o5c6Y++eQT7dy5UwUKFJAkHTx4UPXq1ZOnp6deeOEFhYSEaNasWWrZsqXWrFmjZs2a2fpNTU1V8+bNtWPHDkVFRalixYpas2aNnn76aZ06dUpjxoyxy2PIkCGaOnWqOnTooGHDhmnv3r2aOnWqduzYoe+//14eHv//e8OyZcv0yCOPqGrVqnrrrbcUHx+vyZMnq379+vrll19UpEgRW2xmcgYAAAAA5AwW4+yQaw6zZMkSdenSRW+88YZeeOEFSVKXLl20dOlSbd++XdWqVZMkJSYmqnLlyvL19dWff/4pi8UiSZoxY4YGDhyoqVOnKioqytZvp06d9NVXX2n//v0KDw+XJO3Zs0dVqlRRhw4dtHTpUlvstGnTNGjQIH388cfq1q2bJOny5csqUaKEPD09tWfPHgUGBkqSdu7cqZo1a6pv376aOXOmrY/M5HwzCQkJCgkJUXx8vIKDg91YqwAA3Dnafv6ZW/OtfKRLFmcCALhbZKamylGnaqfHWtSeP39eknTx4kV9+eWXatSoka0AlaTAwED169dP+/btU3R0tK190aJF8vf3V//+/e36HTx4sC5fvqxPP/3U1rZ48WIZYzR48GC72P79+8vf318LFy60tW3cuFGxsbHq16+frWiWpGrVqqlRo0b69NNPdfnyZbdyBgAAAADkDDmycE5OTlZcXJxOnDihb7/9Vk888YQkqXXr1pKk3bt3KyUlRXXr1nWYt06dOpJkK0LT0tL066+/qnr16vL19bWLrV27tiwWi13BGh0dLQ8PD9WuXdsu1tfXV9WqVXOIlZRuHgkJCdq3b1+mcwYAAAAA5Bw5snCePXu28ufPr2LFiqlly5a6cOGCFi5cqAYNGkiSYmNjJUlhYWEO81rbYmJiJF07Sn3p0iWnsT4+PgoNDbXFWvsODQ2Vj4+P077j4uKUmpqa6TwyE+tMSkqKEhIS7P4AAAAAALdfjhoczOrhhx9WhQoVlJiYqB07dujLL79UXFycbXpSUpIkOS1urUeVrTGuYq3x1hhrvKtYa4y3t3eW5XFjrDMTJkzQ2LFj050OAAAAALg9cmThXLRoURUtWlTStSK6U6dOioiIUFJSkkaMGCF/f39J147C3ig5OVmSbDGuYq3x1hhr/OnTp9ONzWjftxLrzIgRIzR06FDb44SEBBUrVizdeAAAAABA1siRp2rf6N5771X16tU1Y8YMSbLd4snZqc3WNuvpz3ny5JGfn5/T2JSUFMXFxdmdPl2kSBHFxcU5LXBjYmIUGhoqb2/vTOeRmVhnfHx8FBwcbPcHAAAAALj97ojCWZIuXbqkc+fOSZKqVKkiHx8fbd261SFu27ZtkqRatWpJkjw8PFSjRg3t2LHDoRj++eefZYyxxUpSRESE0tLS9PPPP9vFJicna+fOnQ6xktLNIzg4WOXKlct0zgAAAACAnCNHFc5///230/b169fr999/t40+HRgYqHbt2mnDhg3atWuXLS4xMVGzZ89W2bJl7UbF7tq1q5KSkuzuqSxJkydPlqenpyIjI21tkZGRslgsmjx5sl3srFmzlJSUpO7du9vaGjZsqMKFC2v27NlKTEy0te/atUsbNmxQ586d5eXl5VbOAAAAAICcwWKMMdmdhFWHDh108uRJNWnSROHh4UpOTtb27dv1ySefyN/fXxs2bLDdA/nAgQOqXbu2vLy8NGTIEAUHB2vWrFn67bfftGrVKrVs2dLWb2pqqurVq6ddu3Zp0KBBqlixolavXq3ly5dr1KhReuWVV+zyiIqK0vTp09WhQwe1bt1ae/fu1dSpU1W/fn2tW7dOHh7//3vDkiVLFBkZqapVq6p///5KSEjQpEmTZLFYtH37drvTrzOT881k5mbdAADc6dp+/plb8618pEsWZwIAuFtkpqbKUYXzZ599po8++ki7du3SmTNnZLFYFB4erubNm+v5559X8eLF7eL37t2r4cOHa+PGjUpNTVWNGjU0ZswYNWvWzKHvCxcuaNSoUVq2bJnOnj2r0qVL6+mnn9bAgQNlsVjsYq9evarJkydr5syZOnLkiEJDQxUZGalx48YpMDDQoe+VK1dq/Pjx2r17t3x8fNS0aVO98cYbKl26tENsZnJ2hcIZAPBfQuEMAMhqd2zhjIyjcAYA/JdQOAMAslpmaqocdY0zAAAAAAA5DYUzAAAAAAAuUDgDAAAAAOAChTMAAAAAAC5QOAMAAAAA4AKFMwAAAAAALlA4AwAAAADgAoUzAAAAAAAuUDgDAAAAAOAChTMAAAAAAC5QOAMAAAAA4AKFMwAAAAAALlA4AwAAAADgAoUzAAAAAAAuUDgDAAAAAOAChTMAAAAAAC5QOAMAAAAA4AKFMwAAAAAALlA4AwAAAADgAoUzAAAAAAAuUDgDAAAAAOAChTMAAAAAAC5QOAMAAAAA4AKFMwAAAAAALlA4AwAAAADgAoUzAAAAAAAuUDgDAAAAAOAChTMAAAAAAC5QOAMAAAAA4AKFMwAAAAAALlA4AwAAAADgAoUzAAAAAAAuUDgDAAAAAOAChTMAAAAAAC5QOAMAAAAA4AKFMwAAAAAALlA4AwAAAADgAoUzAAAAAAAuUDgDAAAAAOAChTMAAAAAAC5QOAMAAAAA4AKFMwAAAAAALlA4AwAAAADgAoUzAAAAAAAuUDgDAAAAAOBCjiqc9+3bp5deekl16tRR/vz5FRQUpGrVqunVV1/VxYsX7WLHjBkji8Xi9O/tt9926DstLU2TJk1ShQoV5Ovrq2LFimnYsGEO/VqtXr1a9erVU0BAgPLmzavOnTvr8OHDTmP/+usvPfzww8qTJ48CAgLUoEEDrVu3zmlsfHy8oqKiFBYWJl9fX1WuXFnvvfeejDGZXFsAAAAAgH+DZ3YncL25c+fq3Xff1UMPPaTu3bvLy8tL69ev16hRo/TZZ59p27Zt8vPzs5tn0qRJCg0NtWurWbOmQ99DhgzR1KlT1aFDBw0bNkx79+7V1KlTtWPHDn3//ffy8Pj/3xCWLVumRx55RFWrVtVbb72l+Ph4TZ48WfXr19cvv/yiIkWK2GIPHjyoevXqydPTUy+88IJCQkI0a9YstWzZUmvWrFGzZs1ssampqWrevLl27NihqKgoVaxYUWvWrNHTTz+tU6dOacyYMVm0JgEAAAAAWcVictChzl9++UVly5ZVSEiIXfuoUaP06quvatq0aXrmmWckXTviPHbsWB0+fFglSpRw2e+ePXtUpUoVdejQQUuXLrW1T5s2TYMGDdLHH3+sbt26SZIuX76sEiVKyNPTU3v27FFgYKAkaefOnapZs6b69u2rmTNn2vro0qWLli5dqu3bt6tatWqSpMTERFWuXFm+vr76888/ZbFYJEkzZszQwIEDNXXqVEVFRdn66NSpk7766ivt379f4eHhGVpXCQkJCgkJUXx8vIKDgzM0DwAAd6q2n3/m1nwrH+mSxZkAAO4WmampctSp2rVq1XIomiUpMjJSkvT77787nS8hIUFXrlxJt9/FixfLGKPBgwfbtffv31/+/v5auHChrW3jxo2KjY1Vv379bEWzJFWrVk2NGjXSp59+qsuXL0uSLl68qC+//FKNGjWyFc2SFBgYqH79+mnfvn2Kjo62tS9atEj+/v7q37+/XR6DBw/W5cuX9emnn6a7DAAAAACA7JGjCuf0nDhxQpJUsGBBh2n33nuvQkJC5Ovrq3r16mnNmjUOMdHR0fLw8FDt2rXt2n19fVWtWjW74tb6/7p16zr0U6dOHSUkJGjfvn2SpN27dyslJSXd2Ov7S0tL06+//qrq1avL19fXLrZ27dqyWCx2eQAAAAAAcoYcXzhfvXpVr7zyijw9PW2nU0tS7ty5NWDAAE2bNk0rVqzQhAkTdPToUbVp00bz58+36yM2NlahoaHy8fFx6D8sLExxcXFKTU21xVrbncVKUkxMTKZjz58/r0uXLjmN9fHxUWhoqC3WmZSUFCUkJNj9AQAAAABuvxw1OJgzgwcP1tatW/Xaa6+pfPnydu036tOnj+655x4NGTJEjzzyiO1U66SkJKdFsyTb0d+kpCR5e3srKSlJkpzGXx97/b+3GmuNt8Y4M2HCBI0dOzbd6QAAAACA2yNHH3EePXq0pk+frgEDBmjEiBE3jc+XL5+efPJJXbhwQVu2bLG1+/v7KyUlxek8ycnJtpjr/3UWf7tirfHWGGdGjBih+Ph429/x48fTjQUAAAAAZJ0cWziPGTNG48ePV+/evfX+++9neD7rCNtxcXG2tiJFiiguLs5p0RoTE6PQ0FB5e3vbYq3tzmKl/z8NOzOxefLkkZ+fn9PYlJQUxcXFOT2N28rHx0fBwcF2fwAAAACA2y9HFs7WW0317NlTs2fPtt3OKSP2798vyX4gsYiICKWlpennn3+2i01OTtbOnTtVq1Ytu1hJ2rp1q0Pf27ZtU3BwsMqVKydJqlKlinx8fNKNlWTr28PDQzVq1NCOHTscCviff/5Zxhi7PAAAAAAAOUOOK5zHjRunsWPH6vHHH9fcuXPl4eGY4pUrVxQfH+/Qfvz4cb333nvKly+f6tWrZ2uPjIyUxWLR5MmT7eJnzZqlpKQkde/e3dbWsGFDFS5cWLNnz1ZiYqKtfdeuXdqwYYM6d+4sLy8vSdduO9WuXTtt2LBBu3btssUmJiZq9uzZKlu2rN1I3l27dlVSUpLdfaAlafLkyfL09LTddgsAAAAAkHPkqMHB3n33Xb388ssqXry4mjVrpkWLFtlNL1iwoJo3b67ExESVLFlSDz/8sCpWrKg8efLor7/+shW7ixcvlp+fn22+KlWqaODAgZo+fbo6duyo1q1ba+/evZo6daoaNmxoN1q3l5eXpkyZosjISDVo0ED9+/dXQkKCJk2apPz58zsM0DVhwgStXbtWLVq00JAhQxQcHKxZs2YpJiZGq1atsjta3r9/f82bN09Dhw7VkSNHVLFiRa1evVrLly/XqFGjbKeZAwAAAAByDosxxmR3Ela9evXShx9+mO70hg0basOGDUpJSdHAgQP1008/6cSJE0pMTFRoaKjq16+vF154weF+zdK121pNnjxZM2fO1JEjRxQaGqrIyEiNGzfONvr29VauXKnx48dr9+7d8vHxUdOmTfXGG2+odOnSDrF79+7V8OHDtXHjRqWmpqpGjRoaM2aMmjVr5hB74cIFjRo1SsuWLdPZs2dVunRpPf300xo4cGCmTklPSEhQSEiI4uPjud4ZAHDXa/v5Z27Nt/KRLlmcCQDgbpGZmipHFc7IOApnAMB/CYUzACCrZaamynHXOAMAAAAAkJPkqGucAQDIbq2Xj3drvtUdRmVxJgAAIKfgiDMAAAAAAC5QOAMAAAAA4AKFMwAAAAAALlA4AwAAAADgAoUzAAAAAAAuUDgDAAAAAOAChTMAAAAAAC5QOAMAAAAA4AKFMwAAAAAALlA4AwAAAADgAoUzAAAAAAAuUDgDAAAAAOAChTMAAAAAAC5QOAMAAAAA4AKFMwAAAAAALlA4AwAAAADgAoUzAAAAAAAuuFU4nz17Vnv37rVrO3z4sKKiotS9e3d98803WZIcAAAAAADZzdOdmZ599lnt27dPP//8syQpMTFRDRo0UGxsrCTp008/1bp16/TAAw9kXaYAAAAAAGQDt444b926Va1bt7Y9/vTTTxUbG6vVq1crNjZWFStW1JtvvpllSQIAAAAAkF3cKpxPnTqlYsWK2R6vWbNGtWrVUqtWrVSoUCH16tVLO3bsyLIkAQAAAADILm4Vzl5eXrp06ZLt8caNG9WwYUPb49y5c+vs2bO3nh0AAAAAANnMrcK5XLlyWrp0qYwx+vLLL3Xu3Dk1bdrUNv348ePKmzdvliUJAAAAAEB2cWtwsIEDB6pXr17KkyePkpKSVKpUKbvCedOmTapSpUqWJQkAAAAAQHZxq3Du0aOHLBaLvvjiC4WEhGjkyJHy8vKSdO1WVRcuXNDTTz+dpYkCAAAAAJAd3CqcJenxxx/X448/7tCeL18+bd++/ZaSAgAAAAAgp3DrGudSpUrpyy+/THf6ypUrVapUKbeTAgAAAAAgp3CrcD5y5IgSExPTnX7x4kUdPXrU7aQAAAAAAMgp3Cqcb+bUqVPy9/e/HV0DAAAAAPCvyvA1zj/88IM2bNhge7xs2TIdOHDAIe7cuXP65JNPVK1atazIDwAAAACAbJXhwnn9+vUaO3asJMlisWjZsmVatmyZ09gyZcpo0qRJWZMhAAAAAADZKMOF8+DBg9WrVy8ZY1SqVClNnjxZ7du3t4uxWCwKDAxU3rx5szxRAAAAAACyQ4YL55CQEIWEhEi6dvS5YsWKKlCgwG1LDAAAAACAnMCt+zg3bNgwq/MAAAAAACBHcqtwlqRjx47pgw8+0P79+3X27FkZY+ymWywWrV279pYTBAAAAAAgO7lVOK9Zs0YdOnRQamqqAgMDlS9fvqzOCwAAAACAHMGtwnnEiBEKDQ3VF198oVq1amV1TgAAAAAA5Bge7sz0559/avDgwRTNAAAAAIC7nluFc/78+eXt7Z3VuQAAAAAAkOO4VTg//vjjWrp0aVbnAgAAAABAjuPWNc69evXS+vXr1b59ez377LMqWbKkcuXK5RBXvHjxW04QAAAAAIDs5FbhXKFCBVksFhljtHLlynTjrl696nZiAAAAAADkBG4Vzi+99JIsFktW5wIAAAAAQI7jVuE8ZsyYLE7jmn379mnhwoX69ttvdfDgQSUnJ6t06dLq3LmzBg8erICAALv4v/76S//73/+0ceNGpaamqkaNGho7dqyaNGni0Hd8fLxGjRqlZcuW6ezZsypdurSeeeYZPfnkkw4/AqSlpWnKlCn64IMPdOTIEeXPn19dunTRuHHjHHKQpNWrV2v8+PHatWuXfHx81LRpU7355psqWbKkQ2xmcgYAAAAAZD+3Bge7XebOnatJkyapdOnSeumll/TWW2+pfPnyGjVqlOrVq6dLly7ZYg8ePKh69epp69ateuGFF/TWW28pMTFRLVu21Pfff2/Xb2pqqpo3b673339fkZGRmjZtmsqXL6+nn35aY8eOdchjyJAhGjp0qCpVqqRp06apc+fOmjp1qtq1a6e0tDS72GXLlqlt27a6dOmS3nrrLT3//PP64YcfVL9+fcXGxtrFZiZnAAAAAEDOYDHGmMzO9MMPP2Qo7oEHHshUv7/88ovKli2rkJAQu/ZRo0bp1Vdf1bRp0/TMM89Ikrp06aKlS5dq+/btqlatmiQpMTFRlStXlq+vr/7880/bkeQZM2Zo4MCBmjp1qqKiomz9durUSV999ZX279+v8PBwSdKePXtUpUoVdejQwW7k8GnTpmnQoEH6+OOP1a1bN0nS5cuXVaJECXl6emrPnj0KDAyUJO3cuVM1a9ZU3759NXPmTFsfmcn5ZhISEhQSEqL4+HgFBwdndBUDAG6i9fLxbs23usOoLM4E12v7+WduzbfykS5ZnAkA4G6RmZrKrSPOjRo1UuPGjW/6l1m1atVyKJolKTIyUpL0+++/S5IuXryoL7/8Uo0aNbIVoJIUGBiofv36ad++fYqOjra1L1q0SP7+/urfv79dv4MHD9bly5f16aef2toWL14sY4wGDx5sF9u/f3/5+/tr4cKFtraNGzcqNjZW/fr1sxXNklStWjU1atRIn376qS5fvuxWzgAAAACAnMGta5znzZvn0HblyhUdPHhQ8+fPV4kSJfTEE0/ccnJWJ06ckCQVLFhQkrR7926lpKSobt26DrF16tSRJEVHR6t27dpKS0vTr7/+qho1asjX19cutnbt2rJYLHYFa3R0tDw8PFS7dm27WF9fX1WrVs0hVlK6eaxbt0779u1T5cqVM5UzAAAAACDncKtw7tmzZ7rTnn/+edWoUcPthG509epVvfLKK/L09LSdIm29djgsLMwh3toWExMjSTp//rwuXbrkNNbHx0ehoaG2WGvfoaGh8vHxcdr3li1blJqaKm9v7wznUbly5Uzl7ExKSopSUlJsjxMSEtKNBQAAAABknSwfHCxPnjzq16+f3nzzzSzpb/Dgwdq6davGjRun8uXLS5KSkpIkyWlxaz2qbI1xFWuNt8ZY413FZrTvW4l1ZsKECQoJCbH9FStWLN1YAAAAAEDWuS2jaufJk0eHDh265X5Gjx6t6dOna8CAARoxYoSt3d/fX5LsjsBaJScn28W4irXGW2Os8a5iM9r3rcQ6M2LECMXHx9v+jh8/nm4sAAAAACDrZHnhnJycrAULFqhQoUK31M+YMWM0fvx49e7dW++//77dtCJFikhyfmqztc16+nOePHnk5+fnNDYlJUVxcXF2p08XKVJEcXFxTgvcmJgYhYaGytvbO9N5ZCbWGR8fHwUHB9v9AQAAAABuP7euce7Tp4/T9nPnzmnr1q06c+aM3nrrLbeTGjNmjMaOHauePXtq9uzZDrdoqlKlinx8fLR161aHebdt2ybp2gjdkuTh4aEaNWpox44dSklJsTtV+ueff5YxxhYrSREREfr222/1888/q0GDBrb25ORk7dy50+4WWxEREZKkrVu3qlmzZg55BAcHq1y5cpnOGQAAAACQc7h1xHn+/PlO/3788UeVLl1aCxcu1LBhw9xKaNy4cRo7dqwef/xxzZ07Vx4ejikGBgaqXbt22rBhg3bt2mVrT0xM1OzZs1W2bFm70am7du2qpKQku3sqS9LkyZPl6elpu92VdO3WVxaLRZMnT7aLnTVrlpKSktS9e3dbW8OGDVW4cGHNnj1biYmJtvZdu3Zpw4YN6ty5s7y8vNzKGQAAAACQM7h1xDktLS2r85Akvfvuu3r55ZdVvHhxNWvWTIsWLbKbXrBgQTVv3lzStcGy1q5dqxYtWmjIkCEKDg7WrFmzFBMTo1WrVtkdpe7fv7/mzZunoUOH6siRI6pYsaJWr16t5cuXa9SoUSpRooQttkqVKho4cKCmT5+ujh07qnXr1tq7d6+mTp2qhg0b2kb2liQvLy9NmTJFkZGRatCggfr376+EhARNmjRJ+fPn19ixY+3yz0zOAAAAAICcwa3C+Xax3hf52LFjTm951bBhQ1vhXKZMGW3evFnDhw/X66+/rtTUVNWoUUNff/21w2nT3t7e+v777zVq1CgtXrxYZ8+eVenSpTVt2jQNHDjQ4XkmT56sEiVKaObMmVq1apVCQ0MVFRWlcePGORwB79y5s/z8/DR+/Hg999xz8vHxUdOmTfXGG284XLOcmZwBAAAAADmDxRhj3J05ISFB33//vW0E7VKlSql58+YKCgrKsgThXEJCgkJCQhQfH89AYQCQhVovH+/WfKs7jMriTHC9tp9/5tZ8Kx/pksWZAADuFpmpqdw+4jx79mwNGzZMiYmJstbeFotFgYGBmjhxovr27etu1wAAAAAA5BhuFc5ffvmlBgwYoFKlSumVV15R5cqVJUl79uzRtGnTNGDAABUoUEDt2rXL0mQBAAAAAPi3uVU4v/nmm6pYsaJ++uknBQYG2tqbNm2q3r17q06dOnrjjTconAEAAAAAdzy3bke1a9cu9erVy65otgoKClLPnj3tbrkEAAAAAMCdyq3C+WbjiXFbJQAAAADA3cKtwrlq1aqaP3++Ll686DAtMTFR8+fPV9WqVW85OQAAAAAAsptb1zg///zz6tixo2rUqKFBgwapUqVKkv5/cLADBw5o2bJlWZooAAAAAADZwa3C+eGHH9b06dP1v//9T1FRUbZTs40xCggI0PTp09W+ffssTRQAAAAAgOzg9n2cn376aXXr1k3fffedDh8+LEkqVaqUmjdvrpCQkCxLEAAAAACA7OR24SxJuXPnVufOnbMqFwAAAAC4a/098Q+35is0tFIWZ4LMcqtw3rFjh7Zs2aKBAwc6nf7uu++qfv36qlat2q3kBgAAbkGbpTPdmm9VpwFZnAkAAHc2t0bVHjt2rFatWpXu9DVr1mjcuHFuJwUAAAAAQE7hVuEcHR2thg0bpju9YcOG+vnnn91OCgAAAACAnMKtwjkuLk558+ZNd3ru3LkVFxfndlIAAAAAAOQUbhXOBQoU0J49e9Kd/vvvv7ssrAEAAAAAuFO4VTg3a9ZMs2fPdlo8//HHH5ozZ46aNWt2y8kBAAAAAJDd3BpVe9SoUVq2bJkiIiLUp08f2+jZO3fu1Ny5c+Xt7a3Ro0dnZZ4AAAAAAGQLtwrn0qVLa+3aterVq5dmzJhhN61y5cqaN2+eypYtmyUJAgAAAACQndwqnCWpVq1a+v3337Vz507t379fklSuXDlVrVo1y5IDAAAAACC7uV04W1WrVs12qjYAAAAAAHcbtwYHAwAAAADgv4LCGQAAAAAAFyicAQAAAABwgcIZAAAAAAAXKJwBAAAAAHCBwhkAAAAAABdu6XZU+/fv1/79+3X27FkZYxym9+jR41a6BwAAAAAg27lVOJ88eVI9e/bU2rVrJclp0WyxWCicAQAAAAB3PLcK5wEDBmj9+vUaPHiwGjRooDx58mR1XgAAAAAA5AhuFc7r1q3Ts88+q7fffjur8wEAAAAAIEdxa3CwwMBAlSlTJqtzAQAAAAAgx3GrcG7btq2+//77rM4FAAAAAIAcx63C+Z133tHhw4c1ZMgQHTp0yOngYAAAAAAA3A3cKpxz586tnj17aurUqSpbtqw8PT2VK1cuuz9Pz1u60xUAAAAAADmCW9Xtm2++qREjRqhgwYKqXbs2o2oDAAAAAO5abhXO06ZNU6NGjfT111/Ly8srq3MCAAAAACDHcOtU7XPnzqlLly4UzQAAAACAu55bhXPVqlV17NixrM4FAAAAAIAcx63C+dVXX9XMmTP1yy+/ZHU+AAAAAADkKG5d47xgwQKFhYWpTp06qlu3rkqVKqVcuXLZxVgsFs2ZMydLkgQAAAAAILu4VTjPnz/f9v/Nmzdr8+bNDjEUzgAAAACAu4FbhXNaWlpW5wEAAAAAQI7k1jXOAAAAAAD8V7h1xNnq4sWL2rp1q06dOqVmzZqpYMGCWZUXAAAAAAA5gttHnN977z2FhYWpRYsW6tGjh/bs2SNJOn36tHx9fTVr1qwsSxIAAAAAgOziVuG8dOlSDRw4UI0bN9bs2bNljLFNK1CggFq1aqUvvvjCrYQmTJigzp07q1SpUrJYLCpRokS6sb169ZLFYnH69/nnnzvEp6Sk6KWXXlLJkiXl4+Oj0qVLa/z48bp8+bLT/j/66CNVr15dfn5+KliwoPr166czZ844jf3pp5/UrFkzBQUFKTg4WK1atdLOnTudxsbGxqpHjx7Knz+//Pz8VKtWLS1ZsuSm6wYAAAAA8O9z61Ttt956S40bN9by5ct19uxZ9evXz256rVq13D7iPHLkSOXNm1c1atTQhQsXMjTPggULHNpq167t0BYZGakVK1aoT58+qlu3rrZu3arRo0frwIEDdiOFS9KkSZM0dOhQNWzYUFOmTNGJEyc0ceJEbd26VT///LMCAgJssdu2bVOjRo0UFhamcePGSZKmT5+uBg0aaMuWLapSpYot9ty5c7r//vt1+vRpDR06VEWLFtWiRYvUpUsXzZ07V717987QMgMAAAAA/h1uFc6//fab3njjjXSnFy5cWKdPn3YroYMHD6pUqVKSpHvuuUeJiYk3neexxx67aczq1au1YsUKDR06VO+8844kqV+/fsqdO7cmTpyoAQMGqF69epKkuLg4jRo1ShEREVq7dq3tHtURERF66KGHNGXKFI0cOdLW96BBg+Tt7a0ffvhBYWFhkqQuXbqoYsWKGjZsmL799ltb7Ouvv67Dhw/ryy+/VLt27SRJffv2Vd26dfXcc8+pc+fOCgwMzMiqAgAAAAD8C9w6VTtXrlwub0kVGxtrd0Q2M6xFc2YYY5SQkOAyp0WLFkmSBg8ebNdufbxw4UJb2xdffKGkpCRFRUXZimZJateunUqVKmUXe+DAAUVHR6tz5862olmSwsLC1LlzZ33//ff6+++/7fIoXbq0rWiWrq3PqKgonTt3TqtXr87cwgMAAAAAbiu3CueqVavqm2++cTotLS1NS5YsUURExC0llhkhISEKCQmRn5+fmjdvrp9++skhJjo6WmFhYSpWrJhde7FixVSkSBFFR0fbxUpS3bp1HfqpU6eO/vzzT9uR8JvFGmO0fft2SdLJkycVExOjOnXqOI29vj8AAAAAQM7gVuH8zDPPaM2aNRo9erTOnTsn6VrB/Ndff6lz587as2ePBg0alKWJOlOoUCENGTJE7733npYvX66RI0fql19+UYMGDfT999/bxcbGxtodEb5eWFiYYmJi7GKt7c5ijTG2mJvFSrL1nZnYG6WkpCghIcHuDwAAAABw+7l1jXNkZKR+++03vfrqq5owYYIkqVWrVjLGyBijMWPG6MEHH8zSRJ15/fXX7R4//PDD6tatm6pVq6annnpK+/fvt01LSkqSj4+P0358fX2VlJRkFyvJabyvr69dzO2KvdGECRM0duxYp9MAAAAAALePW4WzJI0fP14dO3bUxx9/rD///FPGGJUtW1aPP/64atWqlZU5ZkrZsmXVpUsXzZ8/X/v27VO5cuUkSf7+/kpJSXE6T3Jysvz9/W2Prf9PSUmRn5+fQ+z1MdfHOuvX3dgbjRgxQkOHDrU9TkhIcDjtHAAAAACQ9dwunCWpRo0aqlGjRlblkmWs936Oi4uzFc5FihRJ9zTomJgYu9OnixQpYmsvU6aMQ6zFYrHFXB/rrF/p/0/DzkzsjXx8fNI9Yg4AAAAAuH3cusa5VKlS+vLLL9OdvnLlSrdGx84q1lO0CxYsaGuLiIhQTEyMjh8/bhd7/PhxxcbG2h0ltw5stnXrVoe+t23bpvLly9tuGXWzWIvFopo1a0q6dpuusLAwbdu2zWmspGw9Wg8AAAAAcORW4XzkyBGX91e+ePGijh496nZSGXHx4kXb6c3X27Fjh5YsWaKKFSuqdOnStvauXbtKkiZPnmwXb33cvXt3W1v79u3l5+en6dOn6+rVq7b2r776SocOHbKLLVOmjGrVqqUlS5bYBv+Srg0EtmTJEjVp0kSFChWyy+PgwYP66quvbG1Xr17VtGnTlDt3brVu3TqTawIAAAAAcDvd0qna6Tl16lS61+rezIIFC2xF95kzZ5Samqrx48dLksLDw/X4449LunZU+cEHH9TDDz+ssmXLKiAgQLt27dLcuXOVK1cuzZw5067fNm3aqG3btpo4caLi4+NVt25dbd26VXPmzNFjjz2m+++/3xabP39+vfLKK3ruuefUrFkzde3aVTExMXrnnXdUoUIFh3tBT5kyRY0bN1aDBg0UFRUlSZo2bZrS0tL0zjvv2MUOHz5cS5YsUbdu3TR06FCFhYVp8eLFio6O1uzZsxUUFOTWegMAAAAA3B4ZLpx/+OEHbdiwwfZ42bJlOnDggEPcuXPn9Mknn6hatWpuJTRnzhxt3LjRrm306NGSpIYNG9oK50KFCqlZs2Zav369Pv74Y126dEmFCxdWZGSkRowYoQoVKjj0vWTJEo0fP14LFy7UggULFBYWpnHjxmn48OEOscOGDVO+fPk0adIkDRo0SMHBwerSpYtef/1122naVvXq1dOGDRs0atQojRo1ShaLRfXq1dOSJUtUtWpVu9h8+fJp8+bNGj58uN59910lJiaqUqVK+uSTTxQZGenWOgMAAMguXZb+kel5PutU6TZkAgC3j8UYYzISOHbsWNvtkCwWi1zNVqZMGS1atIjrdW+jhIQEhYSEKD4+XsHBwdmdDgDcNVovH+/WfKs7jMriTG5dm6Uzbx7kxKpOA7I4k1vX9vPP3Jpv5SNdsjgT3IjCGci4vydm/v0iSYWG8p65HTJTU2X4iPPgwYPVq1cvGWNUqlQpTZ48We3bt7eLsVgsCgwMVN68ed3LHAAAAACAHCbDhXNISIhCQkIkSevXr1fFihVVoECB25YYAAAAAAA5gVuDgzVs2DCr8wAAAAAA3CFOT1vn1nwFoppkcSb/jgwVzuPGjZPFYtGLL74oDw8PjRs37qbzWCwW26BeAAAAAADcqTJUOI8ZM0YWi0X/+9//5O3trTFjxtx0HgpnAAAAAMDdIEOF8+HDhyVJ3t7edo8BAAAAALjbZahwDg8Pd/kYAAAAAIC7lVuDgwEAgNurzbLpbs23quMzWZwJAADwyO4EAAAAAADIySicAQAAAABwgcIZAAAAAAAXKJwBAAAAAHCBwhkAAAAAABconAEAAAAAcCFDt6Pq06dPpju2WCyaM2dOpucDAAAAACAnyVDhPH/+/Ex3TOEMAAAAALgbZKhwTktLu915AAAAAACQI3GNMwAAAAAALlA4AwAAAADgQoZO1XbmypUr+uKLL/TTTz/p/PnzDqdzc40zAAAAAOBu4FbhfO7cOTVu3Fi///67jDGyWCwyxkiS7f8UzgAAAACAu4Fbp2qPGjVKf/75p2bPnq2DBw/KGKNvvvlGe/fuVdeuXRUREaGzZ89mda4AAAAAAPzr3CqcV61apR49eqh3794KDg6WJOXKlUvly5fXwoUL5efnpxEjRmRpogAAAAAAZAe3Cue///5bERERkiRPz2tneycnJ9umP/zww/ryyy+zID0AAAAAALKXW4Vz3rx5dfHiRUlSUFCQvLy8dPz4cdt0Ly8vnT9/PmsyBAAAAAAgG7lVOJcrV05//PHHtQ48PFS9enXNnz9fKSkpSkpK0kcffaRSpUplaaIAAAAAAGQHtwrnFi1a6PPPP1dKSookaejQofrpp5+UN29eFShQQL/88ouGDBmSpYkCAAAAAJAd3Lod1ciRI/Xcc8/Jx8dHktSlSxd5enpq4cKFypUrlx555BFFRkZmaaIAAAAAAGQHtwpni8ViK5qtOnbsqI4dO2ZJUgAAAAAA5BRunardpEkTrV27Nt3p69evV5MmTdxOCgAAAACAnMKtwnnDhg06depUutNPnz6tjRs3up0UAAAAAAA5hVuF881cuHDB4VRuAAAAAADuRBm+xnn37t3auXOn7fGmTZt05coVh7hz585pxowZqlSpUpYkCAAAAABAdspw4bx8+XKNHTtW0rXBwT744AN98MEHTmODgoI0derUrMkQAAAAAIBslOHCuVevXmrUqJGMMWrSpIlGjhyp5s2b28VYLBYFBgaqUqVK8vX1zfJkAQAAAAD4t2W4cA4PD1d4eLgkad68eWrYsKFKlChxu/ICAAAAACBHcOs+zj179rR7HBcXJ0kKDQ299YwAAAAAAMhB3B5VOzY2Vj179lTu3LlVsGBBFSxYUHny5FGvXr0UExOTlTkCAAAAAJBt3DrifOzYMdWpU0d///23qlWrpsqVK0uS/vjjD3300Uf67rvvtG3bNhUrVixLkwUAAAAA4N/mVuE8evRonT9/XitXrlTr1q3tpq1Zs0YdO3bU6NGjNX/+/KzIEQAAAACAbOPWqdrffvutnn76aYeiWZIefPBBPfXUU/r6669vOTkAAAAAALKbW4Xz+fPnVbZs2XSnly1bVhcuXHA3JwAAAAAAcgy3CueiRYtqw4YN6U7/4YcfVLRoUXdzAgAAAAAgx8hw4Xzs2DFdunRJktS5c2ctWbJEI0aMUHx8vC0mISFBI0eO1GeffabIyMiszxYAAAAAgH9ZhgvnkiVLavny5ZKuDQ5Wt25dvfHGGwoNDVV4eLjCw8OVL18+vf7666pXr55GjRp125IGAAAAAODfkuHC2Rhj+7+/v782bNigDz74QC1atFBAQIACAgLUsmVLzZw5U+vXr5efn59bCU2YMEGdO3dWqVKlZLFYVKJECZfxP/30k5o1a6agoCAFBwerVatW2rlzp9PY2NhY9ejRQ/nz55efn59q1aqlJUuWOI1NSUnRSy+9pJIlS8rHx0elS5fW+PHjdfnyZafxH330kapXry4/Pz8VLFhQ/fr105kzZ245ZwAAAABA9nLrdlSS5Onpqf79+6t///5ZmY9GjhypvHnzqkaNGjcdYGzbtm1q1KiRwsLCNG7cOEnS9OnT1aBBA23ZskVVqlSxxZ47d07333+/Tp8+raFDh6po0aJatGiRunTporlz56p37952fUdGRmrFihXq06eP6tatq61bt2r06NE6cOCAw222Jk2apKFDh6phw4aaMmWKTpw4oYkTJ2rr1q36+eefFRAQ4FbOAAAAAIDs53bhfLscPHhQpUqVkiTdc889SkxMTDd20KBB8vb21g8//KCwsDBJUpcuXVSxYkUNGzZM3377rS329ddf1+HDh/Xll1+qXbt2kqS+ffuqbt26eu6559S5c2cFBgZKklavXq0VK1Zo6NCheueddyRJ/fr1U+7cuTVx4kQNGDBA9erVkyTFxcVp1KhRioiI0Nq1a5UrVy5JUkREhB566CFNmTJFI0eOdCtnAAAAAED2y1ThvGnTJl25ciXD8T169Mh0Qtai+WYOHDig6Oho9enTx1aASlJYWJg6d+6sefPm6e+//1ahQoUkSYsWLVLp0qVtRbMk5cqVS1FRUerRo4dWr16tLl262GIlafDgwXbPOXjwYE2cOFELFy60Fc5ffPGFkpKSFBUVZSuaJaldu3YqVaqUFi5caCucM5szAAAAACD7ZapwnjlzpmbOnHnTOGOMLBaLW4VzRkVHR0uS6tat6zCtTp06mjt3rrZv3642bdro5MmTiomJUffu3Z3GWvuzFs7R0dEKCwtTsWLF7GKLFSumIkWK2J47I3ksXrxYiYmJCgwMzFTOAAAAAICcIVOF84ABA2yFZnaLjY2VJLsjt1bWtpiYmEzHWuMrVark9HnDwsJ04sSJDOdhjFFsbKzKlSuX6Tyul5KSopSUFNvjhIQEp3HIGn++296t+SoMXJHFmQAAAADIbpkqnBs0aKBu3brdrlwyJSkpSZLk4+PjMM3X19cuJjOx1v87i7XG3xh7u/K43oQJEzR27Fin0wAAAAAAt0+Gb0eV0/j7+0uS3VFYq+TkZLuYzMRa/+8s1hp/Y+ztyuN6I0aMUHx8vO3v+PHjTuMAAAAAAFkrx42qnVFFihSR5PzUZmub9fTnzMRa49M7ZTomJsYh1tpepkwZh1iLxWKLyWwe1/Px8Un3KDgAAAAA4Pa5Y484R0RESJK2bt3qMG3btm2yWCyqWbOmJKlw4cIKCwvTtm3bnMZKUq1atez6jomJcTiqe/z4ccXGxjrEusqjfPnytttcZSZnAAAAAEDOkOHCOS0tLcdc3yxJZcqUUa1atbRkyRLboFvStcG6lixZoiZNmtjd1qlr1646ePCgvvrqK1vb1atXNW3aNOXOnVutW7e2i5WkyZMn2z2n9fH1o3O3b99efn5+mj59uq5evWpr/+qrr3To0CG72MzmDAAAAADIfjnuVO0FCxbo6NGjkqQzZ84oNTVV48ePlySFh4fr8ccft8VOmTJFjRs3VoMGDRQVFSVJmjZtmtLS0vTOO+/Y9Tt8+HAtWbJE3bp109ChQxUWFqbFixcrOjpas2fPVlBQkC22TZs2atu2rSZOnKj4+HjVrVtXW7du1Zw5c/TYY4/p/vvvt8Xmz59fr7zyip577jk1a9ZMXbt2VUxMjN555x1VqFDB4V7QmckZAAAAAJD9clzhPGfOHG3cuNGubfTo0ZKkhg0b2hXO9erV04YNGzRq1CiNGjVKFotF9erV05IlS1S1alW7PvLly6fNmzdr+PDhevfdd5WYmKhKlSrpk08+UWRkpEMeS5Ys0fjx47Vw4UItWLBAYWFhGjdunIYPH+4QO2zYMOXLl0+TJk3SoEGDFBwcrC5duuj111+3nabtTs4AAAAAgOyX4wrnDRs2ZCq+bt26Wrt2bYZiw8LCtGDBggzF+vr6avz48baj3TfTq1cv9erVK0OxmckZAAAAAJC97tjBwQAAAAAA+DdQOAMAAAAA4AKFMwAAAAAALlA4AwAAAADgAoUzAAAAAAAuUDgDAAAAAOAChTMAAAAAAC5QOAMAAAAA4AKFMwAAAAAALlA4AwAAAADgAoUzAAAAAAAuUDgDAAAAAOAChTMAAAAAAC54ZncCAAAAAID/ntPTV7s1X4FnWmdxJjfHEWcAAAAAAFygcAYAAAAAwAUKZwAAAAAAXOAaZwAAACCLfLz0TKbn6d4p/23IBEBWonAGAAAAgP+QU1M2uzVfwWfrZ3Emdw5O1QYAAAAAwAWOOAMAAAC4LXbMPu3WfNX7FcjiTIBbwxFnAAAAAABcoHAGAAAAAMAFTtUGcFMr5z7o1nxt+6zJ4kxwOwxe2irT80zu9PVtyAQAACBn4ogzAAAAAAAuUDgDAAAAAOACp2oD+M+Z81HLTM/Tt8c3tyETAAAA3Ak44gwAAAAAgAsccQYAAP8J7T7/wq35vnrk4SzNAwBw56FwBgDcsi4rMj8y92ftGZkbAADcGSicAQDIYm2WvePWfKs6DsviTADcib5cEpfpeR7qHHobMgFgReEM3Ca/vt/OrflqPPlVFmcCAAAA4FZQOAPAHWzMZ5kfIXxMF0YIBwAAyAxG1QYAAAAAwAWOOAMA7hqtv/ifW/OtfviNLM4EAADcTSicAQAAAOAOcWrydrfmKzi4ZhZn8t/CqdoAAAAAALjAEWcAcMO7CzM/KNfAxxiUCwCAO9Xfbx1xa75Cz5fI0jyQPSicAQDAbdX284/dmm/lI92zOBMAANxD4QwAyBEeXNHXrfnWtJ+TxZkAAADYo3AGAADpart0vlvzrezUK0vzAAAgOzE4GAAAAAAALlA4AwAAAADgAoUzAAAAAAAuUDgDAAAAAODCHV04WywWp3+BgYEOsX/99Zcefvhh5cmTRwEBAWrQoIHWrVvntN/4+HhFRUUpLCxMvr6+qly5st577z0ZYxxi09LSNGnSJFWoUEG+vr4qVqyYhg0bposXLzrte/Xq1apXr54CAgKUN29ede7cWYcPH761FQEAAAAAuG3u+FG1GzRooAEDBti1eXl52T0+ePCg6tWrJ09PT73wwgsKCQnRrFmz1LJlS61Zs0bNmjWzxaampqp58+basWOHoqKiVLFiRa1Zs0ZPP/20Tp06pTFjxtj1PWTIEE2dOlUdOnTQsGHDtHfvXk2dOlU7duzQ999/Lw+P//9tYtmyZXrkkUdUtWpVvfXWW4qPj9fkyZNVv359/fLLLypSpEjWryAAAJCjPPz5927N98UjzW4eBAC4Le74wrlUqVJ67LHHXMaMGDFCFy5c0Pbt21WtWjVJUo8ePVS5cmUNHDhQf/75pywWiyRp9uzZio6O1tSpUxUVFSVJ6t+/vzp16qTXXntNvXv3Vnh4uCRpz549mjZtmjp27KilS5fanq9kyZIaNGiQPvnkE3Xr1k2SdPnyZUVFRalYsWLatGmT7aj4gw8+qJo1a2rMmDGaOXNmlq4bAAAAAMCtu6NP1bZKTU1VYmKi02kXL17Ul19+qUaNGtmKZkkKDAxUv379tG/fPkVHR9vaFy1aJH9/f/Xv39+un8GDB+vy5cv69NNPbW2LFy+WMUaDBw+2i+3fv7/8/f21cOFCW9vGjRsVGxurfv362Z1KXq1aNTVq1EiffvqpLl++7M7iAwAAAABuozu+cP7888/l7++voKAgFShQQFFRUYqPj7dN3717t1JSUlS3bl2HeevUqSNJtsI5LS1Nv/76q6pXry5fX1+72Nq1a8tisdgV2dHR0fLw8FDt2rXtYn19fVWtWjWHWEnp5pGQkKB9+/ZldvEBAAAAALfZHX2qdu3atdW5c2eVKVNGCQkJWr16taZPn66NGzdqy5YtCgwMVGxsrCQpLCzMYX5rW0xMjCTp/PnzunTpktNYHx8fhYaG2mIlKTY2VqGhofLx8XHa95YtW5Samipvb+8M51G5cmWny5qSkqKUlBTb44SEBOcrBQAA4A7Te9mxTM8zr2Px25AJADh3RxfOP/30k93jHj166N5779WLL76oKVOm6MUXX1RSUpIkOS1urUeVrTGuYq3x1hhrvKtYa4y3t3em8nBmwoQJGjt2bLrTAQAAADh3/J2/3Zqv2LBCWZwJ7lR3dOHszPPPP6+xY8dq1apVevHFF+Xv7y9JdkdrrZKTkyXJFuMq1hpvjbHGnz59Ot3YjPZ9Y6wzI0aM0NChQ22PExISVKxYsXTjAQAAbqbT0l8yPc/STrVuQyZA+v5695Rb85UfWDCLM0FOdPrdZW7NV2Bgx0zF3/HXON/Iy8tLRYoUUVxcnCTZbvF0/SnWVtY266nSefLkkZ+fn9PYlJQUxcXF2Z1qbX0eZ8VwTEyMQkND5e3tnek8nPHx8VFwcLDdHwAAAADg9rvrCufk5GSdOHFCBQte+4WpSpUq8vHx0datWx1it23bJkmqVevaL6ceHh6qUaOGduzY4VAM//zzzzLG2GIlKSIiQmlpafr5558dcti5c6dDrKR08wgODla5cuXcWWQAAAAAwG10x56qffbsWeXLl8+hffTo0bpy5YratWsn6dptp9q1a6dly5Zp165dqlq1qiQpMTFRs2fPVtmyZe1Gxe7atas2b96smTNn2u7jLEmTJ0+Wp6enIiMjbW2RkZF67bXXNHnyZDVo0MDWPmvWLCUlJal79+62toYNG6pw4cKaPXu2hgwZYrsl1a5du7Rhwwb17t1bXl5eWbR2gLvXwvktMz3PY72+uQ2ZAPgvav/5arfmW/FI6yzOBADwb7pjC+fx48dr27Ztaty4sYoXL67ExEStXr1a69ev13333WdX9E6YMEFr165VixYtNGTIEAUHB2vWrFmKiYnRqlWrZLFYbLH9+/fXvHnzNHToUB05ckQVK1bU6tWrtXz5co0aNUolSpSwxVapUkUDBw7U9OnT1bFjR7Vu3Vp79+7V1KlT1bBhQ3Xr1s0W6+XlpSlTpigyMlINGjRQ//79lZCQoEmTJil//vwM/AUAAAAAOdQdWzg3atRIf/zxhz788EOdPXtWuXLlUtmyZfXqq69q6NChdvdhLlOmjDZv3qzhw4fr9ddfV2pqqmrUqKGvv/5azZo1s+vX29tb33//vUaNGqXFixfr7NmzKl26tKZNm6aBAwc65DF58mSVKFFCM2fO1KpVqxQaGqqoqCiNGzdOHh72Z8J37txZfn5+Gj9+vJ577jn5+PioadOmeuONN1xe3wwAAADXxiyPzfw8HYrchkwA3I3u2MK5ffv2at++fYbjK1asqBUrVmQoNnfu3Jo+fbqmT59+09hcuXJp2LBhGjZsWIb6btu2rdq2bZuh2P+ikzNGujVf4adfy+JMAAAAAOCaO7ZwBtJzfFr3mwc5USzq4yzOBAAAAMDdgMIZAAAAkPTu8szfL3hgB+4VDPwX3HW3owIAAAAAICtROAMAAAAA4AKFMwAAAAAALlA4AwAAAADgAoUzAAAAAAAuUDgDAAAAAOAChTMAAAAAAC5QOAMAAAAA4AKFMwAAAAAALlA4AwAAAADgAoUzAAAAAAAuUDgDAAAAAOAChTMAAAAAAC54ZncCyBnOvD/TrfnyPzkgizMBAAAAgJyFI84AAAAAALjAEWdkmVPvvePWfAWfGpbFmQAAAABA1uGIMwAAAAAALlA4AwAAAADgAqdqA0A2eXtxy0zP81zXb25DJgAAAHCFwhkAAAC4y3y/6Ixb8zXrlj+LMwHuDhTOwF3u+9mt3ZqvWb/VWZwJAAAAcGfiGmcAAAAAAFygcAYAAAAAwAUKZwAAAAAAXKBwBgAAAADABQYHA/Cv+HxeK7fme6T311mcCQAAAJA5HHEGAAAAAMAFCmcAAAAAAFygcAYAAAAAwAWucQYAALjDdFj6o1vzLe90fxZngrvZlo/OuDVfvR75szgTIPtROAM52OaZbd2ar/6AlVmcCQAAAPDfxanaAAAAAAC4QOEMAAAAAIALFM4AAAAAALhA4QwAAAAAgAsMDnYXOPPeQrfmy//UY1mcCQAAAADcfTjiDAAAAACACxTOAAAAAAC4QOEMAAAAAIALFM4AAAAAALhA4QwAAAAAgAsUzgAAAAAAuEDhDAAAAACACxTO/7K0tDRNmjRJFSpUkK+vr4oVK6Zhw4bp4sWL2Z0aAAAAAMAJCud/2ZAhQzR06FBVqlRJ06ZNU+fOnTV16lS1a9dOaWlp2Z0eAAAAAOAGntmdwH/Jnj17NG3aNHXs2FFLly61tZcsWVKDBg3SJ598om7dumVjhgAAAACAG3HE+V+0ePFiGWM0ePBgu/b+/fvL399fCxcuzJ7EAAAAAADponD+F0VHR8vDw0O1a9e2a/f19VW1atUUHR2dTZkBAAAAANLDqdr/otjYWIWGhsrHx8dhWlhYmLZs2aLU1FR5e3s7TE9JSVFKSortcXx8vCQpISFBqZcuuZWPT0KC7f//ZEkfyW714WfXR4qLyPQF2PVx2a0+Eq7rIzGH9HExh/SRdOlKjuhDki650U9O7SM56db7SMkhfVzOgj6uJKVmuo8b+7mc5N5niH0f7n2WZX0f7n0u58w+kujDrg/3BgTN6j6u9ZOYI/pITfrnlvtIyYI+LrnVh5/d4yS3+rDfN3SvD/v9x4tu9HFjLhcv3XofiW734ZsFffz/a/NPsrt9+N9yH/7X76cmZ/79klV92O1zJ7v3GWK/3+5eH752fbj3mZpVfVg/A4wxN423mIxEIUuULl1aly9f1rFjxxym9ejRQwsWLND58+eVO3duh+ljxozR2LFj/4UsAQAAAOC/4/jx4ypatKjLGI44/4v8/f11+vRpp9OSk5NtMc6MGDFCQ4cOtT1OS0vTuXPnlC9fPlksFqfzJCQkqFixYjp+/LiCg4Pdypk+6IM+/t0+clIu9EEf9EEfd3ofOSkX+qAP+sh5fRhj9M8//6hIkSI37Y/C+V9UpEgR/fHHH0pJSXE4XTsmJkahoaFOT9OWJB8fH4d5nB2ZdiY4OPiWvnDogz7o49/vIyflQh/0QR/0caf3kZNyoQ/6oI+c1UdISEiG+mFwsH9RRESE0tLS9PPPP9u1Jycna+fOnapVq1Y2ZQYAAAAASA+F878oMjJSFotFkydPtmufNWuWkpKS1L179+xJDAAAAACQLk7V/hdVqVJFAwcO1PTp09WxY0e1bt1ae/fu1dSpU9WwYUN169YtS5/Px8dHL7/8stNRvOmDPugjZ/aRk3KhD/qgD/q40/vISbnQB33Qx53Xx/UYVftfdvXqVU2ePFkzZ87UkSNHFBoaqsjISI0bN06BgYHZnR4AAAAA4AYUzgAAAAAAuMA1zgAAAAAAuEDhDAAAAACACxTOAAAAAAC4QOEMAAAAAIALFM53oStXrighIUFXrlzJ7lSyVHx8vCZOnKgDBw5kWw5//fWXtm3bpkOHDmVbDgAAAAD+XdzH+S7xySefaOHChYqOjlZcXJytPTQ0VBEREerevbu6du2ajRneuri4OD3//PMqWbKkypQpc9ueZ/PmzYqJiVGXLl1sbR9++KFGjhypv//+29ZWvnx5TZs2TU2bNr1tuVzv3LlzOnbsmJKSkhQcHKwyZcrI19f3X3luAMB/1/Hjx3X48GE98MAD6cacPn1auXPnlre3t9PpZ86c0d69e9Ptwxijy5cv280fHx+vZcuW6dy5c6pTp47q16/vVv6HDx9W1apVtXDhQj300EOZmjcpKUlbtmxRXFycChYsqHr16mX6nrDJycn68MMPtWHDBp0/f14FChRQmzZt1KVLF1ksFof4CRMmqF27drrnnnsy9Tw3OnnypJYsWaKAgAA9+uijCggI0NWrV/Xhhx9q48aNunLlimrXrq2+ffu6vCVqYmKiVqxYoW3btun48eO2/ZAKFSqodevWqlev3i3lCdwpuB3VHS4pKUkPPfSQ1q1bJ39/f1WrVk1hYWHy9fVVcnKyYmJitHPnTl26dEmNGjXSV199JX9/f4d+UlJSNGfOHP3+++8qWLCgunXrprJlyzrEff/993rttde0bt06h2lxcXGaP3++zp8/r9atW9u+4N544w3NmDFD586dU926dfXOO++oSpUqDvMPGjTI5bLGx8drwYIFevDBB1W6dGlZLBZNmTIlQ+toypQpWrVqle2L76GHHtIzzzzj9MuvSZMmKlmypObMmSNJ+vjjj/X4448rd+7ceuihh1S4cGEdP35cK1as0OXLl7VhwwbVqVPHoR8fHx899NBD6tu3r1q2bOn0y/FmkpOTNXHiRM2bN8/hKLenp6caNWqkF1980eXOzPVOnz6t7du3KyYmRklJSfL391dYWJhq1qypAgUKZDo/Zy5duqQzZ86oePHiGZ7n6tWr+uGHH3Tu3DnVrl1bxYoVc+u5T5w4odatW2vSpElu/aBx4MAB2zZSsmTJTM//zTff2O0YtW7d2um2YbV48WK1bNlSefPmzfRzXe/SpUv67rvvFBAQoCZNmti2tXXr1tl2jiIiItS+ffubboebN292unPUsmVLt1+X6/0bO+Dpyc6dZ3acr/nll18UHR3t8BlUq1YtRURE3NKyWmVkG7teZl/bu/W1ceXVV1/VSy+9pKtXrzpMW7RokYYNG6bTp0/L29tbjz76qN5++23ly5fPLu7jjz9Wjx49nPbx6quv6s0339TFixfVpEkTLV68WAkJCapfv77tB2uLxaJevXrZvpuvd+7cOZf5Hzp0SLVr19b8+fPVtm1bSXL43J04caLatGmj8uXL29o++ugjDRkyRBcuXLC1hYaG6r333lPHjh2dPldUVJRiYmK0bNkySdc+z5o0aaK9e/fq+l1ui8Wipk2bauXKlQ6fdR4eHrJYLIqIiFC/fv306KOPutwGnDl8+LAiIiJ0/vx5GWNUuXJlbd26VZGRkVqzZo1dbNmyZbVlyxaH10y69j0VFRVl68ea+/X/b9SokT766COFhYW5zCmnvP/5kSd9fFfdhMEdbdiwYcbLy8tMmzbNJCcnO41JTk42U6dONV5eXua5555zmH7x4kVTrVo14+HhYSwWi7FYLMbb29u88cYbDrELFy40Hh4eDu0nT540YWFhtvk9PDzMokWLzNSpU42Pj4+pU6eOqVq1qsmVK5fJmzevOX78uEMf1vmsfTj7u366szyCgoLMZ599ZnscHx9vqlataiwWi/Hx8TElS5Y0Xl5exsPDw9SrV8+kpKQ49JE/f34zefJk2+Ny5cqZKlWqmPPnz9vFxcbGmvDwcNOqVSuHPqzLY82zePHi5uWXXzZHjhxxGuvMhQsXTI0aNYzFYjF+fn4mb968tuXo2LGjqV27tvHy8jK5cuUyr7/+usu+fv/9d9OyZUuTK1cuh3Xs4eFhcuXKZVq0aGF+++23dPvYsGGDiYiIMH5+fqZEiRJmzJgxTtdfetuIdVrlypVNkSJFTP/+/U1SUpI5c+aMuffee42Hh4fx8PAwnp6eZty4cRleT9c7cOCAsVgsZtmyZenGLF261GH7W7t2rSlXrpwtBw8PD3PPPfeYLVu2OO1j/Pjxpn///rbHiYmJpkmTJk7Xbe/evdPNxWKxGF9fXxMZGWm+++67TC7tNadOnTKlS5e25d2kSRNz5coV079/f4f3S/369U1SUpLTfjZu3GjKly9vtw6un9/T09P06dPHJCQkuJWn1fjx49PdPj7++GNTqFAh4+HhYXx9fU2vXr1MXFycQ1x629jZs2dd/kVHRxuLxWI+/PBDW9uN3nnnHfPnn3/atX344Ycmb968duulQIECZunSpU6X45lnnjEdOnSwPT516pSpXLmy0+2jefPmTt9H1un33XefmTVrlvnnn3+cPpcrhw4dMvny5bM97z333GP++ecf07p1a4fP1nLlyjld18YYs2jRIrt+nH0WN2nSxJw4cSLdXNauXWsqVKiQ7me8h4eHqVChgtvvg+ult41lxWtrzN332mREeuv0p59+Mh4eHiY0NNR06tTJ3HfffcZisZjixYubPXv22MWm975dvny5sVgspmrVqqZ169bGw8PDdOnSxbRr18507drVfP3112b58uWmadOmxsPDwyxevNihjxs/t272lytXLqd9fPzxx7bHa9asMR4eHqZw4cJm7Nix5sMPPzQvvviiyZs3r/Hy8jK//PKL03VVrlw58/LLL9seP/rooyZXrlxmzJgx5vDhwyY5OdkcOHDADB061FgsFjN69GiHPiwWiylTpoztOzsoKMj07ds33e8kZ/r3728CAgLM7NmzzapVq0zFihVN27ZtjY+Pj3n//ffN2bNnzalTp8zrr79uPDw8TFRUlEMfq1evNh4eHqZKlSpmwoQJZsqUKaZ9+/bGx8fHzJkzx6xevdoMHDjQ+Pr6mrJly5r4+HinueSE978xt/4dY+0/ODjY5MqVyzRv3tzExcWZQ4cOmcKFC9stT58+fZzOn1O+p4zhu8odFM53uGLFiplhw4ZlKHbo0KGmaNGiDu2vvvqqsVgsZtSoUea3334zq1evNnXr1jUeHh7mqaeesotN78Nk6NChJjAw0CxZssT89NNPpnr16qZUqVKmRo0aZvfu3ba4devWGW9vb/Pss8869FGyZEkTHBxsJk2aZI4cOeLwt2HDBmOxWMz7779va7uRxWKx++KLiooyFovFvPbaa+by5cvGmGs/JDz33HPGYrE4LTh9fX3NvHnzjDHGJCUlGYvFYubPn+90nU6YMMEEBwc7nWaxWMwLL7xgBgwYYEJCQozFYrEVqJ999plJTU11Op/Vs88+a7y9vc3ixYtNWlqaMcaYHTt2mPLly9tel5MnT5rOnTsbDw+PdL9wdu7caYKCgkxwcLDp27evmTVrllm9erVZt26dWb16tZk1a5bp06ePCQ4ONkFBQWbnzp0Ofezdu9f4+PgYHx8fU7NmTVOkSBFjsVhMjRo1TGxsrF1setuI9fUrWLCgqVSpkvHw8DBPPvmkeeyxx0yDBg3M+++/byZNmmT70F6zZo1DH0FBQS7/AgMDjcViMf7+/rZlvtGNO0c//fST8fb2NgEBAaZ3795m7Nixpnv37sbHx8cEBgaav/76y6GPe+65x4wYMcL2+MknnzQWi8X06tXLrF+/3vz555/m+++/Nx07djQeHh5m0qRJTl8bi8VicufObftAL1mypHnllVec/rCUnueee854enqaUaNGmXfffdcULlzY9O3b1+TKlcsMHz7cbN++3fz000+2HEeNGuXQx7Zt24yPj48pVKiQeeKJJ8yQIUNM9erVjY+Pj5k0aZKZMWOGadOmjbFYLKZOnTrp/kiXEbdzBzyn7Dyz42xv3bp1xsvLy4SHh5tXXnnFfPfdd+aPP/4whw4dMn/88Yf57rvvzLhx40zx4sWNl5eXWbduXYaX0Zn0trGsKozultfmww8/zPBfp06dnK7Thx56yBQrVsycOnXK1rZq1SqTN29eU6BAAbNr1y5be3rv28aNG5s6deqYq1evGmOMGTt2rPHy8jJt2rSxi7t69aqpUKGCadGihUMfFovFBAcHm8cff9z06tXL4a9Tp07GYrGYxo0b29qc9XH99lGvXj1TtGhRc+7cObu4Y8eOmdy5c5suXbo49GGMMf7+/mbOnDnGGGPS0tKMv79/uvtp3bt3N6VLl043lxMnTphx48aZUqVK2b4nKleubCZOnGjOnDnjtE+r0qVLm6FDh9oef/3118ZisZjnn3/eIfaxxx4zJUuWdGhv0KCBqVmzprly5Ypd+wsvvGBKlSplexwdHW38/f3tvhetcsr7nx95HPFdlXkUznc4X19fM3v27AzFzp492/j6+jq0V6tWzTz66KN2bVevXjXPPPOMsVgspl+/frb29D5MKlSoYAYNGmR7/M033xiLxWLGjBnjENujRw9TsWJFh/akpCQzbNgw4+npaR566CFz7Ngxu+nWo4k3OxJw/YdJgQIFTNeuXZ3GNmnSxFSvXt2hvUyZMrYP/8uXLxtPT0+nH37GGDNx4kQTGBh401wuXrxo5s2bZ+6//37bl1/+/PnN0KFDze+//+50/uLFi9utU6vVq1cbLy8v25dmWlqaqVWrVrpHvlu1amXKlCljYmJinE63iomJMaVLlzYPPvigw7SuXbuavHnzmj/++MP2nO+++67x9fU1ZcqUsSv00ttG2rZtaypXrmwuXrxojDFm4MCBJiAgwDRs2NC2w2TMtaO3xYoVMw899JBDH9ado0aNGjn9s34RVq5c2dbmrI/rt5HmzZubvHnzmv3799vF/frrr8bX19fpEePAwEAzc+ZM2+OQkJB0f11u06aNqVSpktNp1ly2bt1q+vTpY4KCgmxHd1u3bm2WLVvmsMNyo/Lly5sBAwbYHi9dutRYLBa7Nqt27dqZ8uXLO7Rbt5ELFy7Ytffs2dNUq1bN9njFihXG09PT4QennLIDnlN2ntlxduyjRo0aJjEx0WW+//zzj6lWrZp54IEHHKZlxTaWVYXR3fLa3Hg05mZ/ztZp8eLFzauvvurQvm/fPlOsWDGTL18+s2PHDmNM+u/bwoULm3feecf2eO/even+YD127FiTP39+h/YZM2aYkJAQU7NmTRMdHe0wff/+/Znaf7hy5Yrx9PQ0b7/9ttPYYcOGmSJFijidlidPHjNt2jRjzLXvM4vFYr744gunse+//77x8fFxmYvV999/bx599FHj6+trLJZrZ5917tzZfPPNN0779vPzs30OGXPtOz69XN5//33j7e3t0B4UFOT0h1/ra3T9D+1PP/200++XnPL+50ceR3xXZR6F8x2uQoUK5pFHHslQbMeOHU2FChUc2oOCgswHH3zgdJ5Ro0YZi8ViKwrS+zDx9/c3s2bNsj0+ceKEsVgsZsWKFQ6xU6ZMMf7+/unmuX37dlOjRg0TGBho3nrrLdubILOFs/ULa8GCBU5j3377badF77Bhw0yBAgVsH65t27Y1999/v+2ItdXFixfNPffcY+rWrXvTXK73119/meeff94UKlTI9uFSt25duy84Y4zx9va2K86sjh07ZiwWi/nxxx9tbe+8847Jly+f0zyCg4PtTj13ZdKkSU6P0pYqVcrpkcrNmzebPHnymFKlSpmjR48aY9LfRooVK2YmTJhge7xr1y5jsVicLuOIESNM4cKFHdpfeOEF4+XlZdq3b+/ww4oxmd85SktLMz4+PumeGv7UU0+Z8PBwh/bg4GDz/vvvG2OufdlbLOmfHj5jxgynO0Y35mLMtW129uzZpk6dOrZto2DBgub55593OC3LKiAgwO69d/z48XTXgfXHjhvlzp3bvPnmmw7tu3btMh4eHrYfTIy59sPXvffe67AcOWEHPKfsPLPjbC8gIMC89957TnO80YwZM0xAQIBDe1ZsY1lVGN0tr01gYKC5//77zeeff37Tv65du6b73T937lyny3jo0CETHh5u8uXLZ7Zv357u+9bHx8d2lpcxxpw5c8ZYLBanZ1HNnj3b6bow5trlUw8//LDx9PQ0zzzzjN1lJZndf0hISDAWi8V89dVXTmNnzZqVbh7NmjWz+wE6LCzM5XdMWFiYy1xudO7cOTN16lTbZWgeHh6mRIkSDnFFixa1u+TO+p17/bq2eu2110xQUJBDe0BAgMtt7Pr9kDlz5hg/Pz+nfeSE9z8/8jjiuyrzuB3VHW7AgAFaunSpunTpoi1btujy5ct20y9fvqzNmzerc+fO+uKLLzRgwACHPnx9fR3ms3rllVf00ksvad68eerdu7fS0tKcxnl5ednd/so62rOzC/h9fX3tBsi4UY0aNRQdHa2XX35ZY8aMUc2aNbV169Z049Pj7e2tXLlyKTg42On0wMBAp4OUvPjii/L391dERIRmzJih/v37a9++fapYsaJeeuklzZgxQyNHjlSFChX0xx9/6IUXXshUXuXKldObb76pEydOaOnSpXrwwQcVHR2t/v3728UVLFhQe/bscZj/999/l8ViUVBQkK0tJCRESUlJTp/PGJPhgcks1w34cb2TJ0+qVKlSDu316tXTd999p/Pnz6tRo0Y6cuRIun2fPn1ahQoVsj0uXLiwJCk8PNwhtlSpUjp79qxD+xtvvKGff/5ZsbGxqlSpkt555x27bTKjy2mVmJio1NRUVa1a1en0atWq6eTJkw7tVapU0fr16yVd247y5s2b7rIfOXJEISEhGconICBAffv21datW7Vnzx4NHjxYxhi9/fbbqlSpktOBSoKCgnTx4kW7ZZLkdHtISkqSh4fjx35KSorTQQMDAgJkjLF7LRo0aOBwS7iAgADVq1dPS5Ysuenfo48+6nTZ4+LibNvE9cqWLauNGzcqMDBQzZo106+//up0fkl66qmntHfvXhUrVkx169ZVVFSU/vnnH9v0zG4fSUlJunr1qt1gQderUKGC3V0MrGrWrKnVq1dLurZuihQpot27dzvtY9euXQoNDc1QPk2bNtXixYsVGxurKVOmqEKFCvr888/14IMPOh3MLl++fHb5Wf9//vx5h9hz5845HUQmvc98K+v2Jl1b7mPHjjnEeHt7KyEhwWU/VgkJCU4H7MmKbex67r626bkTX5uqVavqwoUL6tSp003/Kleu7PQ5ihQposOHDzudVrJkSW3YsMH23o2OjnYalzdvXrt1kStXLuXLl8/pXSPOnTtn9913vcKFC2v58uX67LPPtHz5clWoUEGfffaZ09j0nD17VseOHbM9j7PXQ7r2OqU3QNHgwYP19ddf67XXXlNaWppeeuklTZgwQfPmzbPtayUnJ2vKlCmaNWuW2rdvn6kc8+TJo6ioKO3cuVPR0dEaMGCA3eBlVtWrV9fcuXNtg3q99dZb8vHx0bJly5SSkmKLi4+P16xZs5y+xtZBqm7cV5o3b548PDxUrlw5u36cfY/klPd/VnzHnDt3zm5QOetnt7NB0cLCwhQfH+/QnlO+pyS+q9ziVrmNHCMtLc0MGjTIdi2Ep6enKVSokAkPDzeFChUynp6etmlRUVG2a2WvV6dOHfPYY4+5fJ6XX37ZWCwWU6JECae/wlWsWNG8+OKLtsfJyclm+vTpTk8PHjlypClevHiGlu/QoUOmRYsWJleuXKZdu3bGw8Pjpr/CNWzY0PTu3dv07t3bBAUFmYkTJzqNHTlypNNrvo0x5ujRo3anVV//K6b1cXBwsNOjpdfnkt4vxjeKiYkxr732ml3bE088YXx8fMynn35qa9u1a5cpV66cKVKkiN1r+eKLL9qdinK9pk2bmjJlypiTJ0+6zOHkyZOmdOnSplmzZg7TSpQoYcaPH5/uvNu3bzd58+Y1JUqUMOPGjXO6jRQoUMDutYiLizMWi8WsXbvWIXbixIkmd+7c6T7f1atXbafJV61a1Wzbts0Yk/GjCosWLbI9Dg4Otnt8Yx4hISEO7R999JHx9PS0vb7jxo0zefLkcbg264svvjD+/v7pvr8yso2kpqaaJUuWmFatWhlPT0+H6U2bNjURERG2a+atlzvceGrW5cuXTdWqVe1OvbaqXr26adq0qUP7m2++aTw8POxOxZ86darDa1O/fn1zzz33uFwOq/SuPytTpozT66esDh8+bMLDw02ePHnMs88+m+7ALVbLli0zYWFhpkiRIrb3UEa3j6lTp5qjR4+aI0eOmODgYPPRRx85jX3zzTdN3rx5HdpXrlxpLBaLefXVV83Vq1fNBx98YPz8/MzcuXNtr9OlS5fM5MmTjaenp3n66aed5pGRz49ffvnFPPnkk07fL9ZT88+dO2fS0tLMY489Znx9fU27du3srlO/cOGCKVmypKlTp45DH/Xq1Uv39LdcuXKZ06dP29omTpzo9MyX9u3bm0KFCtmNd+HM7t27TcGCBc3DDz/sMC0rtrGseG2t/dwNr01UVJTx9PRMd8DA640fP95YLBaH9kcffdTUqFHD5byHDx+27Ts4e10aNWrkchDF6/Xt29fpZ9iNEhISzJNPPmk8PDxMq1atbJePZWTQt+u/55988kmnsb1793Y48+Z6Y8aMMRaLxRQqVMi0b9/eFChQwLaPlj9/ftv1oBUrVnQ6+FNm9h+MufZ5cqMtW7aYXLlyGR8fHxMSEmI8PDzM008/bV599VVTunRp8+yzz5qBAweasLAw4+Hh4fSyv88++8xYLNeu6X3jjTfMtGnTTIcOHYyHh4fD2Y6PPvqoqV+/vkMfOeX9nxXfMYULFzZvvfWW7fG5c+dMaGio2bRpk0Psm2++me6ZgFbZ+T1lDN9V7qBwvkvs2bPHvPjii6Zly5amSpUqpnTp0qZKlSqmZcuWtkG/0jN69GgTHBx809Fyx44dm+4pMN27d3e64+1MnTp1TLt27TIUa7VgwQJToECBDH2Y3Pjn7A1mjDF169ZN97pgq40bN5rhw4ebDh06mGbNmpl27dqZp556ysybN89hlG1nuWTmi+9Gp06dMuHh4cbDw8MEBATYRgrMlSuX+fzzz+1iK1WqZLp16+a0n23bthlfX18TEhJi+vfvb+bMmWO+/vprs379evP111+bOXPmmH79+pmQkBDj6+trK0Kv1759e6fXHV1v+/btJl++fLYdghvVq1fPYbC5xMREu+ubrZ5++mlTuXJll89njDFHjhwxDz74oMmVK5d54oknbKNRZmbnyMPDI91rep566imn1+Mbc23HyWKxmIiICDNo0CDbjlGxYsVMRESEKVKkiPHw8DCFChWyncbuLJfMbCPOBg1bvXq1sVgsJiwszNxzzz22wUqee+45U79+fTNx4kTz1ltv2UbOv/5L3+q9994zFovFtGnTxnz66afmq6++MkOGDDE+Pj6mSZMmdrF9+vQxtWrVsmvLKTvgN8rOnWd2nP/fX3/9ZfLmzWs8PT1N8+bNzejRo837779v5s2bZ95//30zevRo06xZM+Pp6Wny5cvn9LKErNjGsuq1vVtem59//tmMGTPGbocyPUePHjUbNmxwaF+wYIGxWCzmhx9+cDn/kSNHTMmSJZ2+b9955x0TGRl50xwSExNNSEiI04GB0vPjjz+aypUr2w4iuHrvjxkzxuHP2WVO8fHxJk+ePHZ3VnDmu+++M40bNzaenp4O+yXh4eFm1KhR6e533er+g9WaNWtMixYtTN26dc2YMWNMamqquXLliunevbstF09PT6d3XLF6/fXXjY+Pj13+LVq0cBgTY/To0Wb58uUO8+eU9z8/8jjHd1XmUDjD7Nu3zzb67s1Mnz7d6UAFf/31l9Mjhzf6+++/TYcOHcySJUsynWdKSoq5cOGCw7XG7jh79qwZMmSI02uws8qYMWNc/mCREadPnzbPPvusqVq1qqlQoYLp1KmT0182ExMTXY7SvXXrVhMREeFw5Pz6o+kRERHpjoI4c+ZMY7FY7AbPcObXX3+1Ffg3+t///mcaN258kyW+doS1YMGCN90pud6iRYtMwYIFTVBQ0E13jpwNxuFskIjk5GRTuHBh06NHj3T7mj17tilTpozTH2z8/PzMY4895vRabKus2jmaOXOmKV++vClcuLDp1auXSUhIMElJSaZBgwZ2OT3yyCPpvn+so25fv11UrlzZoVjv27evw5gIOWUHPD3ZtfPMjvP/O3jwoOncubPtmrcb/3x9fU3nzp3NgQMHnM6fFdtYVr22d9trc6uSk5NvOoihMdfGg8jMLRlvdPHiRbNz5850b0WTntTUVDNx4kQzePDgmx71zIgrV66YCxcuOL01jzMXL140u3btMj/++KPZvn273eBU2enYsWNm69atDgNKOXPq1CmzfPly88knn7i1X5MT3v/8yJM+vqsyzmKMi4tNAdxVDh48qOjoaMXGxiopKUn+/v4qUqSIIiIiVLp06XTnS0lJ0ZEjR5Q/f36763ucOXbsmA4fPqyGDRu6leOFCxe0ceNGValSxel11ek5f/68xowZo2PHjmn48OG677773Hp+q6SkJO3fv1+FChVSwYIFXcb+9ddf+uuvv/TPP//Iz89PRYsWVZUqVeTn5+dyvqNHjyp//vxOrwvLCsYY/fjjj4qJiVGlSpV07733uozftWuX1q9fr5SUFFWuXFkPPvigcuXKdVtycyYlJUWenp43fc7ExESdPXvW6fXx6bl8+bKmT5+uY8eOqU+fPqpSpcot5Xr16lUlJibKz8/P6TV510tKStKBAwfsto8CBQrc0vNnhePHjysmJkbly5dXnjx5XMaePn1aW7ZssW0b99xzj1vPmZSUpN27dzt8BlWpUkUBAQFu9ZnVMvPa3i7Z8drcKD4+XnPmzNFDDz2kMmXK0EcW9JGTcsmOPrL7/X87v2OuZ91/KFq0qPLly5fh+bLze0riuyojKJwBAABg5+DBgypXrpw+//xzdejQgT6yoI/r+1myZIk6duyYbbnklDyAO4lndicAAPi/9u4/Jso6jgP4+7kbKHJc0hUStiGoix9DBGPdaYjBKqO8Rs4K1JM5KgvSRKZ/VIImVkA0sqw1b+h0/BFia2VTZEKrSRk55rCaTDspR6ZYUNxlAt/+cNy87rnr8OB4eHi/NjZ57vt93+frfx+e5/l+J75r167BarWio6MDM2bMQF5eHubOnes2rqmpCTt37sTx48eZMQEzIiMjkZubO+KMK1euYO/evfj999+RnZ2NRYsWAbixU/7u3btx9epVmEwmVFVVeXwyQikZI8l56623PN41Gu/1rF+/3uP6gBt3E4UQ2LNnD5qbmyFJEmpqapjhJWMkOVarFS0tLeO+nrGuQ47dbkdNTQ0OHz6MK1euYMaMGTCbzSgqKpLdMXmyZURGRmLZsmUBr2Os1jORM9zc0gPeRKRK+/fv9+k9ZGYENkNJtchl9Pf3OzcfG36vKDg42OUM0WGezsdkhnozuru7xcyZM132WKirqxPvvPOOmDJlijAajSI5OVlotVpx++23y26Cp5QMJdXib8bwHLl3Tm/OvPnf/8UMd0qpRSkZYWFh4qOPPnL+3tvb6zyDesqUKSImJkYEBQUJjUYjFi5cKPvuODNGP0NJtSglwxdsnInIydMxDswY3wwl1SKXUV5eLiRJcu7g//nnnwuTySQ0Go3bTuqemitmqDejuLhY6HQ6UV9fL7755huRkpIiYmNjRWpqqstmTcePHxfBwcFiw4YNis1QUi3+ZsTExAi9Xi/efvttYbPZ3H5aWlqEJEnigw8+cF77L2a4U0otSsmQJNfNo1588UUhSZLYuXOnc7PKv//+W5SUlAhJksQbb7zBjABkKKkWpWT4go0zETkpuTmbzBlKqkUuY/78+eLpp592uTY4OCiKioqEJEmioKDAed1Tc8UM9WbExcWJ9evXO38fPm6lrKzMbazFYpE9Ak4pGUqqxd8Mu93uPPfdbDa7nQDgy3myzFBuLUrJ+G9DExERIXJzc2XHZmZmipSUFGYEIENJtSglwxd8x5lI5UayM3Vvby8zApShpFr8zTh37hyef/55l2sajQa7du3C9OnTUV5ejqGhIVitVo+5zFBvRldXl8t7vomJiQCAlJQUt7ELFizAwYMHFZuhpFr8zQgJCUFVVRXy8vLwzDPPICEhAaWlpdi4caPPu+kzQ7m1KCXjZv39/bh8+TKys7NlP8/OzkZZWRkzApyhpFqUkuEJG2cilbPZbAgPD0dUVNT/jrXb7cwIUIaSavE3Y+rUqbh+/brs+Ndeew1arRbbt2/H0NAQMjMzZccxQ70ZQUFBGBgYcMkEAJ1OJ/t9QuawD6VkKKmW0VpPamoqvv32W1RXV6OsrAwHDhzA+++/P6JjaJih3FqUkgEAwcHB0Gq10Ov1sp/rdDoMDg4yI8AZSqpFKRmesHEmUrmYmBjMmTMHR48e/d+xO3bsQGlpKTMCkKGkWvzNmD17Nr7++msUFhbKzhn+y+727dvR0tIiO4YZ6s2IiorCL7/84vxdp9Nh165diIuLcxs7fLa5UjOUVMtorQe48RRBSUkJli9fjnXr1iE9PR3Z2dmQJMnjHGZMnFrGO+PDDz9EU1MTgBt3sc+dOyc7rqury+O5x8wY/Qwl1aKUjP91Sw94E9GEsWLFCnHHHXf4NNbTO7DMGP0MJdXib8arr74q9Hq96Ovr8zp327ZtHndeZYZ6M1auXCmysrK8zh9mNBrFsmXLFJuhpFpGaz1y9u/fLyIiInx6p5cZE6+WQGbI7cRtNBplx5pMJrF06VJmBCBDSbUoJcMXvONMpHIpKSk4ePAgbDYbZs2a5XVsdHQ0Fi9ezIwAZCipFn8zVq9ejevXr6OzsxOpqake527duhUGgwFtbW1unzFDvRlbt251uTPqyaVLl3DXXXchLy9PsRlKqmW01iNn1apVePLJJ+FwOBAaGurzPGZMjFoCmTE0NORT3tWrV2E0GrFkyRJmBCBDSbUoJcMXkhAeXnohIiIiIiIiImjGuwAiIiIiIiIiJWPjTEREREREROQFG2ciIiIiIiIiL9g4ExEREREREXnBxpmIiGiSkyQJ+fn5410GERGRYrFxJiIiUqHz58/j2WefRVxcHKZNm4bw8HDEx8djzZo1aG5uHu/yfNbS0gJJklBVVeVyXZIk549Go0FYWBhiY2ORk5OD2tpaOByOcaqYiIjUiOc4ExERqUxbWxsyMjIQFBQEi8WCxMREOBwOdHZ2orGxEWFhYXjggQfGu0y/zZ8/H5s2bQIA2O12dHV1obGxEWvXrkV5eTkaGhqQnJw8zlUSEZEasHEmIiJSmW3btsFut6O9vV22cfz111/HoarRN3PmTKxatcrl2o4dO1BfX4+VK1fikUcewZkzZxAeHj5OFRIRkVrwUW0iIiKV6ezshMFg8Hi3NTIyUvZ6a2srMjIyEBoaCoPBgIKCAvz1119u406fPo2cnBwYDAZMnToVCQkJqKiowODgoMu4/Px8SJKEy5cvw2KxwGAwIDQ0FFlZWTh16pT/C/VgxYoV2Lx5M7q7u/Hee++N2fcQEdHkwcaZiIhIZWbPno2enh4cOnTI5znt7e147LHHkJaWhurqajz00EOwWq0oLi52GdfW1gaTyYTm5masW7cOlZWVuPvuu7FlyxZYLBbZ7KVLl6K7uxtlZWV46aWXnI+Sd3R0+LVObwoKCgAAhw8fHrPvICKiyYOPahMREanMK6+8gmPHjmH58uWYO3cu7r//fqSlpWHJkiWIj4+XnXP69Gm0trbivvvuAwA899xz6OvrQ21tLaqrq6HT6QAAGzZswLVr19Da2op58+YBAIqKivDUU0+hrq4Oa9euRVZWlkt2dHQ0GhoaIEkSAOCJJ55AWloaSkpKcOTIkTH5P5g1axbCwsJw9uzZMcknIqLJhXeciYiIVMZkMuG7777DmjVr0Nvbi9raWrzwwgtISEjA4sWLcf78edk5w03zsMzMTAwMDMBmswEAfvvtN5w4cQJms9nZNAM3drh++eWXAQAff/yxW/bmzZudTTMALFiwAA8++CCamppkHwUfLXq9Hn19fWOWT0REkwcbZyIiIhVKSkrC3r17cenSJdhsNuzbtw/p6en48ssv8fjjj+Off/5xGR8bG+uWYTAYAAA9PT0AgJ9++gkAkJiY6DY2Pj4eGo1GtimXu8udkJCAwcFBXLhwYeSL81FfXx/0ev2Y5RMR0eTBxpmIiEjloqOjYbFY8MUXX2DRokXo6OjAyZMnXcZotVqP84UQY13iqLPZbPjzzz9xzz33jHcpRESkAmyciYiIJglJkpyPY1+8eHHE82NiYgAAZ86ccfvsxx9/xNDQkOyd6x9++MHt2vfffw+tVovo6OgR1+GLPXv2AAAeffTRMcknIqLJhY0zERGRyhw7dgwDAwNu1x0OBxobGwHceFR6pCIiIrBw4UJ8+umnLjtiCyHw+uuvAwBycnLc5lVUVLjctT516hSampqQlZXl3HRsNNXX16OiogJRUVEoLCwc9XwiIpp8uKs2ERGRymzcuBE9PT0wm81ISkrCtGnT8PPPP6Ourg5nz56FxWJBUlLSLWXX1NQgIyMD6enpKCwsRGRkJD777DMcPXoUeXl5bjtqA8CFCxfw8MMPw2w2o7u7G++++y5CQkJQWVnp1zovXryIAwcOALjxR4Guri40Njbi5MmTmDNnDg4dOoTp06f79R1EREQAG2ciIiLVqa6uxieffIKvvvoKDQ0N+OOPP3Dbbbdh3rx52LJlC/Lz8285+95778WJEydQWlqK3bt3o7+/H7GxsXjzzTexadMm2TlHjhxBcXExSktL4XA4YDQaUVlZ6bIz961ob2/H6tWrAQChoaG48847kZycDKvVitzcXISEhPiVT0RENEwSE3HHDyIiIlK8/Px87Nu3b0JuLkZERHQzvuNMRERERERE5AUbZyIiIiIiIiIv2DgTERERERERecF3nImIiIiIiIi84B1nIiIiIiIiIi/YOBMRERERERF5wcaZiIiIiIiIyAs2zkREREREREResHEmIiIiIiIi8oKNMxEREREREZEXbJyJiIiIiIiIvGDjTEREREREROTFv4KkyP63Z8h5AAAAAElFTkSuQmCC"},"metadata":{}}],"execution_count":25},{"cell_type":"code","source":"# 내림차순\ngroup_shop_sum1 = group_shop_sum.sort_values(by='item_cnt_day', ascending=False)\ngroup_shop_sum1['item_category_id'] = group_shop_sum1['shop_id'].astype(str)\n\nfigure, ax= plt.subplots() \nfigure.set_size_inches(11, 5)\n\nsns.barplot(x='shop_id', y='item_cnt_day', data=group_shop_sum1, order=group_shop_sum1['shop_id'], ax=ax)\nax.set(title='Distribution of total item counts by shop id (ordered)',\n       xlabel='Shop ID', \n       ylabel='Total item counts')\nax.tick_params(axis='x', labelrotation=90)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:21.122815Z","iopub.execute_input":"2024-11-27T09:46:21.123115Z","iopub.status.idle":"2024-11-27T09:46:21.779900Z","shell.execute_reply.started":"2024-11-27T09:46:21.123086Z","shell.execute_reply":"2024-11-27T09:46:21.778838Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1100x500 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":26},{"cell_type":"markdown","source":"- 일부 7~8개의 상점이 다른 상점들 보다 판매량이 높음","metadata":{}},{"cell_type":"markdown","source":"---\n\n\n### ➕ 10000개 이하의 상품은?\n\n상점별로 취급 물건이 다를까, 상품 분류를 보고 상점을 추정할 수 있을까...  \n매출이 적은 상품들을 가진 상점들의 sales는 어떨까","metadata":{}},{"cell_type":"code","source":"figure, ax= plt.subplots() \nfigure.set_size_inches(11, 5)\n\n# 상품분류별 총 상품 판매량\ngroup_cat_sum = train.groupby('item_category_id').agg({'item_cnt_day': 'sum'})\ngroup_cat_sum = group_cat_sum.reset_index()\n\ngroup_cat_sum = group_cat_sum[group_cat_sum['item_cnt_day'] < 10000]\n\n# 상품분류별 총 상품 판매량 막대그래프\nsns.barplot(x='item_category_id', y='item_cnt_day', data=group_cat_sum)\nax.set(title='Distribution of total item counts by item category id',\n       xlabel='Item category ID', \n       ylabel='Total item counts')\nax.tick_params(axis='x', labelrotation=90)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:21.781218Z","iopub.execute_input":"2024-11-27T09:46:21.781549Z","iopub.status.idle":"2024-11-27T09:46:22.409403Z","shell.execute_reply.started":"2024-11-27T09:46:21.781517Z","shell.execute_reply":"2024-11-27T09:46:22.408395Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1100x500 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":27},{"cell_type":"markdown","source":"- 상품: 10000 이하에서도 판매량이 극히 적은 카테고리가 존재함  \n\n#### ➕➕ 적은 상품들의 평균 가격","metadata":{}},{"cell_type":"code","source":"# 상품분류별 총 상품 판매량\ngroup_cat_sum = train.groupby('item_category_id').agg({'item_cnt_day': 'sum'})\ngroup_cat_sum = group_cat_sum.reset_index()\ngroup_cat_sum = group_cat_sum[group_cat_sum['item_cnt_day'] < 10000]\n# 상품분류별 평균 상품 가격\ngroup_cat_mean = train[train['item_category_id'].isin(group_cat_sum['item_category_id'].unique())].groupby('item_category_id').agg({'item_price': 'mean'})\ngroup_cat_mean = group_cat_mean.reset_index()\n\nfigure, ax = plt.subplots()\nfigure.set_size_inches(11, 5)\n\n# barplot- 상품분류별 총 상품 판매량\nsns.barplot(\n    x='item_category_id',\n    y='item_cnt_day',\n    data=group_cat_sum,\n    color='skyblue',\n    ax=ax\n)\n\n# lineplot- 상품분류별 평균 가격\nax2 = ax.twinx()  # 동일한 x축을 공유하는 새로운 y축 생성\nax2.plot(\n    group_cat_mean['item_category_id'],\n    group_cat_mean['item_price'],\n    color='blue',\n    marker='o',\n    linestyle='-',\n    label='Avg Price'\n)\n\n# x축 범위를 데이터의 최소값과 최대값으로 설정\nax.set_xlim(-0.5, len(group_cat_sum) - 0.5)\n\n# 제목 및 범례\nax.set(title='Distribution of Total Item Counts and Avg Price by Item Category ID (under 10000)',\n       xlabel='Item category ID', \n       ylabel='Total Item Counts')\nax2.set(ylabel='Avg Price')\n\n# x축 레이블 회전\nax.tick_params(axis='x', labelrotation=90)\n# 꺾은선 그래프에 범례 추가\nax2.legend(loc='upper right')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:22.417361Z","iopub.execute_input":"2024-11-27T09:46:22.417702Z","iopub.status.idle":"2024-11-27T09:46:23.189403Z","shell.execute_reply.started":"2024-11-27T09:46:22.417672Z","shell.execute_reply":"2024-11-27T09:46:23.188260Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1100x500 with 2 Axes>","image/png":"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"},"metadata":{}}],"execution_count":28},{"cell_type":"markdown","source":"### ➕ 10000개 이하의 상점은?","metadata":{}},{"cell_type":"code","source":"figure, ax= plt.subplots() \nfigure.set_size_inches(11, 5)\n\n# 상점별 총 상품 판매량\ngroup_shop_sum = train.groupby('shop_id').agg({'item_cnt_day': 'sum'})\ngroup_shop_sum = group_shop_sum.reset_index()\n\ngroup_shop_sum = group_shop_sum[group_shop_sum['item_cnt_day'] < 10000]\n\n# 상점별 총 상품 판매량 막대그래프\nsns.barplot(x='shop_id', y='item_cnt_day', data=group_shop_sum)\nax.set(title='Distribution of total item counts by shop id',\n       xlabel='Shop ID', \n       ylabel='Total item counts')\nax.tick_params(axis='x', labelrotation=90)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:23.190953Z","iopub.execute_input":"2024-11-27T09:46:23.191419Z","iopub.status.idle":"2024-11-27T09:46:23.510327Z","shell.execute_reply.started":"2024-11-27T09:46:23.191372Z","shell.execute_reply":"2024-11-27T09:46:23.509162Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1100x500 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":29},{"cell_type":"markdown","source":"\n- 상점: 10000 이하에서도 판매량이 적은 상점이 존재함\n  - 적은 이유 추측: 생긴지 오래 안됐거나, 취급하는 카테고리가 다른 상점과 다를지 궁금\n\n### ➕➕ 11, 36 점포","metadata":{}},{"cell_type":"code","source":"# 상점별 date_block_num\ndf_1 = train.groupby('shop_id').nunique('date_block_num')\ndf_1 = df_1.reset_index()\n\nfigure, ax= plt.subplots() \nfigure.set_size_inches(11, 5)\nsns.barplot(x='shop_id', y='date_block_num', data=df_1, order=df_1['shop_id'], ax=ax)\nax.set(title='date_block_num count by Shop Id')\nax.tick_params(axis='x', labelrotation=90)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:23.511619Z","iopub.execute_input":"2024-11-27T09:46:23.511946Z","iopub.status.idle":"2024-11-27T09:46:26.481891Z","shell.execute_reply.started":"2024-11-27T09:46:23.511915Z","shell.execute_reply":"2024-11-27T09:46:26.480701Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1100x500 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":30},{"cell_type":"markdown","source":"- 적었던 11, 36번 점포가 date_block_num이 극히 적은 것을 알 수 있음","metadata":{}},{"cell_type":"code","source":"# date_block_num 찾기\ndf_1 = train[train['shop_id'].isin([11, 36])]\ndf_1['date_block_num'].nunique()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:26.483555Z","iopub.execute_input":"2024-11-27T09:46:26.484053Z","iopub.status.idle":"2024-11-27T09:46:26.514785Z","shell.execute_reply.started":"2024-11-27T09:46:26.484003Z","shell.execute_reply":"2024-11-27T09:46:26.513747Z"}},"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"2"},"metadata":{}}],"execution_count":31},{"cell_type":"markdown","source":"- 11번 36번 점포의 date_block_num이 각각 1개씩으로, 어떤 가게에 대해서는 시계열의 의미가 없는 경우도 있음을 확인","metadata":{}},{"cell_type":"markdown","source":"\n\n### ➕ 전체 카테고리별 평균 가격\n\n카테고리별로 가격의 차이가 있을까, 판매량이 많은 상품은 가격이 어떨까","metadata":{}},{"cell_type":"code","source":"# 상품분류별 총 상품 판매량\ngroup_cat_sum = train.groupby('item_category_id').agg({'item_cnt_day': 'sum'})\ngroup_cat_sum = group_cat_sum.reset_index()\n# 상품분류별 평균 상품 가격\ngroup_cat_mean = train.groupby('item_category_id').agg({'item_price': 'mean'})\ngroup_cat_mean = group_cat_mean.reset_index()\n\nfigure, ax = plt.subplots()\nfigure.set_size_inches(16, 5)\n\n# barplot- 상품분류별 총 상품 판매량\nsns.barplot(\n    x='item_category_id',\n    y='item_cnt_day',\n    data=group_cat_sum,\n    color='skyblue',\n    ax=ax\n)\n\n# lineplot- 상품분류별 평균 가격\nax2 = ax.twinx()  # 동일한 x축을 공유하는 새로운 y축 생성\nax2.plot(\n    group_cat_mean['item_category_id'],\n    group_cat_mean['item_price'],\n    color='blue',\n    marker='o',\n    linestyle='-',\n    label='Avg Price'\n)\n\n# x축 범위를 데이터의 최소값과 최대값으로 설정\nax.set_xlim(-0.5, len(group_cat_sum) - 0.5)\n\n# 제목 및 범례\nax.set(title='Distribution of Total Item Counts and Avg Price by Item Category ID',\n       xlabel='Item category ID', \n       ylabel='Total Item Counts')\nax2.set(ylabel='Avg Price')\n\n# x축 레이블 회전\nax.tick_params(axis='x', labelrotation=90)\n# 꺾은선 그래프에 범례 추가\nax2.legend(loc='upper right')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:26.516049Z","iopub.execute_input":"2024-11-27T09:46:26.516381Z","iopub.status.idle":"2024-11-27T09:46:27.900395Z","shell.execute_reply.started":"2024-11-27T09:46:26.516350Z","shell.execute_reply":"2024-11-27T09:46:27.899336Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1600x500 with 2 Axes>","image/png":"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높은 판매량일 수록 낮은 가격대를 형성하고 있는 것으로 보임","metadata":{}},{"cell_type":"markdown","source":"### ➕ 전체 상점별 평균 매출\n\n상점별로 상품의 팡매량과 매출과 비슷할까","metadata":{}},{"cell_type":"code","source":"# 상점별 총 상품 판매량\ngroup_shop_sum = train.groupby('shop_id').agg({'item_cnt_day': 'sum'})\ngroup_shop_sum = group_shop_sum.reset_index()\n# 상점별 총 매출\ntrain['sales'] = train['item_price'] * train['item_cnt_day']\ngroup_shop_salessum = train.groupby('shop_id').agg({'sales': 'sum'})\ngroup_shop_salessum = group_shop_salessum.reset_index()\n\nfigure, ax = plt.subplots()\nfigure.set_size_inches(16, 5)\n\n# barplot- 상점별 총 상품 판매량\nsns.barplot(\n    x='shop_id',\n    y='item_cnt_day',\n    data=group_shop_sum,\n    color='skyblue',\n    ax=ax\n)\n\n# lineplot- 상점별 총 매출\nax2 = ax.twinx()\nax2.plot(\n    group_shop_salessum['shop_id'],\n    group_shop_salessum['sales'],\n    color='blue',\n    marker='o',\n    linestyle='-',\n    label='Total sales'\n)\n\n# x축 범위를 데이터의 최소값과 최대값으로 설정\nax.set_xlim(-0.5, len(group_shop_sum) - 0.5)\n\n# 제목 및 범례\nax.set(title='Distribution of Total Item Counts and Sales figures by Shop ID',\n       xlabel='Shop ID', \n       ylabel='Total Item Counts')\n\n# 천 단위 수정\nfrom matplotlib.ticker import FuncFormatter\n# y축 포맷팅 설정\nax2.yaxis.set_major_formatter(FuncFormatter(lambda x, _: f'{x/1000:.0f}K'))\n# y축 레이블 설정\nax2.set_ylabel('Total Sales (in 1000s)')\n\n# x축 레이블 회전\nax.tick_params(axis='x', labelrotation=90)\n# 꺾은선 그래프에 범례 추가\nax2.legend(loc='upper right')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:27.901660Z","iopub.execute_input":"2024-11-27T09:46:27.901966Z","iopub.status.idle":"2024-11-27T09:46:28.893303Z","shell.execute_reply.started":"2024-11-27T09:46:27.901937Z","shell.execute_reply":"2024-11-27T09:46:28.892067Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1600x500 with 2 Axes>","image/png":"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이상치를 제거하지 않았음에도 판매량과 sales는 어느정도 비례하는 것을 보아, 가격은 비교적 작지만 판매량이 많은 카테고리에 따라 매출이 결정되는 것으로 추측됨\n\n</br>\n\n### 🔑 분석 정리\n\n- 파생 피처 추가: 상점명과 상품 분류 명의 첫 단어로 추가 예정  \n- 피처 제거: 월별 판매량이 필요하므로 date 컬럼 삭제  \n  - 상점id, 상품id, 상품 분류id는 각각의 name과 매칭되므로 둘 중 하나만 남김  \n- 이상치 제거: 일별 판매량과 판매가에 이상치 존재하므로 삭제 필요\n\n### 🔑 모델링 전략\n\n- 베이스라인 모델: LightGBM  \n  - 피처 엔지니어링: 피처명 한글화, 데이터 다운캐스팅, 데이터 조합 생성, 타깃값 추가\n- 성능 개선: LightGBM  \n  - 피처 엔지니어링: 이상치 제거, 전처리, 파생피처 추가, 인코딩, 결측값 처리\n","metadata":{}},{"cell_type":"markdown","source":"# 9.3 Baseline Model\n---\n\n- 순서\n  1. 피처명 한글화\n  2. 데이터 다운캐스팅\n  3. 데이터 조합 생성\n  4. 파생 피처 추가\n  5. 테스트 데이터 붙이기\n  6. 이외 상점, 상품, 상품 분류 데이터 병합\n  7. train, validation, test 데이터 만들기\n\n","metadata":{}},{"cell_type":"markdown","source":"## 🔍 9.3.1 피처 엔지니어링 I : 피처명 한글화","metadata":{}},{"cell_type":"code","source":"sales_train = sales_train.rename(columns={'date': '날짜', \n                                          'date_block_num': '월ID',\n                                          'shop_id': '상점ID',\n                                          'item_id': '상품ID',\n                                          'item_price': '판매가',\n                                          'item_cnt_day': '판매량'})\n\nsales_train.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:28.894791Z","iopub.execute_input":"2024-11-27T09:46:28.895238Z","iopub.status.idle":"2024-11-27T09:46:28.969985Z","shell.execute_reply.started":"2024-11-27T09:46:28.895170Z","shell.execute_reply":"2024-11-27T09:46:28.968920Z"}},"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"           날짜  월ID  상점ID   상품ID      판매가  판매량\n0  02.01.2013    0    59  22154   999.00  1.0\n1  03.01.2013    0    25   2552   899.00  1.0\n2  05.01.2013    0    25   2552   899.00 -1.0\n3  06.01.2013    0    25   2554  1709.05  1.0\n4  15.01.2013    0    25   2555  1099.00  1.0","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>날짜</th>\n      <th>월ID</th>\n      <th>상점ID</th>\n      <th>상품ID</th>\n      <th>판매가</th>\n      <th>판매량</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>02.01.2013</td>\n      <td>0</td>\n      <td>59</td>\n      <td>22154</td>\n      <td>999.00</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>03.01.2013</td>\n      <td>0</td>\n      <td>25</td>\n      <td>2552</td>\n      <td>899.00</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>05.01.2013</td>\n      <td>0</td>\n      <td>25</td>\n      <td>2552</td>\n      <td>899.00</td>\n      <td>-1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>06.01.2013</td>\n      <td>0</td>\n      <td>25</td>\n      <td>2554</td>\n      <td>1709.05</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>15.01.2013</td>\n      <td>0</td>\n      <td>25</td>\n      <td>2555</td>\n      <td>1099.00</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":34},{"cell_type":"code","source":"shops = shops.rename(columns={'shop_name': '상점명',\n                              'shop_id': '상점ID'})\n\nshops.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:28.971165Z","iopub.execute_input":"2024-11-27T09:46:28.971498Z","iopub.status.idle":"2024-11-27T09:46:28.981428Z","shell.execute_reply.started":"2024-11-27T09:46:28.971467Z","shell.execute_reply":"2024-11-27T09:46:28.980374Z"}},"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"                              상점명  상점ID\n0   !Якутск Орджоникидзе, 56 фран     0\n1   !Якутск ТЦ \"Центральный\" фран     1\n2                Адыгея ТЦ \"Мега\"     2\n3  Балашиха ТРК \"Октябрь-Киномир\"     3\n4        Волжский ТЦ \"Волга Молл\"     4","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>상점명</th>\n      <th>상점ID</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>!Якутск Орджоникидзе, 56 фран</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>!Якутск ТЦ \"Центральный\" фран</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Адыгея ТЦ \"Мега\"</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Балашиха ТРК \"Октябрь-Киномир\"</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Волжский ТЦ \"Волга Молл\"</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":35},{"cell_type":"code","source":"items = items.rename(columns={'item_name': '상품명',\n                              'item_id': '상품ID',\n                              'item_category_id': '상품분류ID'})\n\nitems.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:28.982994Z","iopub.execute_input":"2024-11-27T09:46:28.983446Z","iopub.status.idle":"2024-11-27T09:46:29.003063Z","shell.execute_reply.started":"2024-11-27T09:46:28.983402Z","shell.execute_reply":"2024-11-27T09:46:29.001923Z"}},"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"                                                 상품명  상품ID  상품분류ID\n0          ! ВО ВЛАСТИ НАВАЖДЕНИЯ (ПЛАСТ.)         D     0      40\n1  !ABBYY FineReader 12 Professional Edition Full...     1      76\n2      ***В ЛУЧАХ СЛАВЫ   (UNV)                    D     2      40\n3    ***ГОЛУБАЯ ВОЛНА  (Univ)                      D     3      40\n4        ***КОРОБКА (СТЕКЛО)                       D     4      40","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>상품명</th>\n      <th>상품ID</th>\n      <th>상품분류ID</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>! ВО ВЛАСТИ НАВАЖДЕНИЯ (ПЛАСТ.)         D</td>\n      <td>0</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>!ABBYY FineReader 12 Professional Edition Full...</td>\n      <td>1</td>\n      <td>76</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>***В ЛУЧАХ СЛАВЫ   (UNV)                    D</td>\n      <td>2</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>***ГОЛУБАЯ ВОЛНА  (Univ)                      D</td>\n      <td>3</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>***КОРОБКА (СТЕКЛО)                       D</td>\n      <td>4</td>\n      <td>40</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":36},{"cell_type":"code","source":"item_categories = item_categories.rename(columns=\n                                         {'item_category_name': '상품분류명',\n                                          'item_category_id': '상품분류ID'})\n\nitem_categories.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:29.004301Z","iopub.execute_input":"2024-11-27T09:46:29.004646Z","iopub.status.idle":"2024-11-27T09:46:29.018778Z","shell.execute_reply.started":"2024-11-27T09:46:29.004613Z","shell.execute_reply":"2024-11-27T09:46:29.017724Z"}},"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"                     상품분류명  상품분류ID\n0  PC - Гарнитуры/Наушники       0\n1         Аксессуары - PS2       1\n2         Аксессуары - PS3       2\n3         Аксессуары - PS4       3\n4         Аксессуары - PSP       4","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>상품분류명</th>\n      <th>상품분류ID</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>PC - Гарнитуры/Наушники</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Аксессуары - PS2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Аксессуары - PS3</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Аксессуары - PS4</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Аксессуары - PSP</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":37},{"cell_type":"code","source":"test = test.rename(columns={'shop_id': '상점ID',\n                            'item_id': '상품ID'})\n\ntest.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:29.020074Z","iopub.execute_input":"2024-11-27T09:46:29.020405Z","iopub.status.idle":"2024-11-27T09:46:29.038076Z","shell.execute_reply.started":"2024-11-27T09:46:29.020374Z","shell.execute_reply":"2024-11-27T09:46:29.036961Z"}},"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"   ID  상점ID  상품ID\n0   0     5  5037\n1   1     5  5320\n2   2     5  5233\n3   3     5  5232\n4   4     5  5268","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>상점ID</th>\n      <th>상품ID</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>5</td>\n      <td>5037</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>5</td>\n      <td>5320</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>5</td>\n      <td>5233</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>5</td>\n      <td>5232</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>5</td>\n      <td>5268</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":38},{"cell_type":"markdown","source":"## 🔍 9.3.2 피처 엔지니어링 II : 데이터 다운캐스팅\n\n- pandas의 `info(memory_usage = 'deep')` 를 이용해 데이터프레임의 **실제** 메모리 확인 가능\n\n### data downcasting  \n- 더 작은 데이터 타입으로 변환하는 작업\n- 진행 전 데이터의 min, max를 알아두는 것이 좋음  \n- 범위에 맞춰 type을 줄여주되, 연산이 필요한 컬럼은 많이 늘어나기도 하니 범위를 넓게 잡아주는 것이 좋음  \n- object type을 category type 혹은 datetime type으로 변경하는 것만으로도 용량 절약 가능  \n- [참조](https://hoonnam0424.tistory.com/2)  \n  ","metadata":{}},{"cell_type":"code","source":"sales_train.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:29.039234Z","iopub.execute_input":"2024-11-27T09:46:29.039534Z","iopub.status.idle":"2024-11-27T09:46:29.051179Z","shell.execute_reply.started":"2024-11-27T09:46:29.039507Z","shell.execute_reply":"2024-11-27T09:46:29.050025Z"}},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2935849 entries, 0 to 2935848\nData columns (total 6 columns):\n #   Column  Dtype  \n---  ------  -----  \n 0   날짜      object \n 1   월ID     int64  \n 2   상점ID    int64  \n 3   상품ID    int64  \n 4   판매가     float64\n 5   판매량     float64\ndtypes: float64(2), int64(3), object(1)\nmemory usage: 134.4+ MB\n","output_type":"stream"}],"execution_count":39},{"cell_type":"code","source":"all_df = [sales_train, shops, items, item_categories, test]\nfor df in all_df:\n    print(df.info(memory_usage = 'deep'), \"\\n\\n\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:29.052497Z","iopub.execute_input":"2024-11-27T09:46:29.052871Z","iopub.status.idle":"2024-11-27T09:46:29.640744Z","shell.execute_reply.started":"2024-11-27T09:46:29.052836Z","shell.execute_reply":"2024-11-27T09:46:29.639602Z"}},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2935849 entries, 0 to 2935848\nData columns (total 6 columns):\n #   Column  Dtype  \n---  ------  -----  \n 0   날짜      object \n 1   월ID     int64  \n 2   상점ID    int64  \n 3   상품ID    int64  \n 4   판매가     float64\n 5   판매량     float64\ndtypes: float64(2), int64(3), object(1)\nmemory usage: 299.6 MB\nNone \n\n\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 60 entries, 0 to 59\nData columns (total 2 columns):\n #   Column  Non-Null Count  Dtype \n---  ------  --------------  ----- \n 0   상점명     60 non-null     object\n 1   상점ID    60 non-null     int64 \ndtypes: int64(1), object(1)\nmemory usage: 10.7 KB\nNone \n\n\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 22170 entries, 0 to 22169\nData columns (total 3 columns):\n #   Column  Non-Null Count  Dtype \n---  ------  --------------  ----- \n 0   상품명     22170 non-null  object\n 1   상품ID    22170 non-null  int64 \n 2   상품분류ID  22170 non-null  int64 \ndtypes: int64(2), object(1)\nmemory usage: 4.9 MB\nNone \n\n\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 84 entries, 0 to 83\nData columns (total 2 columns):\n #   Column  Non-Null Count  Dtype \n---  ------  --------------  ----- \n 0   상품분류명   84 non-null     object\n 1   상품분류ID  84 non-null     int64 \ndtypes: int64(1), object(1)\nmemory usage: 14.4 KB\nNone \n\n\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 214200 entries, 0 to 214199\nData columns (total 3 columns):\n #   Column  Non-Null Count   Dtype\n---  ------  --------------   -----\n 0   ID      214200 non-null  int64\n 1   상점ID    214200 non-null  int64\n 2   상품ID    214200 non-null  int64\ndtypes: int64(3)\nmemory usage: 4.9 MB\nNone \n\n\n","output_type":"stream"}],"execution_count":40},{"cell_type":"code","source":"def downcast(df, verbose=True):\n    start_mem = df.memory_usage().sum() / 1024**2\n    for col in df.columns:\n        dtype_name = df[col].dtype.name\n        if dtype_name == 'object':\n            pass\n        elif dtype_name == 'bool':\n            df[col] = df[col].astype('int8')\n        elif dtype_name.startswith('int') or (df[col].round() == df[col]).all():\n            df[col] = pd.to_numeric(df[col], downcast='integer')\n        else:\n            df[col] = pd.to_numeric(df[col], downcast='float')\n    end_mem = df.memory_usage().sum() / 1024**2\n    if verbose:\n        print('{:.1f}% 압축됨'.format(100 * (start_mem - end_mem) / start_mem))\n    \n    return df","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:29.642149Z","iopub.execute_input":"2024-11-27T09:46:29.642585Z","iopub.status.idle":"2024-11-27T09:46:29.651132Z","shell.execute_reply.started":"2024-11-27T09:46:29.642540Z","shell.execute_reply":"2024-11-27T09:46:29.650047Z"}},"outputs":[],"execution_count":41},{"cell_type":"code","source":"all_df = [sales_train, shops, items, item_categories, test]\nfor df in all_df:\n    df = downcast(df)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:29.652422Z","iopub.execute_input":"2024-11-27T09:46:29.652932Z","iopub.status.idle":"2024-11-27T09:46:30.019041Z","shell.execute_reply.started":"2024-11-27T09:46:29.652865Z","shell.execute_reply":"2024-11-27T09:46:30.017973Z"}},"outputs":[{"name":"stdout","text":"54.2% 압축됨\n38.6% 압축됨\n54.2% 압축됨\n39.9% 압축됨\n70.8% 압축됨\n","output_type":"stream"}],"execution_count":42},{"cell_type":"code","source":"all_df = [sales_train, shops, items, item_categories, test]\nfor df in all_df:\n    print(df.info(memory_usage = 'deep'), \"\\n\\n\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:30.020494Z","iopub.execute_input":"2024-11-27T09:46:30.021244Z","iopub.status.idle":"2024-11-27T09:46:30.613926Z","shell.execute_reply.started":"2024-11-27T09:46:30.021173Z","shell.execute_reply":"2024-11-27T09:46:30.612659Z"}},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2935849 entries, 0 to 2935848\nData columns (total 6 columns):\n #   Column  Dtype  \n---  ------  -----  \n 0   날짜      object \n 1   월ID     int8   \n 2   상점ID    int8   \n 3   상품ID    int16  \n 4   판매가     float64\n 5   판매량     int16  \ndtypes: float64(1), int16(2), int8(2), object(1)\nmemory usage: 226.8 MB\nNone \n\n\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 60 entries, 0 to 59\nData columns (total 2 columns):\n #   Column  Non-Null Count  Dtype \n---  ------  --------------  ----- \n 0   상점명     60 non-null     object\n 1   상점ID    60 non-null     int8  \ndtypes: int8(1), object(1)\nmemory usage: 10.3 KB\nNone \n\n\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 22170 entries, 0 to 22169\nData columns (total 3 columns):\n #   Column  Non-Null Count  Dtype \n---  ------  --------------  ----- \n 0   상품명     22170 non-null  object\n 1   상품ID    22170 non-null  int16 \n 2   상품분류ID  22170 non-null  int8  \ndtypes: int16(1), int8(1), object(1)\nmemory usage: 4.6 MB\nNone \n\n\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 84 entries, 0 to 83\nData columns (total 2 columns):\n #   Column  Non-Null Count  Dtype \n---  ------  --------------  ----- \n 0   상품분류명   84 non-null     object\n 1   상품분류ID  84 non-null     int8  \ndtypes: int8(1), object(1)\nmemory usage: 13.8 KB\nNone \n\n\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 214200 entries, 0 to 214199\nData columns (total 3 columns):\n #   Column  Non-Null Count   Dtype\n---  ------  --------------   -----\n 0   ID      214200 non-null  int32\n 1   상점ID    214200 non-null  int8 \n 2   상품ID    214200 non-null  int16\ndtypes: int16(1), int32(1), int8(1)\nmemory usage: 1.4 MB\nNone \n\n\n","output_type":"stream"}],"execution_count":43},{"cell_type":"markdown","source":"## 🔍 9.3.3 피처 엔지니어링 III : 데이터 조합 생성\n\n- 예측해야하는 데이터는 월-상점-상품의 판매량이므로 해당 조합 생성  \n- 데이터가 없는 것은 0으로 간주  ","metadata":{}},{"cell_type":"code","source":"from itertools import product\n\ntrain = []\n# 월id, 상점id, 상품id 조합 생성 \nfor i in sales_train['월ID'].unique():\n    all_shop = sales_train.loc[sales_train['월ID']==i, '상점ID'].unique()\n    all_item = sales_train.loc[sales_train['월ID']==i, '상품ID'].unique()\n    train.append(np.array(list(product([i], all_shop, all_item))))\n\nidx_features = ['월ID', '상점ID', '상품ID'] # 기준 피처\n# 리스트 타입 train을 DataFrame 타입으로 변환 \ntrain = pd.DataFrame(np.vstack(train), columns=idx_features)\n\ntrain","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:30.615363Z","iopub.execute_input":"2024-11-27T09:46:30.615706Z","iopub.status.idle":"2024-11-27T09:46:36.969529Z","shell.execute_reply.started":"2024-11-27T09:46:30.615673Z","shell.execute_reply":"2024-11-27T09:46:36.968541Z"}},"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"          월ID  상점ID   상품ID\n0           0    59  22154\n1           0    59   2552\n2           0    59   2554\n3           0    59   2555\n4           0    59   2564\n...       ...   ...    ...\n10913845   33    21   7635\n10913846   33    21   7638\n10913847   33    21   7640\n10913848   33    21   7632\n10913849   33    21   7440\n\n[10913850 rows x 3 columns]","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>월ID</th>\n      <th>상점ID</th>\n      <th>상품ID</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>59</td>\n      <td>22154</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2552</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2554</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2555</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2564</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>10913845</th>\n      <td>33</td>\n      <td>21</td>\n      <td>7635</td>\n    </tr>\n    <tr>\n      <th>10913846</th>\n      <td>33</td>\n      <td>21</td>\n      <td>7638</td>\n    </tr>\n    <tr>\n      <th>10913847</th>\n      <td>33</td>\n      <td>21</td>\n      <td>7640</td>\n    </tr>\n    <tr>\n      <th>10913848</th>\n      <td>33</td>\n      <td>21</td>\n      <td>7632</td>\n    </tr>\n    <tr>\n      <th>10913849</th>\n      <td>33</td>\n      <td>21</td>\n      <td>7440</td>\n    </tr>\n  </tbody>\n</table>\n<p>10913850 rows × 3 columns</p>\n</div>"},"metadata":{}}],"execution_count":44},{"cell_type":"markdown","source":"## 🔍 9.3.4 피처 엔지니어링 IV : 타깃값(월간 판매량) 추가\n\n- 타깃값은 각 상점의 상품별 월간 판매량이므로 groupby를 이용하여 월간 판매량 집계  ","metadata":{}},{"cell_type":"code","source":"# 앞서 조합을 만들면서 사용한 idx_features 활용\n# idx_features를 기준으로 그룹화하여 판매량 sum \ngroup = sales_train.groupby(idx_features).agg({'판매량': 'sum'})\ngroup = group.reset_index()\n\n# 피처명을 '판매량'에서 '월간 판매량'으로 변경\ngroup = group.rename(columns={'판매량': '월간 판매량'})\n\ngroup","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:36.970661Z","iopub.execute_input":"2024-11-27T09:46:36.970939Z","iopub.status.idle":"2024-11-27T09:46:37.593499Z","shell.execute_reply.started":"2024-11-27T09:46:36.970912Z","shell.execute_reply":"2024-11-27T09:46:37.592507Z"}},"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"         월ID  상점ID   상품ID  월간 판매량\n0          0     0     32       6\n1          0     0     33       3\n2          0     0     35       1\n3          0     0     43       1\n4          0     0     51       2\n...      ...   ...    ...     ...\n1609119   33    59  22087       6\n1609120   33    59  22088       2\n1609121   33    59  22091       1\n1609122   33    59  22100       1\n1609123   33    59  22102       1\n\n[1609124 rows x 4 columns]","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>월ID</th>\n      <th>상점ID</th>\n      <th>상품ID</th>\n      <th>월간 판매량</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>32</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>33</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>35</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>43</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>51</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1609119</th>\n      <td>33</td>\n      <td>59</td>\n      <td>22087</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>1609120</th>\n      <td>33</td>\n      <td>59</td>\n      <td>22088</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1609121</th>\n      <td>33</td>\n      <td>59</td>\n      <td>22091</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1609122</th>\n      <td>33</td>\n      <td>59</td>\n      <td>22100</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1609123</th>\n      <td>33</td>\n      <td>59</td>\n      <td>22102</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>1609124 rows × 4 columns</p>\n</div>"},"metadata":{}}],"execution_count":45},{"cell_type":"code","source":"# train과 group 병합\ntrain = train.merge(group, on=idx_features, how='left')\n\ntrain","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:37.594790Z","iopub.execute_input":"2024-11-27T09:46:37.595115Z","iopub.status.idle":"2024-11-27T09:46:41.922060Z","shell.execute_reply.started":"2024-11-27T09:46:37.595082Z","shell.execute_reply":"2024-11-27T09:46:41.920987Z"}},"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":"          월ID  상점ID   상품ID  월간 판매량\n0           0    59  22154     1.0\n1           0    59   2552     NaN\n2           0    59   2554     NaN\n3           0    59   2555     NaN\n4           0    59   2564     NaN\n...       ...   ...    ...     ...\n10913845   33    21   7635     NaN\n10913846   33    21   7638     NaN\n10913847   33    21   7640     NaN\n10913848   33    21   7632     NaN\n10913849   33    21   7440     NaN\n\n[10913850 rows x 4 columns]","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>월ID</th>\n      <th>상점ID</th>\n      <th>상품ID</th>\n      <th>월간 판매량</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>59</td>\n      <td>22154</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2552</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2554</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2555</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2564</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>10913845</th>\n      <td>33</td>\n      <td>21</td>\n      <td>7635</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10913846</th>\n      <td>33</td>\n      <td>21</td>\n      <td>7638</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10913847</th>\n      <td>33</td>\n      <td>21</td>\n      <td>7640</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10913848</th>\n      <td>33</td>\n      <td>21</td>\n      <td>7632</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10913849</th>\n      <td>33</td>\n      <td>21</td>\n      <td>7440</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>10913850 rows × 4 columns</p>\n</div>"},"metadata":{}}],"execution_count":46},{"cell_type":"markdown","source":"- 메모리 절약을 위해 `group` 처럼 더 이상 필요하지 않은 영역을 해제하는 가비지 컬렉션 수행","metadata":{}},{"cell_type":"code","source":"import gc # 가비지컬렉터 불러오기\n\ndel group # 더는 사용하지 않는 변수 지정\ngc.collect();","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:41.923328Z","iopub.execute_input":"2024-11-27T09:46:41.923663Z","iopub.status.idle":"2024-11-27T09:46:42.082710Z","shell.execute_reply.started":"2024-11-27T09:46:41.923632Z","shell.execute_reply":"2024-11-27T09:46:42.081472Z"}},"outputs":[],"execution_count":47},{"cell_type":"markdown","source":"## 🔍 9.3.5 피처 엔지니어링 V : 테스트 데이터 이어붙이기\n\n- shops, items, items_categories 데이터 이어붙일 시 편하도록 준비\n- 현재 monthnum은 33까지 있고, 테스트 데이터는 이어지는 2015년 11월 판매기록이므로 `34`로 설정","metadata":{}},{"cell_type":"code","source":"test['월ID'] = 34","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:42.084293Z","iopub.execute_input":"2024-11-27T09:46:42.084643Z","iopub.status.idle":"2024-11-27T09:46:42.102345Z","shell.execute_reply.started":"2024-11-27T09:46:42.084610Z","shell.execute_reply":"2024-11-27T09:46:42.101290Z"}},"outputs":[],"execution_count":48},{"cell_type":"code","source":"# train, test 이어붙이기\nall_data = pd.concat([train, test.drop('ID', axis=1)],\n                     ignore_index=True, # 기존 인덱스 무시(0부터 새로 시작)\n                     keys=idx_features) # 이어붙이는 기준이 되는 피처","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:42.103804Z","iopub.execute_input":"2024-11-27T09:46:42.104258Z","iopub.status.idle":"2024-11-27T09:46:42.188248Z","shell.execute_reply.started":"2024-11-27T09:46:42.104178Z","shell.execute_reply":"2024-11-27T09:46:42.187292Z"}},"outputs":[],"execution_count":49},{"cell_type":"code","source":"# 결측값 0으로 대체\nall_data = all_data.fillna(0)\nall_data","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:42.189499Z","iopub.execute_input":"2024-11-27T09:46:42.189847Z","iopub.status.idle":"2024-11-27T09:46:42.335732Z","shell.execute_reply.started":"2024-11-27T09:46:42.189812Z","shell.execute_reply":"2024-11-27T09:46:42.334694Z"}},"outputs":[{"execution_count":50,"output_type":"execute_result","data":{"text/plain":"          월ID  상점ID   상품ID  월간 판매량\n0           0    59  22154     1.0\n1           0    59   2552     0.0\n2           0    59   2554     0.0\n3           0    59   2555     0.0\n4           0    59   2564     0.0\n...       ...   ...    ...     ...\n11128045   34    45  18454     0.0\n11128046   34    45  16188     0.0\n11128047   34    45  15757     0.0\n11128048   34    45  19648     0.0\n11128049   34    45    969     0.0\n\n[11128050 rows x 4 columns]","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>월ID</th>\n      <th>상점ID</th>\n      <th>상품ID</th>\n      <th>월간 판매량</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>59</td>\n      <td>22154</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2552</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2554</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2555</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2564</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>11128045</th>\n      <td>34</td>\n      <td>45</td>\n      <td>18454</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>11128046</th>\n      <td>34</td>\n      <td>45</td>\n      <td>16188</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>11128047</th>\n      <td>34</td>\n      <td>45</td>\n      <td>15757</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>11128048</th>\n      <td>34</td>\n      <td>45</td>\n      <td>19648</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>11128049</th>\n      <td>34</td>\n      <td>45</td>\n      <td>969</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>11128050 rows × 4 columns</p>\n</div>"},"metadata":{}}],"execution_count":50},{"cell_type":"markdown","source":"## 🔍 9.3.6 피처 엔지니어링 VI : 나머지 데이터 병합(최종 데이터 생성)\n\n- 상점, 상품, 상품 분류 데이터 병합","metadata":{}},{"cell_type":"code","source":"# merge shops, items, item_categories data\nall_data = all_data.merge(shops, on='상점ID', how='left')\nall_data = all_data.merge(items, on='상품ID', how='left')\nall_data = all_data.merge(item_categories, on='상품분류ID', how='left')\n\n# data downcasting\nall_data = downcast(all_data)\n\n# garbage collection\ndel shops, items, item_categories\ngc.collect();","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:42.337464Z","iopub.execute_input":"2024-11-27T09:46:42.338214Z","iopub.status.idle":"2024-11-27T09:46:47.793024Z","shell.execute_reply.started":"2024-11-27T09:46:42.338142Z","shell.execute_reply":"2024-11-27T09:46:47.791889Z"}},"outputs":[{"name":"stdout","text":"31.1% 압축됨\n","output_type":"stream"}],"execution_count":51},{"cell_type":"code","source":"# 확인\nall_data.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:47.794650Z","iopub.execute_input":"2024-11-27T09:46:47.795106Z","iopub.status.idle":"2024-11-27T09:46:47.808240Z","shell.execute_reply.started":"2024-11-27T09:46:47.795059Z","shell.execute_reply":"2024-11-27T09:46:47.807020Z"}},"outputs":[{"execution_count":52,"output_type":"execute_result","data":{"text/plain":"   월ID  상점ID   상품ID  월간 판매량                     상점명  \\\n0    0    59  22154       1  Ярославль ТЦ \"Альтаир\"   \n1    0    59   2552       0  Ярославль ТЦ \"Альтаир\"   \n2    0    59   2554       0  Ярославль ТЦ \"Альтаир\"   \n3    0    59   2555       0  Ярославль ТЦ \"Альтаир\"   \n4    0    59   2564       0  Ярославль ТЦ \"Альтаир\"   \n\n                                                 상품명  상품분류ID  \\\n0                                  ЯВЛЕНИЕ 2012 (BD)      37   \n1           DEEP PURPLE  The House Of Blue Light  LP      58   \n2           DEEP PURPLE  Who Do You Think We Are  LP      58   \n3            DEEP PURPLE 30 Very Best Of 2CD (Фирм.)      56   \n4  DEEP PURPLE Perihelion: Live In Concert DVD (К...      59   \n\n                                 상품분류명  \n0                       Кино - Blu-Ray  \n1                       Музыка - Винил  \n2                       Музыка - Винил  \n3  Музыка - CD фирменного производства  \n4           Музыка - Музыкальное видео  ","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>월ID</th>\n      <th>상점ID</th>\n      <th>상품ID</th>\n      <th>월간 판매량</th>\n      <th>상점명</th>\n      <th>상품명</th>\n      <th>상품분류ID</th>\n      <th>상품분류명</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>59</td>\n      <td>22154</td>\n      <td>1</td>\n      <td>Ярославль ТЦ \"Альтаир\"</td>\n      <td>ЯВЛЕНИЕ 2012 (BD)</td>\n      <td>37</td>\n      <td>Кино - Blu-Ray</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2552</td>\n      <td>0</td>\n      <td>Ярославль ТЦ \"Альтаир\"</td>\n      <td>DEEP PURPLE  The House Of Blue Light  LP</td>\n      <td>58</td>\n      <td>Музыка - Винил</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2554</td>\n      <td>0</td>\n      <td>Ярославль ТЦ \"Альтаир\"</td>\n      <td>DEEP PURPLE  Who Do You Think We Are  LP</td>\n      <td>58</td>\n      <td>Музыка - Винил</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2555</td>\n      <td>0</td>\n      <td>Ярославль ТЦ \"Альтаир\"</td>\n      <td>DEEP PURPLE 30 Very Best Of 2CD (Фирм.)</td>\n      <td>56</td>\n      <td>Музыка - CD фирменного производства</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2564</td>\n      <td>0</td>\n      <td>Ярославль ТЦ \"Альтаир\"</td>\n      <td>DEEP PURPLE Perihelion: Live In Concert DVD (К...</td>\n      <td>59</td>\n      <td>Музыка - Музыкальное видео</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":52},{"cell_type":"markdown","source":"- 상점명, 상품명, 상품분류명 drop","metadata":{}},{"cell_type":"code","source":"all_data = all_data.drop(['상점명', '상품명', '상품분류명'], axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:47.809741Z","iopub.execute_input":"2024-11-27T09:46:47.810232Z","iopub.status.idle":"2024-11-27T09:46:48.001764Z","shell.execute_reply.started":"2024-11-27T09:46:47.810169Z","shell.execute_reply":"2024-11-27T09:46:48.000750Z"}},"outputs":[],"execution_count":53},{"cell_type":"markdown","source":"## 🔍 9.3.7 피처 엔지니어링 VII : 마무리\n\n- 월 기준으로 훈련, 검증, 테스트 데이터 분리\n- 타깃값은 최대 20이어야 하므로 0 ~ 20로 제한  ","metadata":{}},{"cell_type":"code","source":"# feature\n# train data\nX_train = all_data[all_data['월ID'] < 33]\nX_train = X_train.drop(['월간 판매량'], axis=1)\n# validation data\nX_valid = all_data[all_data['월ID'] == 33]\nX_valid = X_valid.drop(['월간 판매량'], axis=1)\n# test data\nX_test = all_data[all_data['월ID'] == 34]\nX_test = X_test.drop(['월간 판매량'], axis=1)\n\n# target\n# train data\ny_train = all_data[all_data['월ID'] < 33]['월간 판매량']\ny_train = y_train.clip(0, 20)\n# validation data\ny_valid = all_data[all_data['월ID'] == 33]['월간 판매량']\ny_valid = y_valid.clip(0, 20)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:48.003079Z","iopub.execute_input":"2024-11-27T09:46:48.004160Z","iopub.status.idle":"2024-11-27T09:46:49.060610Z","shell.execute_reply.started":"2024-11-27T09:46:48.004123Z","shell.execute_reply":"2024-11-27T09:46:49.059489Z"}},"outputs":[],"execution_count":54},{"cell_type":"code","source":"del all_data\ngc.collect();","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:49.061797Z","iopub.execute_input":"2024-11-27T09:46:49.062270Z","iopub.status.idle":"2024-11-27T09:46:49.175027Z","shell.execute_reply.started":"2024-11-27T09:46:49.062221Z","shell.execute_reply":"2024-11-27T09:46:49.173937Z"}},"outputs":[],"execution_count":55},{"cell_type":"markdown","source":"## 🦾 9.3.8 모델 훈련 및 성능 검증\n\n- **LightGBM** 활용  \n- 기본 파라미터만 설정  \n- 상점, 상품, 상품 분류가 범주형  \n  - 상품은 고윳값 개수가 많으므로 범주형으로 취급 X  \n  - 상점과 상품 분류만 categorical_feature 파라미터에 전달함  \n\n\n### LightGBM\n- 리프 중심의 트리 확장 방식  \n- 범주형 피쳐를 인코딩 없이 처리 가능  \n- Random Foorest와 달리 부스팅을 사용하여 순차적 학습  \n- XBGoost에 비해 학습 속도가 빠르고 메모리 사용량이 적음  ","metadata":{}},{"cell_type":"code","source":"import lightgbm as lgb\nfrom lightgbm import early_stopping, log_evaluation\n\n# LightGBM 하이퍼파라미터\nparams = {'metric': 'rmse',  # 평가지표: RMSE\n          'num_leaves': 255,\n          'learning_rate': 0.01,\n          'force_col_wise': True,\n          'random_state': 10}\n\n# categorical 피처 설정\ncat_features = ['상점ID', '상품분류ID']\n\n# LightGBM train, val 데이터셋\ndtrain = lgb.Dataset(X_train, y_train)\ndvalid = lgb.Dataset(X_valid, y_valid)\n\n\n# LightGBM 모델 훈련\n\n# 50회 반복마다 검증지표 출력(verbose_eval 파라미터 대체)\ncallbacks = [lgb.log_evaluation(period=50)]\n\nlgb_model = lgb.train(params=params,\n                      train_set=dtrain,\n                      num_boost_round=500,\n                      valid_sets=(dtrain, dvalid),\n                      categorical_feature=cat_features,\n                      callbacks=callbacks) # verbose_eval 대체\n                      # verbose_eval=50 # 3.3.0 버전 이후 제공하지 않습니다.","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:46:49.176463Z","iopub.execute_input":"2024-11-27T09:46:49.176814Z","iopub.status.idle":"2024-11-27T09:49:59.323710Z","shell.execute_reply.started":"2024-11-27T09:46:49.176783Z","shell.execute_reply":"2024-11-27T09:49:59.322642Z"}},"outputs":[{"name":"stdout","text":"[LightGBM] [Info] Total Bins 426\n[LightGBM] [Info] Number of data points in the train set: 10675678, number of used features: 4\n[LightGBM] [Info] Start training from score 0.299125\n[50]\ttraining's rmse: 1.14777\tvalid_1's rmse: 1.06755\n[100]\ttraining's rmse: 1.11425\tvalid_1's rmse: 1.0386\n[150]\ttraining's rmse: 1.09673\tvalid_1's rmse: 1.02671\n[200]\ttraining's rmse: 1.08573\tvalid_1's rmse: 1.02027\n[250]\ttraining's rmse: 1.07722\tvalid_1's rmse: 1.01661\n[300]\ttraining's rmse: 1.0698\tvalid_1's rmse: 1.0138\n[350]\ttraining's rmse: 1.06317\tvalid_1's rmse: 1.01084\n[400]\ttraining's rmse: 1.05734\tvalid_1's rmse: 1.00936\n[450]\ttraining's rmse: 1.05224\tvalid_1's rmse: 1.00818\n[500]\ttraining's rmse: 1.04792\tvalid_1's rmse: 1.00722\n","output_type":"stream"}],"execution_count":56},{"cell_type":"markdown","source":"## 🦾 9.3.9 예측 및 결과 제출","metadata":{}},{"cell_type":"code","source":"# predict\npreds = lgb_model.predict(X_test).clip(0, 20)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:49:59.324866Z","iopub.execute_input":"2024-11-27T09:49:59.325159Z","iopub.status.idle":"2024-11-27T09:50:06.122206Z","shell.execute_reply.started":"2024-11-27T09:49:59.325129Z","shell.execute_reply":"2024-11-27T09:50:06.121131Z"}},"outputs":[],"execution_count":57},{"cell_type":"code","source":"# generate a submission file\nsubmission['item_cnt_month'] = preds\nsubmission.to_csv('submission-baseline.csv', index=False)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T09:50:06.123419Z","iopub.execute_input":"2024-11-27T09:50:06.123765Z","iopub.status.idle":"2024-11-27T09:50:06.585454Z","shell.execute_reply.started":"2024-11-27T09:50:06.123734Z","shell.execute_reply":"2024-11-27T09:50:06.584426Z"}},"outputs":[],"execution_count":58},{"cell_type":"code","source":"# 메모리 사용 최적화를 위해 factory reset하거나 아래 코드 실행해주세요!\ndel X_train, y_train, X_valid, y_valid, X_test, lgb_model, dtrain, dvalid\ngc.collect();","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 9.4 성능 개선\n---\n\n- 개별 데이터 피처 엔지니어링: 전처리, 파생 피처 생성, 인코딩\n- 데이터 조합 생성 후 파생 피처 추가\n- 테스트 데이터 이어붙인 뒤 위에서 피처 엔지니어링한 데이터 병합\n- 시차 피처 생성\n\n## 🔍 9.4.1 피처 엔지니어링 I : 피처명 한글화와 데이터 다운캐스팅\n\n- 베이스라인에서 진행했던 부분 일부 수행","metadata":{}},{"cell_type":"code","source":"# 위에서 메모리 정리를 했기 때문에 데이터를 일부 다시 불러옵니다.\ndata_path = '/kaggle/input/competitive-data-science-predict-future-sales/'\n\nsales_train = pd.read_csv(data_path + 'sales_train.csv')\nshops = pd.read_csv(data_path + 'shops.csv')\nitems = pd.read_csv(data_path + 'items.csv')\nitem_categories = pd.read_csv(data_path + 'item_categories.csv')\ntest = pd.read_csv(data_path + 'test.csv')\nsubmission = pd.read_csv(data_path + 'sample_submission.csv')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:21.541275Z","iopub.execute_input":"2024-11-27T10:02:21.542062Z","iopub.status.idle":"2024-11-27T10:02:22.969893Z","shell.execute_reply.started":"2024-11-27T10:02:21.542017Z","shell.execute_reply":"2024-11-27T10:02:22.968688Z"}},"outputs":[],"execution_count":108},{"cell_type":"code","source":"sales_train = sales_train.rename(columns={'date': '날짜', \n                                          'date_block_num': '월ID',\n                                          'shop_id': '상점ID',\n                                          'item_id': '상품ID',\n                                          'item_price': '판매가',\n                                          'item_cnt_day': '판매량'})\n\nshops = shops.rename(columns={'shop_name': '상점명',\n                              'shop_id': '상점ID'})\n\nitems = items.rename(columns={'item_name': '상품명',\n                              'item_id': '상품ID',\n                              'item_category_id': '상품분류ID'})\n\nitem_categories = item_categories.rename(columns=\n                                         {'item_category_name': '상품분류명',\n                                          'item_category_id': '상품분류ID'})\n\ntest = test.rename(columns={'shop_id': '상점ID',\n                            'item_id': '상품ID'})","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:22.971725Z","iopub.execute_input":"2024-11-27T10:02:22.972056Z","iopub.status.idle":"2024-11-27T10:02:23.023696Z","shell.execute_reply.started":"2024-11-27T10:02:22.972022Z","shell.execute_reply":"2024-11-27T10:02:23.022650Z"}},"outputs":[],"execution_count":109},{"cell_type":"code","source":"def downcast(df, verbose=True):\n    start_mem = df.memory_usage().sum() / 1024**2\n    for col in df.columns:\n        dtype_name = df[col].dtype.name\n        if dtype_name == 'object':\n            pass\n        elif dtype_name == 'bool':\n            df[col] = df[col].astype('int8')\n        elif dtype_name.startswith('int') or (df[col].round() == df[col]).all():\n            df[col] = pd.to_numeric(df[col], downcast='integer')\n        else:\n            df[col] = pd.to_numeric(df[col], downcast='float')\n    end_mem = df.memory_usage().sum() / 1024**2\n    if verbose:\n        print('{:.1f}% 압축됨'.format(100 * (start_mem - end_mem) / start_mem))\n    \n    return df\n\nall_df = [sales_train, shops, items, item_categories, test]\nfor df in all_df:\n    df = downcast(df)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:23.024854Z","iopub.execute_input":"2024-11-27T10:02:23.025171Z","iopub.status.idle":"2024-11-27T10:02:23.312885Z","shell.execute_reply.started":"2024-11-27T10:02:23.025140Z","shell.execute_reply":"2024-11-27T10:02:23.311853Z"}},"outputs":[{"name":"stdout","text":"54.2% 압축됨\n38.6% 압축됨\n54.2% 압축됨\n39.9% 압축됨\n70.8% 압축됨\n","output_type":"stream"}],"execution_count":110},{"cell_type":"markdown","source":"## 🔍 9.4.2 피처 엔지니어링 II : 개별 데이터 피처 엔지니어링\n\n### 🔑 `sales_train` 이상치 제거 및 전처리\n\n- 이상치 제거: 판매가와 판매량이 음수인 경우 환불이거나 오류이므로 이상치로 간주\n- 판매가 50000 이상, 판매량 1000 이상인 경우 이상치로 간주","metadata":{}},{"cell_type":"code","source":"# 판매가가 0보다 큰 데이터 추출\nsales_train = sales_train[sales_train['판매가'] > 0]\n# 판매가가 50,000보다 작은 데이터 추출\nsales_train = sales_train[sales_train['판매가'] < 50000]\n\n# 판매량이 0보다 큰 데이터 추출\nsales_train = sales_train[sales_train['판매량'] > 0]\n# 판매량이 1,000보다 작은 데이터 추출\nsales_train = sales_train[sales_train['판매량'] < 1000]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:23.314927Z","iopub.execute_input":"2024-11-27T10:02:23.315285Z","iopub.status.idle":"2024-11-27T10:02:23.808244Z","shell.execute_reply.started":"2024-11-27T10:02:23.315251Z","shell.execute_reply":"2024-11-27T10:02:23.807204Z"}},"outputs":[],"execution_count":111},{"cell_type":"markdown","source":"- 같은 상점이나 상점명이 조금 다른 상점들을 상점으로 취급\n- 상점 id만 사용할 것이므로 id만 수정","metadata":{}},{"cell_type":"code","source":"# 상점명 확인 코드\nprint(shops['상점명'][0], '||', shops['상점명'][57])\nprint(shops['상점명'][1], '||', shops['상점명'][58])\nprint(shops['상점명'][10], '||', shops['상점명'][11])\nprint(shops['상점명'][39], '||', shops['상점명'][40])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:23.809407Z","iopub.execute_input":"2024-11-27T10:02:23.809736Z","iopub.status.idle":"2024-11-27T10:02:23.818535Z","shell.execute_reply.started":"2024-11-27T10:02:23.809703Z","shell.execute_reply":"2024-11-27T10:02:23.817284Z"}},"outputs":[{"name":"stdout","text":"!Якутск Орджоникидзе, 56 фран || Якутск Орджоникидзе, 56\n!Якутск ТЦ \"Центральный\" фран || Якутск ТЦ \"Центральный\"\nЖуковский ул. Чкалова 39м? || Жуковский ул. Чкалова 39м²\nРостовНаДону ТРК \"Мегацентр Горизонт\" || РостовНаДону ТРК \"Мегацентр Горизонт\" Островной\n","output_type":"stream"}],"execution_count":112},{"cell_type":"code","source":"# sales_train 데이터에서 상점ID 수정\nsales_train.loc[sales_train['상점ID'] == 0, '상점ID'] = 57\nsales_train.loc[sales_train['상점ID'] == 1, '상점ID'] = 58\nsales_train.loc[sales_train['상점ID'] == 10, '상점ID'] = 11\nsales_train.loc[sales_train['상점ID'] == 39, '상점ID'] = 40\n\n# test 데이터에서 상점ID 수정\ntest.loc[test['상점ID'] == 0, '상점ID'] = 57\ntest.loc[test['상점ID'] == 1, '상점ID'] = 58\ntest.loc[test['상점ID'] == 10, '상점ID'] = 11\ntest.loc[test['상점ID'] == 39, '상점ID'] = 40","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:23.820293Z","iopub.execute_input":"2024-11-27T10:02:23.820749Z","iopub.status.idle":"2024-11-27T10:02:23.853039Z","shell.execute_reply.started":"2024-11-27T10:02:23.820702Z","shell.execute_reply":"2024-11-27T10:02:23.851699Z"}},"outputs":[],"execution_count":113},{"cell_type":"markdown","source":"### 🔑 `shops` 파생 피처 생성 및 인코딩\n\n- 상점명 앞부분을 가져와 `도시` 피처 생성\n- 상점명의 오탈자 제거\n- 레이블 인코딩 진행\n- 상점명 피처 제거","metadata":{}},{"cell_type":"code","source":"shops['도시'] = shops['상점명'].apply(lambda x: x.split()[0])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:23.854960Z","iopub.execute_input":"2024-11-27T10:02:23.855327Z","iopub.status.idle":"2024-11-27T10:02:23.861575Z","shell.execute_reply.started":"2024-11-27T10:02:23.855295Z","shell.execute_reply":"2024-11-27T10:02:23.860208Z"}},"outputs":[],"execution_count":114},{"cell_type":"code","source":"# 도시 확인 \nshops['도시'].unique()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:23.862857Z","iopub.execute_input":"2024-11-27T10:02:23.863214Z","iopub.status.idle":"2024-11-27T10:02:23.879236Z","shell.execute_reply.started":"2024-11-27T10:02:23.863161Z","shell.execute_reply":"2024-11-27T10:02:23.878108Z"}},"outputs":[{"execution_count":115,"output_type":"execute_result","data":{"text/plain":"array(['!Якутск', 'Адыгея', 'Балашиха', 'Волжский', 'Вологда', 'Воронеж',\n       'Выездная', 'Жуковский', 'Интернет-магазин', 'Казань', 'Калуга',\n       'Коломна', 'Красноярск', 'Курск', 'Москва', 'Мытищи', 'Н.Новгород',\n       'Новосибирск', 'Омск', 'РостовНаДону', 'СПб', 'Самара', 'Сергиев',\n       'Сургут', 'Томск', 'Тюмень', 'Уфа', 'Химки', 'Цифровой', 'Чехов',\n       'Якутск', 'Ярославль'], dtype=object)"},"metadata":{}}],"execution_count":115},{"cell_type":"code","source":"# \"!\" 가 존재하는 부분 오류 제거\nshops.loc[shops['도시'] =='!Якутск', '도시'] = 'Якутск'","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:23.880797Z","iopub.execute_input":"2024-11-27T10:02:23.881260Z","iopub.status.idle":"2024-11-27T10:02:23.891009Z","shell.execute_reply.started":"2024-11-27T10:02:23.881213Z","shell.execute_reply":"2024-11-27T10:02:23.889955Z"}},"outputs":[],"execution_count":116},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\n\n# 레이블 인코더 생성\nlabel_encoder = LabelEncoder()\n# 도시 피처 레이블 인코딩\nshops['도시'] = label_encoder.fit_transform(shops['도시'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:23.894490Z","iopub.execute_input":"2024-11-27T10:02:23.894865Z","iopub.status.idle":"2024-11-27T10:02:23.905725Z","shell.execute_reply.started":"2024-11-27T10:02:23.894833Z","shell.execute_reply":"2024-11-27T10:02:23.904731Z"}},"outputs":[],"execution_count":117},{"cell_type":"code","source":"# 상점명 피처 drop\nshops = shops.drop('상점명', axis=1)\nshops.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:23.906960Z","iopub.execute_input":"2024-11-27T10:02:23.907285Z","iopub.status.idle":"2024-11-27T10:02:23.924348Z","shell.execute_reply.started":"2024-11-27T10:02:23.907253Z","shell.execute_reply":"2024-11-27T10:02:23.923086Z"}},"outputs":[{"execution_count":118,"output_type":"execute_result","data":{"text/plain":"   상점ID  도시\n0     0  29\n1     1  29\n2     2   0\n3     3   1\n4     4   2","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>상점ID</th>\n      <th>도시</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>29</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>29</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":118},{"cell_type":"markdown","source":"### 🔑 `itmes` 파생 피처 생성\n\n- items 데이터의 상품명 drop\n- sales_train 데이터 내 판매 데이터를 이용하여 첫 판매월 피처 구하여 생성\n  - 첫 판매월이 결측인 값들이 존재: 판매된 적이 없는 상품\n  - 처음 팔린 달을 2015년 11월(test)이라고 가정: 11월 판매량만 예측하면 되기 때문(테스트 데이터에 없는 상품이면 고려대상이 아니게 되기 때문)","metadata":{}},{"cell_type":"code","source":"# 상품명 피처 drop\nitems = items.drop(['상품명'], axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:23.925727Z","iopub.execute_input":"2024-11-27T10:02:23.926048Z","iopub.status.idle":"2024-11-27T10:02:23.934881Z","shell.execute_reply.started":"2024-11-27T10:02:23.926016Z","shell.execute_reply":"2024-11-27T10:02:23.933835Z"}},"outputs":[],"execution_count":119},{"cell_type":"code","source":"# 상품이 맨 처음 팔린 날을 피처로 추가\nitems['첫 판매월'] = sales_train.groupby('상품ID').agg({'월ID': 'min'})['월ID']\nitems.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:23.936259Z","iopub.execute_input":"2024-11-27T10:02:23.936694Z","iopub.status.idle":"2024-11-27T10:02:24.001484Z","shell.execute_reply.started":"2024-11-27T10:02:23.936656Z","shell.execute_reply":"2024-11-27T10:02:24.000354Z"}},"outputs":[{"execution_count":120,"output_type":"execute_result","data":{"text/plain":"   상품ID  상품분류ID  첫 판매월\n0     0      40   20.0\n1     1      76   15.0\n2     2      40   19.0\n3     3      40   18.0\n4     4      40   20.0","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>상품ID</th>\n      <th>상품분류ID</th>\n      <th>첫 판매월</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>40</td>\n      <td>20.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>76</td>\n      <td>15.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>40</td>\n      <td>19.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>40</td>\n      <td>18.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>40</td>\n      <td>20.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":120},{"cell_type":"code","source":"items[items['첫 판매월'].isna()]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:24.002748Z","iopub.execute_input":"2024-11-27T10:02:24.003064Z","iopub.status.idle":"2024-11-27T10:02:24.016545Z","shell.execute_reply.started":"2024-11-27T10:02:24.003032Z","shell.execute_reply":"2024-11-27T10:02:24.015441Z"}},"outputs":[{"execution_count":121,"output_type":"execute_result","data":{"text/plain":"        상품ID  상품분류ID  첫 판매월\n83        83      40    NaN\n140      140      45    NaN\n168      168      44    NaN\n173      173      45    NaN\n204      204      44    NaN\n...      ...     ...    ...\n21974  21974      61    NaN\n21975  21975      61    NaN\n22022  22022      40    NaN\n22035  22035      40    NaN\n22137  22137      40    NaN\n\n[368 rows x 3 columns]","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>상품ID</th>\n      <th>상품분류ID</th>\n      <th>첫 판매월</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>83</th>\n      <td>83</td>\n      <td>40</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>140</th>\n      <td>140</td>\n      <td>45</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>168</th>\n      <td>168</td>\n      <td>44</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>173</th>\n      <td>173</td>\n      <td>45</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>204</th>\n      <td>204</td>\n      <td>44</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>21974</th>\n      <td>21974</td>\n      <td>61</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>21975</th>\n      <td>21975</td>\n      <td>61</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>22022</th>\n      <td>22022</td>\n      <td>40</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>22035</th>\n      <td>22035</td>\n      <td>40</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>22137</th>\n      <td>22137</td>\n      <td>40</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>368 rows × 3 columns</p>\n</div>"},"metadata":{}}],"execution_count":121},{"cell_type":"code","source":"# 첫 판매월 피처의 결측값을 34로 대체\nitems['첫 판매월'] = items['첫 판매월'].fillna(34)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:24.017767Z","iopub.execute_input":"2024-11-27T10:02:24.018089Z","iopub.status.idle":"2024-11-27T10:02:24.033464Z","shell.execute_reply.started":"2024-11-27T10:02:24.018059Z","shell.execute_reply":"2024-11-27T10:02:24.032327Z"}},"outputs":[],"execution_count":122},{"cell_type":"markdown","source":"### 🔑 `item_categories` 파생 피처 생성 및 인코딩\n\n- 상품 분류 name에서 대분류라는 파생 피처를 만들고 인코딩 (첫 단어 추출)\n- 상품 분류 고윳값 개수 자체가 적은 경우 etc로 묶음\n- 레이블 인코딩을 진행한 뒤 상품분류명 피처 drop","metadata":{}},{"cell_type":"code","source":"# 상품분류명의 첫 단어를 대분류로 추출\nitem_categories['대분류'] = item_categories['상품분류명'].apply(lambda x: x.split()[0])  ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:24.034814Z","iopub.execute_input":"2024-11-27T10:02:24.035154Z","iopub.status.idle":"2024-11-27T10:02:24.046117Z","shell.execute_reply.started":"2024-11-27T10:02:24.035122Z","shell.execute_reply":"2024-11-27T10:02:24.045184Z"}},"outputs":[],"execution_count":123},{"cell_type":"code","source":"# 각 첫 단어 고윳값 개수 출력\nitem_categories['대분류'].value_counts()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:24.047308Z","iopub.execute_input":"2024-11-27T10:02:24.047610Z","iopub.status.idle":"2024-11-27T10:02:24.064637Z","shell.execute_reply.started":"2024-11-27T10:02:24.047580Z","shell.execute_reply":"2024-11-27T10:02:24.063629Z"}},"outputs":[{"execution_count":124,"output_type":"execute_result","data":{"text/plain":"대분류\nИгры          14\nКниги         13\nПодарки       12\nИгровые        8\nАксессуары     7\nМузыка         6\nПрограммы      6\nКарты          5\nКино           5\nСлужебные      2\nЧистые         2\nPC             1\nБилеты         1\nДоставка       1\nЭлементы       1\nName: count, dtype: int64"},"metadata":{}}],"execution_count":124},{"cell_type":"code","source":"def make_etc(x):\n    if len(item_categories[item_categories['대분류']==x]) >= 5:\n        return x\n    else:\n        return 'etc'\n\n# 대분류의 고윳값 개수가 5개 미만이면 'etc'로 바꾸기\nitem_categories['대분류'] = item_categories['대분류'].apply(make_etc)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:24.065945Z","iopub.execute_input":"2024-11-27T10:02:24.066376Z","iopub.status.idle":"2024-11-27T10:02:24.104801Z","shell.execute_reply.started":"2024-11-27T10:02:24.066331Z","shell.execute_reply":"2024-11-27T10:02:24.103747Z"}},"outputs":[],"execution_count":125},{"cell_type":"code","source":"# 확인\nitem_categories.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:24.105897Z","iopub.execute_input":"2024-11-27T10:02:24.106333Z","iopub.status.idle":"2024-11-27T10:02:24.115831Z","shell.execute_reply.started":"2024-11-27T10:02:24.106281Z","shell.execute_reply":"2024-11-27T10:02:24.114862Z"}},"outputs":[{"execution_count":126,"output_type":"execute_result","data":{"text/plain":"                     상품분류명  상품분류ID         대분류\n0  PC - Гарнитуры/Наушники       0         etc\n1         Аксессуары - PS2       1  Аксессуары\n2         Аксессуары - PS3       2  Аксессуары\n3         Аксессуары - PS4       3  Аксессуары\n4         Аксессуары - PSP       4  Аксессуары","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>상품분류명</th>\n      <th>상품분류ID</th>\n      <th>대분류</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>PC - Гарнитуры/Наушники</td>\n      <td>0</td>\n      <td>etc</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Аксессуары - PS2</td>\n      <td>1</td>\n      <td>Аксессуары</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Аксессуары - PS3</td>\n      <td>2</td>\n      <td>Аксессуары</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Аксессуары - PS4</td>\n      <td>3</td>\n      <td>Аксессуары</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Аксессуары - PSP</td>\n      <td>4</td>\n      <td>Аксессуары</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":126},{"cell_type":"code","source":"# label encoder\nlabel_encoder = LabelEncoder()\n\n# 대분류 피처 label encoding\nitem_categories['대분류'] = label_encoder.fit_transform(item_categories['대분류'])\n\n# 상품분류명 피처 drop\nitem_categories = item_categories.drop('상품분류명', axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:24.117067Z","iopub.execute_input":"2024-11-27T10:02:24.117395Z","iopub.status.idle":"2024-11-27T10:02:24.128691Z","shell.execute_reply.started":"2024-11-27T10:02:24.117363Z","shell.execute_reply":"2024-11-27T10:02:24.127583Z"}},"outputs":[],"execution_count":127},{"cell_type":"markdown","source":"## 🔍 9.4.3 피처 엔지니어링 III : 데이터 조합 및 파생 피처 생성\n\n### 🔑 데이터 조합\n\n- 베이스라인과 동일","metadata":{}},{"cell_type":"code","source":"from itertools import product\n\ntrain = []\n# 월ID, 상점ID, 상품ID 조합 생성\nfor i in sales_train['월ID'].unique():\n    all_shop = sales_train.loc[sales_train['월ID']==i, '상점ID'].unique()\n    all_item = sales_train.loc[sales_train['월ID']==i, '상품ID'].unique()\n    train.append(np.array(list(product([i], all_shop, all_item))))\n\nidx_features = ['월ID', '상점ID', '상품ID'] # 기준 피처\ntrain = pd.DataFrame(np.vstack(train), columns=idx_features)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:24.129967Z","iopub.execute_input":"2024-11-27T10:02:24.130325Z","iopub.status.idle":"2024-11-27T10:02:30.644003Z","shell.execute_reply.started":"2024-11-27T10:02:24.130295Z","shell.execute_reply":"2024-11-27T10:02:30.642917Z"}},"outputs":[],"execution_count":128},{"cell_type":"markdown","source":"### 🔑 파생 피처 생성\n\n- 월, 상점, 상품 정보로 월간 판매량에 추가로 **평균 판매가** 피처 생성\n  - 결측의 경우 판매량과 판매가가 0인 것에 대해 0으로 대체 예정\n- 판매 여부에 관련된 피처 생성","metadata":{}},{"cell_type":"code","source":"group = sales_train.groupby(idx_features).agg({'판매량': 'sum',\n                                               '판매가': 'mean'})\ngroup = group.reset_index()\ngroup = group.rename(columns={'판매량': '월간 판매량', '판매가': '평균 판매가'})\n\ntrain = train.merge(group, on=idx_features, how='left')\ntrain.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:30.645424Z","iopub.execute_input":"2024-11-27T10:02:30.645760Z","iopub.status.idle":"2024-11-27T10:02:35.293084Z","shell.execute_reply.started":"2024-11-27T10:02:30.645729Z","shell.execute_reply":"2024-11-27T10:02:35.292072Z"}},"outputs":[{"execution_count":129,"output_type":"execute_result","data":{"text/plain":"   월ID  상점ID   상품ID  월간 판매량  평균 판매가\n0    0    59  22154     1.0   999.0\n1    0    59   2552     NaN     NaN\n2    0    59   2554     NaN     NaN\n3    0    59   2555     NaN     NaN\n4    0    59   2564     NaN     NaN","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>월ID</th>\n      <th>상점ID</th>\n      <th>상품ID</th>\n      <th>월간 판매량</th>\n      <th>평균 판매가</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>59</td>\n      <td>22154</td>\n      <td>1.0</td>\n      <td>999.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2552</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2554</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2555</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2564</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":129},{"cell_type":"code","source":"import gc\n# group 변수 가비지 컬렉션\ndel group\ngc.collect();","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:35.294668Z","iopub.execute_input":"2024-11-27T10:02:35.294999Z","iopub.status.idle":"2024-11-27T10:02:35.441792Z","shell.execute_reply.started":"2024-11-27T10:02:35.294969Z","shell.execute_reply":"2024-11-27T10:02:35.440655Z"}},"outputs":[],"execution_count":130},{"cell_type":"markdown","source":"- 기준 피처별 상품 판매 건수: 당일에 한 번이라도 판매를 했다면 1, 판매가 없없다면 0","metadata":{}},{"cell_type":"code","source":"# 상품 판매건수 피처 추가\ngroup = sales_train.groupby(idx_features).agg({'판매량': 'count'})\ngroup = group.reset_index()\ngroup = group.rename(columns={'판매량': '판매건수'})\n\ntrain = train.merge(group, on=idx_features, how='left')\n\n# 가비지 컬렉션\ndel group, sales_train\ngc.collect()\n\ntrain.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:35.443068Z","iopub.execute_input":"2024-11-27T10:02:35.443420Z","iopub.status.idle":"2024-11-27T10:02:40.021161Z","shell.execute_reply.started":"2024-11-27T10:02:35.443387Z","shell.execute_reply":"2024-11-27T10:02:40.020009Z"}},"outputs":[{"execution_count":131,"output_type":"execute_result","data":{"text/plain":"   월ID  상점ID   상품ID  월간 판매량  평균 판매가  판매건수\n0    0    59  22154     1.0   999.0   1.0\n1    0    59   2552     NaN     NaN   NaN\n2    0    59   2554     NaN     NaN   NaN\n3    0    59   2555     NaN     NaN   NaN\n4    0    59   2564     NaN     NaN   NaN","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>월ID</th>\n      <th>상점ID</th>\n      <th>상품ID</th>\n      <th>월간 판매량</th>\n      <th>평균 판매가</th>\n      <th>판매건수</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>59</td>\n      <td>22154</td>\n      <td>1.0</td>\n      <td>999.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2552</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2554</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2555</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2564</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":131},{"cell_type":"markdown","source":"## 🔍 9.4.4 피처 엔지니어링 IV : 데이터 합치기\n\n### 🔑 테스트 데이터 이어붙이기\n\n- 테스트 데이터 이어붙인 되 `shop`, `items`, `item_categories` 데이터를 `sales_train`에 병합","metadata":{}},{"cell_type":"code","source":"# 테스트 데이터 월ID를 34로 설정\ntest['월ID'] = 34\n\n# train과 test 이어붙이기\nall_data = pd.concat([train, test.drop('ID', axis=1)],\n                     ignore_index=True,\n                     keys=idx_features)\n# 결측값을 0으로 대체\nall_data = all_data.fillna(0)\n\nall_data.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:40.022557Z","iopub.execute_input":"2024-11-27T10:02:40.023146Z","iopub.status.idle":"2024-11-27T10:02:40.397120Z","shell.execute_reply.started":"2024-11-27T10:02:40.023092Z","shell.execute_reply":"2024-11-27T10:02:40.396252Z"}},"outputs":[{"execution_count":132,"output_type":"execute_result","data":{"text/plain":"   월ID  상점ID   상품ID  월간 판매량  평균 판매가  판매건수\n0    0    59  22154     1.0   999.0   1.0\n1    0    59   2552     0.0     0.0   0.0\n2    0    59   2554     0.0     0.0   0.0\n3    0    59   2555     0.0     0.0   0.0\n4    0    59   2564     0.0     0.0   0.0","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>월ID</th>\n      <th>상점ID</th>\n      <th>상품ID</th>\n      <th>월간 판매량</th>\n      <th>평균 판매가</th>\n      <th>판매건수</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>59</td>\n      <td>22154</td>\n      <td>1.0</td>\n      <td>999.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2552</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2554</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2555</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>59</td>\n      <td>2564</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":132},{"cell_type":"markdown","source":"### 🔑 모든 데이터 병합","metadata":{}},{"cell_type":"code","source":"# 나머지 데이터 병합\nall_data = all_data.merge(shops, on='상점ID', how='left')\nall_data = all_data.merge(items, on='상품ID', how='left')\nall_data = all_data.merge(item_categories, on='상품분류ID', how='left')\n\n# 데이터 다운캐스팅\nall_data = downcast(all_data)\n\n# 가비지 컬렉션\ndel shops, items, item_categories\ngc.collect();","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:40.399033Z","iopub.execute_input":"2024-11-27T10:02:40.399333Z","iopub.status.idle":"2024-11-27T10:02:45.839528Z","shell.execute_reply.started":"2024-11-27T10:02:40.399304Z","shell.execute_reply":"2024-11-27T10:02:45.838364Z"}},"outputs":[{"name":"stdout","text":"68.9% 압축됨\n","output_type":"stream"}],"execution_count":133},{"cell_type":"markdown","source":"## 🔍 9.4.5 피처 엔지니어링 V : 시차 피처 생성\n\n\n- 시차 피처(time lag feature)  \n  - 과거 시점에 대한 관한 피처\n  - 시계열 문제에서 성능 향상에 주로 쓰임\n\n- 시차 피처 생성\n  - 기준으로 삼을 치러 생성: 이번 문제의 경우 `월간 평균 판매량`\n  - 이를 위해 '기준 피처별 월간 평균 판매량' 피처 생성 필요\n\n\n### 🔑 기준 피처별 월간 평균 판매량 파생 피처 생성  \n  \n- 파라미터\n  - `df` : 작업할 전체 데이터 프레임\n  - `mean_features` : 새로 만든 월간 평균 판매량 파생 피처명을 저장할 리스트\n  - `idx_features` : 기준 피처\n","metadata":{}},{"cell_type":"code","source":"def add_mean_features(df, mean_features, idx_features):\n    # 기준 피처의 첫번째 요소가 '월id'가 맞는지 확인 \n    assert (idx_features[0] == '월ID') and \\\n           len(idx_features) in [2, 3]\n    \n    # 파생 피처명 설정 \n    if len(idx_features) == 2:\n        feature_name = idx_features[1] + '별 평균 판매량'\n    else:\n        feature_name = idx_features[1] + ' ' + idx_features[2] + '별 평균 판매량'\n    \n    # 기준 피처를 토대로 그룹화해 월간 평균 판매량 구하기 \n    group = df.groupby(idx_features).agg({'월간 판매량': 'mean'})\n    group = group.reset_index()\n    group = group.rename(columns={'월간 판매량': feature_name})\n    \n    # df와 group 병합 \n    df = df.merge(group, on=idx_features, how='left')\n    # 데이터 다운캐스팅 \n    df = downcast(df, verbose=False)\n    # 새로 만든 feature_name 피처명을 mean_features 리스트에 추가 \n    mean_features.append(feature_name)\n    \n    # 가비지 컬렉션\n    del group\n    gc.collect()\n    \n    return df, mean_features","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:45.841366Z","iopub.execute_input":"2024-11-27T10:02:45.841706Z","iopub.status.idle":"2024-11-27T10:02:45.849241Z","shell.execute_reply.started":"2024-11-27T10:02:45.841672Z","shell.execute_reply":"2024-11-27T10:02:45.848107Z"}},"outputs":[],"execution_count":134},{"cell_type":"markdown","source":"- 함수를 활용하여 `월-상품`인 것과 `월-상품-도시`로 그룹화한 월간 평균 판매량을 만듦\n- `item_mean_features`: 기준 피처에 상품 id를 포함하는 파생피처명","metadata":{}},{"cell_type":"code","source":"# 그룹화 기준 피처 중 '상품ID'가 포함된 파생 피처명을 담을 리스트\nitem_mean_features = []\n\n# ['월ID', '상품ID']로 그룹화한 월간 평균 판매량 파생 피처 생성\nall_data, item_mean_features = add_mean_features(df=all_data,\n                                                 mean_features=item_mean_features,\n                                                 idx_features=['월ID', '상품ID'])\n\n# ['월ID', '상품ID', '도시']로 그룹화한 월간 평균 판매량 파생 피처 생성\nall_data, item_mean_features = add_mean_features(df=all_data,\n                                                 mean_features=item_mean_features,\n                                                 idx_features=['월ID', '상품ID', '도시'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:45.854631Z","iopub.execute_input":"2024-11-27T10:02:45.854998Z","iopub.status.idle":"2024-11-27T10:02:58.561828Z","shell.execute_reply.started":"2024-11-27T10:02:45.854963Z","shell.execute_reply":"2024-11-27T10:02:58.560328Z"}},"outputs":[],"execution_count":135},{"cell_type":"code","source":"# 확인\nitem_mean_features","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:58.563270Z","iopub.execute_input":"2024-11-27T10:02:58.563615Z","iopub.status.idle":"2024-11-27T10:02:58.570407Z","shell.execute_reply.started":"2024-11-27T10:02:58.563582Z","shell.execute_reply":"2024-11-27T10:02:58.569288Z"}},"outputs":[{"execution_count":136,"output_type":"execute_result","data":{"text/plain":"['상품ID별 평균 판매량', '상품ID 도시별 평균 판매량']"},"metadata":{}}],"execution_count":136},{"cell_type":"markdown","source":"- 월별 상점별 상품분류의 월간 판매량 피처 생성","metadata":{}},{"cell_type":"code","source":"# 그룹화 기준 피처 중 '상점ID'가 포함된 파생 피처명을 담을 리스트\nshop_mean_features = []\n\n# ['월ID', '상점ID', '상품분류ID']로 그룹화한 월간 평균 판매량 파생 피처 생성\nall_data, shop_mean_features = add_mean_features(df=all_data, \n                                                 mean_features=shop_mean_features,\n                                                 idx_features=['월ID', '상점ID', '상품분류ID'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:02:58.571785Z","iopub.execute_input":"2024-11-27T10:02:58.572101Z","iopub.status.idle":"2024-11-27T10:03:01.780746Z","shell.execute_reply.started":"2024-11-27T10:02:58.572071Z","shell.execute_reply":"2024-11-27T10:03:01.779729Z"}},"outputs":[],"execution_count":137},{"cell_type":"code","source":"# 확인\nshop_mean_features","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:03:01.781922Z","iopub.execute_input":"2024-11-27T10:03:01.782252Z","iopub.status.idle":"2024-11-27T10:03:01.788407Z","shell.execute_reply.started":"2024-11-27T10:03:01.782210Z","shell.execute_reply":"2024-11-27T10:03:01.787361Z"}},"outputs":[{"execution_count":138,"output_type":"execute_result","data":{"text/plain":"['상점ID 상품분류ID별 평균 판매량']"},"metadata":{}}],"execution_count":138},{"cell_type":"markdown","source":"### 🔑 시차 피처 생성 원리 및 함수 구현\n\n- '징검다리 피처' 라고 표현: 원하는 시점까지 생성할 수 있으나, 시점이 너무 과거면 예측력이 떨어질 수 있음\n- 따라서 세 달 전까지만 활용  \n\n\n- 파라미터\n  - `df` : 원본 데이터\n  - `lag_features_to_clip` : 값의 범위를 0~20 사이로 제한할 피처를 담을 리스트로, 판매량 관련 피처가 해당\n  - `idx_features` : 기준 피처\n  - `lag_feature` : 시차를 만들 피처\n  - **`nlags` : 시차**\n    - 1 = 한 달 전\n    - 2 = 한 달 전, 두 달 전 시차 피처 생성\n    - 3 = 한 달, 두 달, 세 달 전 피처 생성\n  - `clip` : 새로 만든 시차 피처를 `lag_features_to_clip` 리스트(0~20으로 값을 제한할 피처)에 저장할지의 여부로 boolean","metadata":{}},{"cell_type":"code","source":"def add_lag_features(df, lag_features_to_clip, idx_features, \n                     lag_feature, \n                     nlags=3, \n                     clip=False):\n    # 시차 피처 생성에 필요한 DataFrame 부분만 복사 \n    df_temp = df[idx_features + [lag_feature]].copy() \n\n    # 시차 피처 생성 \n    for i in range(1, nlags+1):\n        # 시차 피처명 \n        lag_feature_name = lag_feature +'_시차' + str(i)\n        # df_temp 열 이름 설정 \n        df_temp.columns = idx_features + [lag_feature_name]\n        # df_temp의 date_block_num 피처에 1 더하기\n        df_temp['월ID'] += 1\n        # idx_feature를 기준으로 df와 df_temp 병합하기 \n        df = df.merge(df_temp.drop_duplicates(), \n                      on=idx_features, \n                      how='left')\n        # 결측값 0으로 대체 \n        df[lag_feature_name] = df[lag_feature_name].fillna(0)\n        # 0 ~ 20 사이로 제한할 시차 피처명을 lag_features_to_clip에 추가 \n        if clip: \n            lag_features_to_clip.append(lag_feature_name)\n    \n    # 데이터 다운캐스팅\n    df = downcast(df, False)\n    # 가비지 컬렉션\n    del df_temp\n    gc.collect()\n    \n    return df, lag_features_to_clip","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:03:01.789854Z","iopub.execute_input":"2024-11-27T10:03:01.790646Z","iopub.status.idle":"2024-11-27T10:03:01.800640Z","shell.execute_reply.started":"2024-11-27T10:03:01.790598Z","shell.execute_reply":"2024-11-27T10:03:01.799484Z"}},"outputs":[],"execution_count":139},{"cell_type":"markdown","source":"### 🔑 시차 피처 생성 I : 월간 판매량\n\n- 월, 상점, 상품을 기준으로 세 달치의 시차 피처 생성\n- `clip = True`로 `lag_features_to_clip`에 저장","metadata":{}},{"cell_type":"code","source":"lag_features_to_clip = [] # 0 ~ 20 사이로 제한할 시차 피처명을 담을 리스트\nidx_features = ['월ID', '상점ID', '상품ID'] # 기준 피처\n\n# idx_features를 기준으로 월간 판매량의 세 달치 시차 피처 생성\nall_data, lag_features_to_clip = add_lag_features(df=all_data, \n                                                  lag_features_to_clip=lag_features_to_clip,\n                                                  idx_features=idx_features,\n                                                  lag_feature='월간 판매량', \n                                                  nlags=3,\n                                                  clip=True) # 값을 0 ~ 20 사이로 제한","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:03:01.802130Z","iopub.execute_input":"2024-11-27T10:03:01.802587Z","iopub.status.idle":"2024-11-27T10:03:32.676253Z","shell.execute_reply.started":"2024-11-27T10:03:01.802542Z","shell.execute_reply":"2024-11-27T10:03:32.675164Z"}},"outputs":[],"execution_count":140},{"cell_type":"code","source":"# 확인\nall_data.head().T","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:03:32.677529Z","iopub.execute_input":"2024-11-27T10:03:32.677860Z","iopub.status.idle":"2024-11-27T10:03:32.694850Z","shell.execute_reply.started":"2024-11-27T10:03:32.677829Z","shell.execute_reply":"2024-11-27T10:03:32.693753Z"}},"outputs":[{"execution_count":141,"output_type":"execute_result","data":{"text/plain":"                                0            1            2            3  \\\n월ID                      0.000000     0.000000     0.000000     0.000000   \n상점ID                    59.000000    59.000000    59.000000    59.000000   \n상품ID                 22154.000000  2552.000000  2554.000000  2555.000000   \n월간 판매량                   1.000000     0.000000     0.000000     0.000000   \n평균 판매가                 999.000000     0.000000     0.000000     0.000000   \n판매건수                     1.000000     0.000000     0.000000     0.000000   \n도시                      30.000000    30.000000    30.000000    30.000000   \n상품분류ID                  37.000000    58.000000    58.000000    56.000000   \n첫 판매월                    0.000000     0.000000     0.000000     0.000000   \n대분류                      5.000000     7.000000     7.000000     7.000000   \n상품ID별 평균 판매량             0.400000     0.022222     0.022222     0.044444   \n상품ID 도시별 평균 판매량          1.000000     0.000000     0.000000     0.000000   \n상점ID 상품분류ID별 평균 판매량      0.088496     0.000000     0.000000     0.008333   \n월간 판매량_시차1               0.000000     0.000000     0.000000     0.000000   \n월간 판매량_시차2               0.000000     0.000000     0.000000     0.000000   \n월간 판매량_시차3               0.000000     0.000000     0.000000     0.000000   \n\n                               4  \n월ID                     0.000000  \n상점ID                   59.000000  \n상품ID                 2564.000000  \n월간 판매량                  0.000000  \n평균 판매가                  0.000000  \n판매건수                    0.000000  \n도시                     30.000000  \n상품분류ID                 59.000000  \n첫 판매월                   0.000000  \n대분류                     7.000000  \n상품ID별 평균 판매량            0.111111  \n상품ID 도시별 평균 판매량         0.000000  \n상점ID 상품분류ID별 평균 판매량     0.011976  \n월간 판매량_시차1              0.000000  \n월간 판매량_시차2              0.000000  \n월간 판매량_시차3              0.000000  ","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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>월ID</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>상점ID</th>\n      <td>59.000000</td>\n      <td>59.000000</td>\n      <td>59.000000</td>\n      <td>59.000000</td>\n      <td>59.000000</td>\n    </tr>\n    <tr>\n      <th>상품ID</th>\n      <td>22154.000000</td>\n      <td>2552.000000</td>\n      <td>2554.000000</td>\n      <td>2555.000000</td>\n      <td>2564.000000</td>\n    </tr>\n    <tr>\n      <th>월간 판매량</th>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>평균 판매가</th>\n      <td>999.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>판매건수</th>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>도시</th>\n      <td>30.000000</td>\n      <td>30.000000</td>\n      <td>30.000000</td>\n      <td>30.000000</td>\n      <td>30.000000</td>\n    </tr>\n    <tr>\n      <th>상품분류ID</th>\n      <td>37.000000</td>\n      <td>58.000000</td>\n      <td>58.000000</td>\n      <td>56.000000</td>\n      <td>59.000000</td>\n    </tr>\n    <tr>\n      <th>첫 판매월</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>대분류</th>\n      <td>5.000000</td>\n      <td>7.000000</td>\n      <td>7.000000</td>\n      <td>7.000000</td>\n      <td>7.000000</td>\n    </tr>\n    <tr>\n      <th>상품ID별 평균 판매량</th>\n      <td>0.400000</td>\n      <td>0.022222</td>\n      <td>0.022222</td>\n      <td>0.044444</td>\n      <td>0.111111</td>\n    </tr>\n    <tr>\n      <th>상품ID 도시별 평균 판매량</th>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>상점ID 상품분류ID별 평균 판매량</th>\n      <td>0.088496</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.008333</td>\n      <td>0.011976</td>\n    </tr>\n    <tr>\n      <th>월간 판매량_시차1</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>월간 판매량_시차2</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>월간 판매량_시차3</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":141},{"cell_type":"code","source":"# 저장 확인\nlag_features_to_clip","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:03:32.696110Z","iopub.execute_input":"2024-11-27T10:03:32.696551Z","iopub.status.idle":"2024-11-27T10:03:32.709745Z","shell.execute_reply.started":"2024-11-27T10:03:32.696518Z","shell.execute_reply":"2024-11-27T10:03:32.708778Z"}},"outputs":[{"execution_count":142,"output_type":"execute_result","data":{"text/plain":"['월간 판매량_시차1', '월간 판매량_시차2', '월간 판매량_시차3']"},"metadata":{}}],"execution_count":142},{"cell_type":"markdown","source":"### 🔑 시차 피처 생성 II : 판매건수, 평균 판매가\n\n- 판매건수와 평균 판매가의 시차 피처 생성 \n- 0~20 사이의 제한이 필요 없음 : `clip` 파라미터 생략","metadata":{}},{"cell_type":"code","source":"# idx_features를 기준으로 판매건수 피처의 세 달치 시차 피처 생성\nall_data, lag_features_to_clip = add_lag_features(df=all_data, \n                                                  lag_features_to_clip=lag_features_to_clip,\n                                                  idx_features=idx_features,\n                                                  lag_feature='판매건수', \n                                                  nlags=3)\n\n# idx_features를 기준으로 평균 판매가 피처의 세 달치 시차 피처 생성\nall_data, lag_features_to_clip = add_lag_features(df=all_data, \n                                                  lag_features_to_clip=lag_features_to_clip,\n                                                  idx_features=idx_features,\n                                                  lag_feature='평균 판매가', \n                                                  nlags=3)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:03:32.710883Z","iopub.execute_input":"2024-11-27T10:03:32.711260Z","iopub.status.idle":"2024-11-27T10:04:33.978664Z","shell.execute_reply.started":"2024-11-27T10:03:32.711215Z","shell.execute_reply":"2024-11-27T10:04:33.977579Z"}},"outputs":[],"execution_count":143},{"cell_type":"markdown","source":"### 🔑 시차 피처 생성 III : 평균 판매량\n\n- 이미 리스트에 저장된 '상품별 평균 판매량', '상품별 도시별 평균 판매량'에 대해 시차 피처 생성\n  - 시차 피처 생성 후 제거\n- 평균 판매량 피처 `item_mean_features` 를 활용하여 시차 피처 생성\n","metadata":{}},{"cell_type":"code","source":"# idx_features를 기준으로 item_mean_features 요소별 시차 피처 생성\nfor item_mean_feature in item_mean_features:\n    all_data, lag_features_to_clip = add_lag_features(df=all_data, \n                                                      lag_features_to_clip=lag_features_to_clip, \n                                                      idx_features=idx_features, \n                                                      lag_feature=item_mean_feature, \n                                                      nlags=3,\n                                                      clip=True)\n# item_mean_features 피처 제거\nall_data = all_data.drop(item_mean_features, axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:04:33.980010Z","iopub.execute_input":"2024-11-27T10:04:33.980366Z","iopub.status.idle":"2024-11-27T10:05:36.726006Z","shell.execute_reply.started":"2024-11-27T10:04:33.980333Z","shell.execute_reply":"2024-11-27T10:05:36.725094Z"}},"outputs":[],"execution_count":144},{"cell_type":"markdown","source":"- 월별 상점별 상품분류의 월간 판매량 피처 `shop_mean_feature` 기준으로 시차 피처 생성","metadata":{}},{"cell_type":"code","source":"# ['월ID', '상점ID', '상품분류ID']를 기준으로 shop_mean_features 요소별 시차 피처 생성\nfor shop_mean_feature in shop_mean_features:\n    all_data, lag_features_to_clip = add_lag_features(df=all_data,\n                                                      lag_features_to_clip=lag_features_to_clip, \n                                                      idx_features=['월ID', '상점ID', '상품분류ID'], \n                                                      lag_feature=shop_mean_feature, \n                                                      nlags=3,\n                                                      clip=True)\n# shop_mean_features 피처 제거\nall_data = all_data.drop(shop_mean_features, axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:05:36.727270Z","iopub.execute_input":"2024-11-27T10:05:36.727679Z","iopub.status.idle":"2024-11-27T10:05:48.155080Z","shell.execute_reply.started":"2024-11-27T10:05:36.727628Z","shell.execute_reply":"2024-11-27T10:05:48.154226Z"}},"outputs":[],"execution_count":145},{"cell_type":"markdown","source":"### 🔑 시차 피처 생성 마무리 : 결측값 처리\n\n- 월 id가 0, 1, 2인 데이터에 대해 결측이 생김: 3달 치의 시차 피처를 만들었기 때문\n- 이 결측치에 대해 월 id가 3 미만인 데이터 drop 결정","metadata":{}},{"cell_type":"code","source":"# 월ID 3미만인 데이터 제거\nall_data = all_data.drop(all_data[all_data['월ID'] < 3].index)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:05:48.156228Z","iopub.execute_input":"2024-11-27T10:05:48.156519Z","iopub.status.idle":"2024-11-27T10:05:50.070483Z","shell.execute_reply.started":"2024-11-27T10:05:48.156491Z","shell.execute_reply":"2024-11-27T10:05:50.069450Z"}},"outputs":[],"execution_count":146},{"cell_type":"markdown","source":"## 🔍 9.4.6 피처 엔지니어링 VI : 기타 피처 엔지니어링\n\n### 🔑 기타 피처 추가  \n- 월간 판매량 시차 피처들의 평균\n- 시차 변화량\n- 신상 여부\n- 월(month)\n- 첫 판매 후 경과 기간\n\n#### 추가로  \n- 필요 없는 피처 제거","metadata":{}},{"cell_type":"markdown","source":"#### 1. 월간 판매량 시차 피처들의 평균","metadata":{}},{"cell_type":"code","source":"all_data['월간 판매량 시차평균'] = all_data[['월간 판매량_시차1',\n                                          '월간 판매량_시차2', \n                                          '월간 판매량_시차3']].mean(axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:05:50.071714Z","iopub.execute_input":"2024-11-27T10:05:50.072020Z","iopub.status.idle":"2024-11-27T10:05:51.380433Z","shell.execute_reply.started":"2024-11-27T10:05:50.071990Z","shell.execute_reply":"2024-11-27T10:05:51.379390Z"}},"outputs":[],"execution_count":147},{"cell_type":"code","source":"# 결과에 대해 0 ~ 20 사이로 값 제한\nall_data[lag_features_to_clip + ['월간 판매량', '월간 판매량 시차평균']] = all_data[lag_features_to_clip + ['월간 판매량', '월간 판매량 시차평균']].clip(0, 20)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:05:51.382168Z","iopub.execute_input":"2024-11-27T10:05:51.382616Z","iopub.status.idle":"2024-11-27T10:05:53.855229Z","shell.execute_reply.started":"2024-11-27T10:05:51.382580Z","shell.execute_reply":"2024-11-27T10:05:53.854271Z"}},"outputs":[],"execution_count":148},{"cell_type":"markdown","source":"#### 2. 시차 변화량\n\n- 시차 데이터간 변화량을 구하여 무한대/소는 결측 처리 뒤 0으로 대체","metadata":{}},{"cell_type":"code","source":"all_data['시차변화량1'] = all_data['월간 판매량_시차1']/all_data['월간 판매량_시차2']\nall_data['시차변화량1'] = all_data['시차변화량1'].replace([np.inf, -np.inf], \n                                                        np.nan).fillna(0)\n\nall_data['시차변화량2'] = all_data['월간 판매량_시차2']/all_data['월간 판매량_시차3']\nall_data['시차변화량2'] = all_data['시차변화량2'].replace([np.inf, -np.inf], \n                                                        np.nan).fillna(0)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:05:53.856486Z","iopub.execute_input":"2024-11-27T10:05:53.856818Z","iopub.status.idle":"2024-11-27T10:05:54.390202Z","shell.execute_reply.started":"2024-11-27T10:05:53.856787Z","shell.execute_reply":"2024-11-27T10:05:54.389150Z"}},"outputs":[],"execution_count":149},{"cell_type":"markdown","source":"#### 3. 신상 여부\n\n- 첫 판매월이 현재 월과 같은지 여부 피처 생성","metadata":{}},{"cell_type":"code","source":"all_data['신상여부'] = all_data['첫 판매월'] == all_data['월ID']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:05:54.391146Z","iopub.execute_input":"2024-11-27T10:05:54.391476Z","iopub.status.idle":"2024-11-27T10:05:54.401047Z","shell.execute_reply.started":"2024-11-27T10:05:54.391446Z","shell.execute_reply":"2024-11-27T10:05:54.399990Z"}},"outputs":[],"execution_count":150},{"cell_type":"markdown","source":"#### 4. 첫 판매 후 경과 기간\n\n- 첫 판매 후 얼마나 지났는지 계산 피처 생성","metadata":{}},{"cell_type":"code","source":"all_data['첫 판매 후 기간'] = all_data['월ID'] - all_data['첫 판매월']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:05:54.402483Z","iopub.execute_input":"2024-11-27T10:05:54.403161Z","iopub.status.idle":"2024-11-27T10:05:54.415007Z","shell.execute_reply.started":"2024-11-27T10:05:54.403113Z","shell.execute_reply":"2024-11-27T10:05:54.413957Z"}},"outputs":[],"execution_count":151},{"cell_type":"markdown","source":"#### 5. 월(month)","metadata":{}},{"cell_type":"code","source":"all_data['월'] = all_data['월ID'] % 12","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:05:54.416302Z","iopub.execute_input":"2024-11-27T10:05:54.416717Z","iopub.status.idle":"2024-11-27T10:05:54.463700Z","shell.execute_reply.started":"2024-11-27T10:05:54.416687Z","shell.execute_reply":"2024-11-27T10:05:54.462620Z"}},"outputs":[],"execution_count":152},{"cell_type":"markdown","source":"## 🔍9.4.7 피처 엔지니어링 VII : 마무리","metadata":{}},{"cell_type":"code","source":"# data 살펴보기\nall_data.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:05:54.465217Z","iopub.execute_input":"2024-11-27T10:05:54.465675Z","iopub.status.idle":"2024-11-27T10:05:54.479409Z","shell.execute_reply.started":"2024-11-27T10:05:54.465626Z","shell.execute_reply":"2024-11-27T10:05:54.478430Z"}},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 9904582 entries, 1122386 to 11026967\nData columns (total 34 columns):\n #   Column                   Dtype  \n---  ------                   -----  \n 0   월ID                      int8   \n 1   상점ID                     int8   \n 2   상품ID                     int16  \n 3   월간 판매량                   int16  \n 4   평균 판매가                   float64\n 5   판매건수                     int8   \n 6   도시                       int8   \n 7   상품분류ID                   int8   \n 8   첫 판매월                    int8   \n 9   대분류                      int8   \n 10  월간 판매량_시차1               int16  \n 11  월간 판매량_시차2               int16  \n 12  월간 판매량_시차3               int16  \n 13  판매건수_시차1                 int8   \n 14  판매건수_시차2                 int8   \n 15  판매건수_시차3                 int8   \n 16  평균 판매가_시차1               float64\n 17  평균 판매가_시차2               float64\n 18  평균 판매가_시차3               float64\n 19  상품ID별 평균 판매량_시차1         float32\n 20  상품ID별 평균 판매량_시차2         float32\n 21  상품ID별 평균 판매량_시차3         float32\n 22  상품ID 도시별 평균 판매량_시차1      float32\n 23  상품ID 도시별 평균 판매량_시차2      float32\n 24  상품ID 도시별 평균 판매량_시차3      float32\n 25  상점ID 상품분류ID별 평균 판매량_시차1  float32\n 26  상점ID 상품분류ID별 평균 판매량_시차2  float32\n 27  상점ID 상품분류ID별 평균 판매량_시차3  float32\n 28  월간 판매량 시차평균              float64\n 29  시차변화량1                   float64\n 30  시차변화량2                   float64\n 31  신상여부                     bool   \n 32  첫 판매 후 기간                int8   \n 33  월                        int8   \ndtypes: bool(1), float32(9), float64(7), int16(5), int8(12)\nmemory usage: 1.1 GB\n","output_type":"stream"}],"execution_count":153},{"cell_type":"markdown","source":"*- 어디선가의 문제로 30개가 아닌 33개의 컬럼이 출력되어 맞지 않는 부분 일부 drop 하고 다운캐스팅을 진행합니다.*","metadata":{}},{"cell_type":"code","source":"all_data = all_data.drop(['평균 판매가', '판매건수', '첫 판매월'], axis=1)\ndowncast(all_data, False) # 출력 X\nall_data.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:05:54.480757Z","iopub.execute_input":"2024-11-27T10:05:54.481207Z","iopub.status.idle":"2024-11-27T10:05:57.320352Z","shell.execute_reply.started":"2024-11-27T10:05:54.481142Z","shell.execute_reply":"2024-11-27T10:05:57.319277Z"}},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 9904582 entries, 1122386 to 11026967\nData columns (total 31 columns):\n #   Column                   Dtype  \n---  ------                   -----  \n 0   월ID                      int8   \n 1   상점ID                     int8   \n 2   상품ID                     int16  \n 3   월간 판매량                   int8   \n 4   도시                       int8   \n 5   상품분류ID                   int8   \n 6   대분류                      int8   \n 7   월간 판매량_시차1               int8   \n 8   월간 판매량_시차2               int8   \n 9   월간 판매량_시차3               int8   \n 10  판매건수_시차1                 int8   \n 11  판매건수_시차2                 int8   \n 12  판매건수_시차3                 int8   \n 13  평균 판매가_시차1               float64\n 14  평균 판매가_시차2               float64\n 15  평균 판매가_시차3               float64\n 16  상품ID별 평균 판매량_시차1         float32\n 17  상품ID별 평균 판매량_시차2         float32\n 18  상품ID별 평균 판매량_시차3         float32\n 19  상품ID 도시별 평균 판매량_시차1      float32\n 20  상품ID 도시별 평균 판매량_시차2      float32\n 21  상품ID 도시별 평균 판매량_시차3      float32\n 22  상점ID 상품분류ID별 평균 판매량_시차1  float32\n 23  상점ID 상품분류ID별 평균 판매량_시차2  float32\n 24  상점ID 상품분류ID별 평균 판매량_시차3  float32\n 25  월간 판매량 시차평균              float32\n 26  시차변화량1                   float32\n 27  시차변화량2                   float32\n 28  신상여부                     int8   \n 29  첫 판매 후 기간                int8   \n 30  월                        int8   \ndtypes: float32(12), float64(3), int16(1), int8(15)\nmemory usage: 840.7 MB\n","output_type":"stream"}],"execution_count":154},{"cell_type":"code","source":"# 훈련 데이터 (피처)\nX_train = all_data[all_data['월ID'] < 33]\nX_train = X_train.drop(['월간 판매량'], axis=1)\n# 검증 데이터 (피처)\nX_valid = all_data[all_data['월ID'] == 33]\nX_valid = X_valid.drop(['월간 판매량'], axis=1)\n# 테스트 데이터 (피처)\nX_test = all_data[all_data['월ID'] == 34]\nX_test = X_test.drop(['월간 판매량'], axis=1)\n\n# 훈련 데이터 (타깃값)\ny_train = all_data[all_data['월ID'] < 33]['월간 판매량']\n# 검증 데이터 (타깃값)\ny_valid = all_data[all_data['월ID'] == 33]['월간 판매량']\n\n# 가비지 컬렉션\ndel all_data\ngc.collect();","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:05:57.321748Z","iopub.execute_input":"2024-11-27T10:05:57.322209Z","iopub.status.idle":"2024-11-27T10:06:01.937661Z","shell.execute_reply.started":"2024-11-27T10:05:57.322142Z","shell.execute_reply":"2024-11-27T10:06:01.936597Z"}},"outputs":[],"execution_count":155},{"cell_type":"markdown","source":"## 🦾 9.4.8 모델 훈련 및 성능 검증\n\n- 베이스라인 모델과 동일","metadata":{}},{"cell_type":"code","source":"import lightgbm as lgb\nfrom lightgbm import early_stopping, log_evaluation # 버전차이로 지원하지 않는 부분 설정\n\n# LightGBM 하이퍼파라미터\nparams = {'metric': 'rmse',  # 평가지표: RMSE\n          'num_leaves': 255,\n          'learning_rate': 0.005,  # 이전 학습율 0.01\n          'feature_fraction': 0.75,  # 추가1\n          'bagging_fraction': 0.75,  # 추가2\n          'bagging_freq': 5,  # 추가3\n          'force_col_wise': True,\n          'random_state': 10}\n\n# categorical 피처 설정 : 도시, 대분류, 월 추가\ncat_features = ['상점ID', '도시', '상품분류ID', '대분류', '월']\n\n# LightGBM 훈련 및 검증 데이터셋\ndtrain = lgb.Dataset(X_train, y_train)\ndvalid = lgb.Dataset(X_valid, y_valid)\n\n\n# LightGBM 모델 훈련\n\n# 100회 반복마다 검증지표 출력(verbose_eval 파라미터 대체)\n# early_stopping_rounds=150 대체 추가\ncallbacks = [log_evaluation(period=100),  # verbose_eval 대체\n            early_stopping(stopping_rounds=150)]  # early_stopping_rounds 대체\n\nlgb_model = lgb.train(params=params,\n                      train_set=dtrain,\n                      num_boost_round=1500,  # 이전 학습: 500\n                      valid_sets=(dtrain, dvalid),\n                      # early_stopping_rounds=150, # v4.0.0 이후 지원하지 않습니다\n                      categorical_feature=cat_features,\n                      callbacks=callbacks) # verbose_eval, early_stopping_rounds 대체\n                      # verbose_eval=100 # v3.3.0 이후 제공하지 않습니다. ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:11:14.839583Z","iopub.execute_input":"2024-11-27T10:11:14.840125Z","iopub.status.idle":"2024-11-27T10:33:47.016142Z","shell.execute_reply.started":"2024-11-27T10:11:14.840074Z","shell.execute_reply":"2024-11-27T10:33:47.014345Z"}},"outputs":[{"name":"stdout","text":"[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n[LightGBM] [Info] Total Bins 3902\n[LightGBM] [Info] Number of data points in the train set: 9452298, number of used features: 30\n[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n[LightGBM] [Info] Start training from score 0.297707\nTraining until validation scores don't improve for 150 rounds\n[100]\ttraining's rmse: 1.01073\tvalid_1's rmse: 0.986993\n[200]\ttraining's rmse: 0.909083\tvalid_1's rmse: 0.922887\n[300]\ttraining's rmse: 0.857505\tvalid_1's rmse: 0.898233\n[400]\ttraining's rmse: 0.829175\tvalid_1's rmse: 0.888136\n[500]\ttraining's rmse: 0.810627\tvalid_1's rmse: 0.884038\n[600]\ttraining's rmse: 0.796874\tvalid_1's rmse: 0.882521\n[700]\ttraining's rmse: 0.786792\tvalid_1's rmse: 0.88237\n[800]\ttraining's rmse: 0.778925\tvalid_1's rmse: 0.882124\n[900]\ttraining's rmse: 0.772394\tvalid_1's rmse: 0.881969\n[1000]\ttraining's rmse: 0.766875\tvalid_1's rmse: 0.881747\n[1100]\ttraining's rmse: 0.761982\tvalid_1's rmse: 0.881581\n[1200]\ttraining's rmse: 0.757978\tvalid_1's rmse: 0.88149\n[1300]\ttraining's rmse: 0.754462\tvalid_1's rmse: 0.881124\n[1400]\ttraining's rmse: 0.751239\tvalid_1's rmse: 0.880934\n[1500]\ttraining's rmse: 0.748028\tvalid_1's rmse: 0.880874\nDid not meet early stopping. Best iteration is:\n[1470]\ttraining's rmse: 0.748911\tvalid_1's rmse: 0.880807\n","output_type":"stream"}],"execution_count":159},{"cell_type":"markdown","source":"## 🦾 9.4.9 예측 및 결과 제출\n","metadata":{}},{"cell_type":"code","source":"# 예측\npreds = lgb_model.predict(X_test).clip(0, 20)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:34:05.445429Z","iopub.execute_input":"2024-11-27T10:34:05.445919Z","iopub.status.idle":"2024-11-27T10:34:27.087682Z","shell.execute_reply.started":"2024-11-27T10:34:05.445875Z","shell.execute_reply":"2024-11-27T10:34:27.086723Z"}},"outputs":[],"execution_count":160},{"cell_type":"code","source":"# 제출 파일 생성\nsubmission['item_cnt_month'] = preds\nsubmission.to_csv('submission-engineered.csv', index=False)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:34:43.446496Z","iopub.execute_input":"2024-11-27T10:34:43.446958Z","iopub.status.idle":"2024-11-27T10:34:43.911465Z","shell.execute_reply.started":"2024-11-27T10:34:43.446919Z","shell.execute_reply":"2024-11-27T10:34:43.910201Z"}},"outputs":[],"execution_count":161},{"cell_type":"code","source":"# 추가로 이어서 할 경우 reset시 사용하세요.\n# del X_train, y_train, X_valid, y_valid, X_test, lgb_model, dtrain, dvalid\n# gc.collect();","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T10:06:01.980708Z","iopub.status.idle":"2024-11-27T10:06:01.981097Z","shell.execute_reply.started":"2024-11-27T10:06:01.980917Z","shell.execute_reply":"2024-11-27T10:06:01.980937Z"}},"outputs":[],"execution_count":null}]}