{"id":183,"date":"2020-10-15T05:44:00","date_gmt":"2020-10-14T20:44:00","guid":{"rendered":"https:\/\/www.ritzcolor.net\/?p=183"},"modified":"2020-12-10T20:07:51","modified_gmt":"2020-12-10T11:07:51","slug":"%e3%81%a9%e7%b4%a0%e4%ba%ba%e3%81%aekaggle-2-%e3%83%87%e3%83%bc%e3%82%bf%e3%82%92%e3%81%bf%e3%81%a6%e3%81%bf%e3%81%9f","status":"publish","type":"post","link":"https:\/\/www.ritzcolor.net\/?p=183","title":{"rendered":"\u3069\u7d20\u4eba\u306eKaggle 2 -\u30c7\u30fc\u30bf\u3092\u307f\u3066\u307f\u305f-"},"content":{"rendered":"\n

\u4eca\u5f8c\u306f\u30b3\u30fc\u30c9\u3092\u30d6\u30ed\u30b0\u5185\u3067\u66f8\u304f\u3053\u3068\u3082\u3042\u308b\u3060\u308d\u3046\u3068\u601d\u3044\u3001wordpress\u3068\u30b3\u30fc\u30c9\u3067\u691c\u7d22\u3057\u305f\u7d50\u679c\u3001\u4e0b\u8a18\u306e\u30b5\u30a4\u30c8\u304b\u3089Highlighting Code Block<\/strong>\u3068\u3044\u3046\u30d7\u30e9\u30b0\u30a4\u30f3 \u3092\u898b\u3064\u3051\u3066\u3001\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3057\u305f\u3002<\/p>\n\n\n\n

https:\/\/wemo.tech\/2122<\/a><\/p>\n\n\n\n

\u4eca\u56de\u306f\u30c7\u30fc\u30bf\u3092\u307e\u305a\u306f\u773a\u3081\u3066\u307f\u308b\u3053\u3068\u3092\u76ee\u7684\u3068\u3057\u3066\u3001\u30b3\u30fc\u30c9\u3084\u8003\u5bdf\u3092\u8a18\u8ff0\u3057\u3066\u3044\u304d\u307e\u3059\u3002\u307e\u305a\u306f\u30c7\u30fc\u30bf\u3092\u683c\u7d0d\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

#\u5fc5\u8981\u306a\u30c7\u30fc\u30bf\u3092\u683c\u7d0d\u3002()\u5185\u306f\u4fdd\u5b58\u3057\u305f\u30d5\u30a9\u30eb\u30c0\u30d1\u30b9\u3092\u8a18\u8f09\u3057\u307e\u3059\u3002\ntrain_df = pd.read_csv('\/kaggle\/input\/sales_train.csv')\ntest_df = pd.read_csv('\/kaggle\/input\/test.csv')\nitem = pd.read_csv('\/kaggle\/input\/items.csv')\nshops = pd.read_csv('\/kaggle\/input\/shops.csv')\ncategories = pd.read_csv('\/kaggle\/input\/item_categories.csv')<\/code><\/pre><\/div>\n\n\n\n
\u305d\u308c\u3067\u306f\u305d\u308c\u305e\u308c\u306e\u30c7\u30fc\u30bf\u306e\u4e2d\u8eab\u3092\u307f\u3066\u307f\u307e\u3059\u3002<\/p>\n\n\n\n
#\u5404\u30c7\u30fc\u30bf\u306e\u7279\u5fb4\u91cf\u3068\u6b20\u640d\u5024\u306e\u6570\u3092\u78ba\u8a8d\nprint("train_df")\ntrain_df.info()\nprint("----------------")\nprint("test_df")\ntest_df.info()\nprint("----------------")\nprint("item")\nitem.info()\nprint("----------------")\nprint("shops")\nshops.info()\nprint("----------------")\nprint("categories")\ncategories.info()\nprint("----------------")<\/code><\/pre><\/div>\n\n\n\n
\u4e0a\u306e\u30b3\u30fc\u30c9\u306e\u5b9f\u884c\u7d50\u679c\u306f\u3053\u3061\u3089\u3002\ntrain_df\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2935849 entries, 0 to 2935848\nData columns (total 6 columns):\n #   Column          Dtype  \n---  ------          -----  \n 0   date            object \n 1   date_block_num  int64  \n 2   shop_id         int64  \n 3   item_id         int64  \n 4   item_price      float64\n 5   item_cnt_day    float64\ndtypes: float64(2), int64(3), object(1)\nmemory usage: 134.4+ MB\n----------------\ntest_df\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   shop_id  214200 non-null  int64\n 2   item_id  214200 non-null  int64\ndtypes: int64(3)\nmemory usage: 4.9 MB\n----------------\nitem\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   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----------------\nshops\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   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----------------\ncategories\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   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----------------<\/pre>\n\n\n\n
train_df\u3092train.head()\u3067\u307f\u3066\u307f\u308b\u3068\u3001\u4e0b\u306e\u3088\u3046\u306a\u69cb\u6210\u3002\u5148\u982d5\u884c\u3057\u304b\u51fa\u529b\u3057\u3066\u307e\u305b\u3093\u304c\u3001\u4e0a\u306e\u7d50\u679c\u304b\u3089\u898b\u308b\u30682935848\u884c\u306e\u30c7\u30fc\u30bf\u304c\u3042\u308b\u3088\u3046\u3067\u3059\u3002shop\u3068item\u304cid\u3067\u8a18\u8f09\u3055\u308c\u3066\u3044\u308b\u306e\u3067\u3001shops\u3068items\u3092\u7d50\u5408\u3057\u3066\u3001\u305d\u308c\u305e\u308c\u306e\u60c5\u5831\u3092\u4ed8\u3051\u52a0\u3048\u308b\u5fc5\u8981\u304c\u3042\u308a\u305d\u3046\u3067\u3059\u3002item_price\u306f\u5358\u4fa1\u3067\u3001item_cnt_day\u306f\u3071\u3063\u3068\u898b\u308f\u304b\u3089\u306a\u3044\u306e\u3067\u3001Kaggle\u30c7\u30fc\u30bf\u60c5\u5831\u3092\u5f8c\u307b\u3069\u78ba\u8a8d\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u305d\u3046\u3067\u3059\u3002(\u30ab\u30e9\u30e0\u304c\u4e00\u3064\u305a\u308c\u3066\u3044\u308b\u3088\u3046\u3067\u3059\u3002\u308f\u304b\u308a\u3065\u3089\u304f\u3066\u3059\u3044\u307e\u305b\u3093\u3002)<\/p>\n\n\n\n
date<\/th>date_block_num<\/th>shop_id<\/th>item_id<\/th>item_price<\/th>item_cnt_day<\/th><\/tr><\/thead>
0<\/th>02.01.2013<\/td>0<\/td>59<\/td>22154<\/td>999.00<\/td>1.0<\/td><\/tr>
1<\/th>03.01.2013<\/td>0<\/td>25<\/td>2552<\/td>899.00<\/td>1.0<\/td><\/tr>
2<\/th>05.01.2013<\/td>0<\/td>25<\/td>2552<\/td>899.00<\/td>-1.0<\/td><\/tr>
3<\/th>06.01.2013<\/td>0<\/td>25<\/td>2554<\/td>1709.05<\/td>1.0<\/td><\/tr>
4<\/th>15.01.2013<\/td>0<\/td>25<\/td>2555<\/td>1099.00<\/td>1.0<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
test_df\u3092\u540c\u69d8\u306b\u307f\u3066\u307f\u308b\u3068\u3001\u307e\u305f\u30ab\u30e9\u30e0\u304c\u4e00\u3064\u305a\u308c\u3066\u307e\u3059\u304c\u4e0b\u306e\u3088\u3046\u306a\u5f62\u3002\u3069\u3093\u306a\u5f62\u3067\u51fa\u529b\u3059\u308b\u304b\u3061\u3083\u3093\u3068\u78ba\u8a8d\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u304c\u3001\u3053\u308c\u3089\u306e\u58f2\u308a\u4e0a\u3052\u3092\u4e88\u6e2c\u3059\u308b\u306e\u304c\u3053\u306e\u30b3\u30f3\u30da\u306e\u76ee\u7684\u3067\u3059\u3002\u3053\u3061\u3089\u3082shop, item\u30c6\u30fc\u30d6\u30eb\u3068\u7d50\u5408\u3057\u3066\u3084\u3089\u306a\u3044\u3068\u3055\u3063\u3071\u308a\u308f\u304b\u3089\u306a\u3044\u3067\u3059\u306d\u3002<\/p>\n\n\n\n

ID<\/th>		shop_id<\/th>item_id<\/th><\/tr><\/thead>
0<\/th>0<\/td>5<\/td>5037<\/td><\/tr>
1<\/th>1<\/td>5<\/td>5320<\/td><\/tr>
2<\/th>2<\/td>5<\/td>5233<\/td><\/tr>
3<\/th>3<\/td>5<\/td>5232<\/td><\/tr>
4<\/th>4<\/td>5<\/td>5268<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\u3055\u3066\u3053\u3061\u3089\u304citem\u30c6\u30fc\u30d6\u30eb\u3002category_id\u304c\u3042\u308a\u307e\u3059\u306e\u3067\u3001\u3053\u3061\u3089\u3082category\u30c6\u30fc\u30d6\u30eb\u3068\u7d50\u5408\u3057\u3066\u3084\u308b\u5fc5\u8981\u304c\u3042\u308a\u305d\u3046\u3067\u3059\u3002\u305d\u3057\u3066item_name\u304c\u307e\u3055\u304b\u306e\u304a\u305d\u3089\u304f\u30ed\u30b7\u30a2\u8a9e\u3002\u3002\u3002Titanic\u3067\u306f\u5148\u982d\u6587\u5b57\u306a\u3069\u3092\u629c\u304d\u51fa\u3057\u3066\u30b0\u30eb\u30fc\u30d7\u306b\u518d\u5206\u914d\u3057\u3066\u3044\u307e\u3057\u305f\u304c\u3001\u3053\u308c\u306f\u3069\u3046\u3057\u305f\u3082\u306e\u304b\u3002\u3082\u3046\u5c11\u3057\u5206\u6790\u3092\u9032\u3081\u306a\u304c\u3089\u5bfe\u5fdc\u3092\u8003\u3048\u306a\u3044\u3068\u3044\u3051\u306a\u3044\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002<\/p>\n\n\n\n
item_name<\/th>item_id<\/th>item_category_id<\/th><\/tr><\/thead>
0<\/th>! \u0412\u041e \u0412\u041b\u0410\u0421\u0422\u0418 \u041d\u0410\u0412\u0410\u0416\u0414\u0415\u041d\u0418\u042f (\u041f\u041b\u0410\u0421\u0422.) D<\/td>0<\/td>40<\/td><\/tr>
1<\/th>!ABBYY FineReader 12 Professional Edition Full…<\/td>1<\/td>76<\/td><\/tr>
2<\/th>***\u0412 \u041b\u0423\u0427\u0410\u0425 \u0421\u041b\u0410\u0412\u042b (UNV) D<\/td>2<\/td>40<\/td><\/tr>
3<\/th>***\u0413\u041e\u041b\u0423\u0411\u0410\u042f \u0412\u041e\u041b\u041d\u0410 (Univ) D<\/td>3<\/td>40<\/td><\/tr>
4<\/th>***\u041a\u041e\u0420\u041e\u0411\u041a\u0410 (\u0421\u0422\u0415\u041a\u041b\u041e) D<\/td>4<\/td>40<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\u3053\u3061\u3089\u304cshop\u30c6\u30fc\u30d6\u30eb\u3002\u3053\u308c\u307e\u305f\u30ed\u30b7\u30a2\u8a9e\u304c\u5149\u308a\u307e\u3059\u3002\u3002\u3002\u540d\u524d\u3057\u304b\u60c5\u5831\u304c\u306a\u3044\u3067\u3059\u304c\u3001\u58f2\u308a\u4e0a\u3052\u4e88\u6e2c\u306b\u5f79\u306b\u7acb\u3064\u306e\u3067\u3057\u3087\u3046\u304b\u3002\u3002\u3002<\/p>\n\n\n\n

shop_name<\/th>		shop_id<\/th><\/tr><\/thead>
0<\/th>!\u042f\u043a\u0443\u0442\u0441\u043a \u041e\u0440\u0434\u0436\u043e\u043d\u0438\u043a\u0438\u0434\u0437\u0435, 56 \u0444\u0440\u0430\u043d<\/td>0<\/td><\/tr>
1<\/th>!\u042f\u043a\u0443\u0442\u0441\u043a \u0422\u0426 “\u0426\u0435\u043d\u0442\u0440\u0430\u043b\u044c\u043d\u044b\u0439” \u0444\u0440\u0430\u043d<\/td>1<\/td><\/tr>
2<\/th>\u0410\u0434\u044b\u0433\u0435\u044f \u0422\u0426 “\u041c\u0435\u0433\u0430”<\/td>2<\/td><\/tr>
3<\/th>\u0411\u0430\u043b\u0430\u0448\u0438\u0445\u0430 \u0422\u0420\u041a “\u041e\u043a\u0442\u044f\u0431\u0440\u044c-\u041a\u0438\u043d\u043e\u043c\u0438\u0440”<\/td>3<\/td><\/tr>
4<\/th>\u0412\u043e\u043b\u0436\u0441\u043a\u0438\u0439 \u0422\u0426 “\u0412\u043e\u043b\u0433\u0430 \u041c\u043e\u043b\u043b”<\/td>4<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
category\u306f\u3053\u3061\u3089\u3002\u3053\u308c\u307e\u305f\u30ed\u30b7\u30a2\u8a9e\u306e\u540d\u524d\u3057\u304b\u306a\u3044\u3002\u3002\u3002\u5168\u90e8\u7d50\u5408\u3057\u305f\u3068\u3053\u308d\u3067\u58f2\u308a\u4e0a\u3052\u4e88\u6e2c\u306e\u5f79\u306b\u7acb\u3064\u30a4\u30e1\u30fc\u30b8\u304c\u6e67\u304d\u307e\u305b\u3093\u304c\u3001\u3069\u3046\u306a\u3063\u3066\u3044\u304f\u3067\u3057\u3087\u3046\u3002\u3002\u3002<\/p>\n\n\n\n

item_category_name<\/th>		item_category_id<\/th><\/tr><\/thead>
0<\/th>PC – \u0413\u0430\u0440\u043d\u0438\u0442\u0443\u0440\u044b\/\u041d\u0430\u0443\u0448\u043d\u0438\u043a\u0438<\/td>0<\/td><\/tr>
1<\/th>\u0410\u043a\u0441\u0435\u0441\u0441\u0443\u0430\u0440\u044b – PS2<\/td>1<\/td><\/tr>
2<\/th>\u0410\u043a\u0441\u0435\u0441\u0441\u0443\u0430\u0440\u044b – PS3<\/td>2<\/td><\/tr>
3<\/th>\u0410\u043a\u0441\u0435\u0441\u0441\u0443\u0430\u0440\u044b – PS4<\/td>3<\/td><\/tr>
4<\/th>\u0410\u043a\u0441\u0435\u0441\u0441\u0443\u0430\u0440\u044b – PSP<\/td>4<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\u30c7\u30fc\u30bf\u3092\u4e00\u901a\u308a\u307f\u305f\u3068\u3053\u308d\u3067\u3001\u4e0b\u8a18\u306eURL\u304b\u3089\u3053\u306e\u30b3\u30f3\u30da\u306e\u76ee\u7684\u3092\u307f\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
https:\/\/www.kaggle.com\/c\/competitive-data-science-predict-future-sales\/overview\/evaluation<\/a><\/p>\n\n\n\n
Submissions are evaluated by root mean squared error (RMSE)<\/a>. True target values are clipped into [0,20] range.\u3068\u8a18\u8f09\u304c\u3042\u308a\u307e\u3059\u306e\u3067\u3001\u307e\u305a\u8a55\u4fa1\u306fRMSE\u3067\u884c\u308f\u308c\u308b\u3088\u3046\u3067\u3059\u3002<\/p>\n\n\n\n
\u305d\u3057\u3066For each id in the test set, you must predict a total number of sales. The file should contain a header and have the following format:\u306e\u8a18\u8f09\u304b\u3089sales\u306e\u6570\u91cf\u3092\u4e88\u6e2c\u3059\u308b\u3088\u3046\u3067\u3059\u3002\u307e\u305fsample_submission.csv\u3092\u307f\u305f\u3068\u3053\u308d\u3001214200\u500b\u306e\u30c7\u30fc\u30bf\u6570\u306a\u306e\u3067\u5358\u4f4d\u306fitem\u3067\u9593\u9055\u3048\u306a\u3055\u305d\u3046\u3067\u3059\u3002<\/p>\n\n\n\n
\u3064\u3044\u3067\u306bDescription\u3092\u307f\u3066\u307f\u308b\u3068\u3001<\/p>\n\n\n\n
In this competition you will work with a challenging time-series dataset consisting of daily sales data, kindly provided by one of the largest Russian software firms – 1C Company.<\/p>\n\n\n\n
We are asking you to predict total sales for every product and store in the next month. By solving this competition you will be able to apply and enhance your data science skills.<\/p>\n\n\n\n
\u3068\u306e\u3053\u3068\u3067\u3001\u30ed\u30b7\u30a2\u306e\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u4f01\u696d\u306e\u6765\u6708\u306e\u58f2\u308a\u4e0a\u3052\u3092\u4e88\u6e2c\u3059\u308b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\u3002train_df\u306f\u65e5\u5225\u3067\u51fa\u3066\u3044\u307e\u3057\u305f\u306e\u3067\u3001\u3053\u306e\u8fba\u308a\u3082\u52a0\u5de5\u304c\u5fc5\u8981\u305d\u3046\u3067\u3059\u3002<\/p>\n\n\n\n
\u3068\u3044\u3046\u3053\u3068\u3067\u3001\u6b21\u56de\u306ftrain_df\u306bitem, shop, category\u306e\u7d50\u5408\u3068\u5404\u30ab\u30e9\u30e0\u306e\u30c7\u30fc\u30bf\u578b\u306a\u3069\u3092\u9032\u3081\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
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