投稿時間:2023-01-24 16:27:50 RSSフィード2023-01-24 16:00 分まとめ(30件)

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IT	ITmedia 総合記事一覧	[ITmedia News] IT人材向け謎解きゲーム、paizaが無料公開　「縺薙ｓ縺ｫ縺｡縺ｯ」が意味するものは？	https://www.itmedia.co.jp/news/articles/2301/24/news146.html	itmedianewsit	2023-01-24 15:51:00
IT	ITmedia 総合記事一覧	[ITmedia News] パナソニック、録画用Blu-rayディスクを全て生産終了　後継商品はなし	https://www.itmedia.co.jp/news/articles/2301/24/news145.html	後継商品	2023-01-24 15:42:00
IT	ITmedia 総合記事一覧	[ITmedia News] DMM、Web3子会社設立　「かんぱに☆ガールズ RE:BLOOM」皮切りに、独自トークン開発へ	https://www.itmedia.co.jp/news/articles/2301/24/news140.html	itmedianewsdmm	2023-01-24 15:25:00
IT	ITmedia 総合記事一覧	[ITmedia News] 「AIアバター」流行　顔写真アップで“似てるけど美しい自分”に会える　480円で試したリアル報告	https://www.itmedia.co.jp/news/articles/2301/24/news139.html	itmedia	2023-01-24 15:04:00
TECH	Techable（テッカブル）	給付まで最短3日！庄内町の子育て支援金支給で「LINEで申請、セブン銀行ATMで受取」開始	https://techable.jp/archives/192736	受け取り	2023-01-24 06:15:20
TECH	Techable（テッカブル）	40問の質問で仕事への価値観を可視化。職場での相互理解のための「ココラボカルテ」	https://techable.jp/archives/192768	cocolabo	2023-01-24 06:14:43
python	Pythonタグが付けられた新着投稿 - Qiita	sklearn.exceptions.NotFittedError: Estimator not fitted, call fit before exploiting the model.	https://qiita.com/pp-qq/items/0a8bf24c042fc4702a33	error	2023-01-24 15:44:41
Linux	Ubuntuタグが付けられた新着投稿 - Qiita	Dockerとその最も使用されるコマンド	https://qiita.com/victorintoon/items/f5b038236415a546a517	docker	2023-01-24 15:23:55
Docker	dockerタグが付けられた新着投稿 - Qiita	Dockerとその最も使用されるコマンド	https://qiita.com/victorintoon/items/f5b038236415a546a517	docker	2023-01-24 15:23:55
Docker	dockerタグが付けられた新着投稿 - Qiita	【初心者】Docker+Django+MySQLで環境構築	https://qiita.com/ri-tama/items/328446e5194f40877cf7	docker	2023-01-24 15:09:54
Azure	Azureタグが付けられた新着投稿 - Qiita	Azure Cognitive Search あれこれ	https://qiita.com/coitate/items/cd8c78a964f7dc852830	azurecognitivesearch	2023-01-24 15:59:20
海外TECH	DEV Community	Transforming Categorical Data: A Practical Guide to Handling Non-Numerical Variables for Machine Learning Algorithms.	https://dev.to/anurag629/transforming-categorical-data-a-practical-guide-to-handling-non-numerical-variables-for-machine-learning-algorithms-cld	Transforming Categorical Data A Practical Guide to Handling Non Numerical Variables for Machine Learning Algorithms There are several ways to deal with categorical data also known as label data in data science One hot encodingLabel encodingDummy encodingBinningCount EncodingFrequency EncodingTarget EncodingThe appropriate technique will depend on the specific data and the goals of the analysis It s important to note that some algorithms like decision trees and random forest can handle categorical variables directly so encoding may not be necessary We will now go through all the above ways with some sample data set and also learn how o make our data trainable Let s Start One hot encodingOne hot encoding is a technique used to convert categorical variables into numerical values by creating a binary column for each category It is useful for handling categorical variables with multiple levels For example let s say we have a dataset of hand bags with a column called color that contains the following values red green and blue colorpriceunitsredgreenblueredgreenOne hot encoding would create three new binary columns one for each unique category with a value of indicating that the category is present and a value of indicating that it is not The resulting data might look like this colorpriceunitscolor redcolor greencolor blueredgreenblueredgreenAs you can see the original color column has been replaced by three new binary columns one for each unique category Each row now has a value of in exactly one of these new columns indicating the presence of that category But wait you should have one question How to do it using python So let s do it using python In Python You can use the get dummies function from the pandas library to apply one hot encoding to the color column of your dataframe Here is an example of how to do it import pandas as pd Create example dataframedf pd DataFrame color red green blue red green price units Apply one hot encoding to color columndf encoded pd get dummies df columns color print df encoded Alternatively you can use the OneHotEncoder class from the sklearn preprocessing library to apply one hot encoding from sklearn preprocessing import OneHotEncoder Create example dataframedf pd DataFrame color red green blue red green price units Create an instance of the encoderencoder OneHotEncoder sparse False Fit and transform the color columncolor encoded encoder fit transform df color Create new dataframe with the encoded valuesdf encoded pd concat df drop columns color pd DataFrame color encoded columns encoder get feature names color axis print df encoded The resulting dataframe will look the same as the previous one but the columns will have a prefix color x rather color Label encodingLabel encoding is a technique used to convert categorical variables into numerical values by assigning a unique integer value to each category It is useful for handling ordinal variables where the order of the categories matters For example let s say we have a dataset with a column called size that contains the following values small medium large Label encoding would replace each category with an integer such as small medium large The resulting data might look like this sizeencoded sizesmallmediumlargesmallmediumAs you can see the original size column has been replaced by encoded size column each row now has a unique integer value representing the category You can use the LabelEncoder class from the sklearn preprocessing library to apply label encoding to your data Here is an example of how to do it from sklearn preprocessing import LabelEncoder Create example dataframedf pd DataFrame size small medium large small medium price units Create an instance of the encoderencoder LabelEncoder Fit and transform the size columndf encoded size encoder fit transform df size print df The resulting dataframe df will have an new column encoded size representing the encoded values of size column The resulting dataframe will look like this sizepriceunitsencoded sizesmallmediumlargesmallmediumIt s important to note that label encoding changes the relationship between the categories It assigns a unique number to each category but it doesn t take into account the ordinal relationship between the categories In this case the encoded values of small medium and large are and respectively but it doesn t mean that small is half the size of medium or large is twice the size of medium Dummy EncodingDummy encoding also known as indicator encoding is a technique used to convert categorical variables into numerical values by creating binary columns for each category similar to one hot encoding but it doesn t remove any column It is useful when working with categorical variables with many levels For example let s say we have a dataset with a column called color that contains the following values red green blue Dummy encoding would create three new binary columns one for each unique category with a value of indicating that the category is present and a value of indicating that it is not The resulting data might look like this colorredgreenblueredgreenblueredgreenAs you can see the original color column is still present in the table but three new binary columns one for each unique category has been added Each row now has a value of in exactly one of these new columns indicating the presence of that category You can use the pd concat function from the pandas library to apply dummy encoding to the color column of your dataframe here is an example of how to do it Create example dataframedf pd DataFrame color red green blue red green price units Apply dummy encoding to color columndf encoded pd concat df pd get dummies df color axis print df encoded The resulting dataframe df encoded will have three new binary columns one for each unique category in the color column with a value of indicating that the category is present and a value of indicating that it is not The original color column is still present in the table The resulting dataframe will look like this colorpriceunitsredgreenblueredgreenblueredgreen BinningBinning is a technique used to group numerical values into bins or ranges it is used to handle numerical variables with a large number of unique values Binning can be useful for creating categorical variables from numerical ones and for handling outliers in the data For example let s say we have a dataset with a column called age that contains the following values To apply binning we can divide the range of values into a pre defined number of intervals or bins For example we can divide the range of ages into four bins This would group the ages into four categories young middle aged old and very old The resulting data might look like this ageage binyoungyoungmiddle agedmiddle agedoldoldvery oldAs you can see the original age column is still present in the table but a new column age bin has been added which contains the binned values for each age The rows in the age bin column now contain categorical values representing the age group You can use the cut function from the pandas library to apply binning to the age column of your dataframe here is an example of how to do it Create example dataframedf pd DataFrame age price units Apply binning to age columndf age bin pd cut df age bins labels young middle aged old very old print df The resulting dataframe df will have an new column age bin representing the binned values of age column The resulting dataframe will look like this agepriceunitsage binyoungyoungmiddle agedmiddle agedoldoldvery oldAs you can see the original age column is still present in the table but a new column age bin has been added which contains the binned values for each age The rows in the age bin column now contain categorical values representing the age group Count EncodingCount encoding is a technique used to convert categorical variables into numerical values by counting the number of occurrences of each category in the dataset It is used to handle categorical variables with many levels For example let s say we have a dataset with a column called product that contains the following values apple orange banana apple orange apple banana Count encoding would replace each category with the number of times it appears in the dataset The resulting data might look like this productcount encodedappleorangebananaappleorangeapplebananaAs you can see the original product column is still present in the table but a new column count encoded has been added which contains the count encoded values for each product The rows in the count encoded column now contain unique integer values representing the number of times each product appears in the dataset You can use the value counts function from the pandas library to apply count encoding to the product column of your dataframe here is an example of how to do it Create example dataframedf pd DataFrame product apple orange banana apple orange apple banana price units Apply count encoding to product columndf count encoded df product map df product value counts print df The resulting dataframe df will have an new column count encoded representing the count encoded values of product column The resulting dataframe will look like this productpriceunitscount encodedappleorangebananaappleorangeapplebanana Frequency EncodingFrequency encoding is a technique used to convert categorical variables into numerical values by representing each category as the proportion of occurrences of that category in the dataset It is similar to count encoding but it normalizes the count by dividing it by the total number of occurrences of all categories in the dataset It is used to handle categorical variables with many levels For example let s say we have a dataset with a column called product that contains the following values apple orange banana apple orange apple banana Frequency encoding would replace each category with the proportion of times it appears in the dataset The resulting data might look like this productfrequency encodedappleorangebananaappleorangeapplebananaAs you can see the original product column is still present in the table but a new column frequency encoded has been added which contains the frequency encoded values for each product The rows in the frequency encoded column now contain decimal values between and representing the proportion of times each product appears in the dataset You can use the value counts function from the pandas library to apply frequency encoding to the product column of your dataframe here is an example of how to do it Create example dataframedf pd DataFrame product apple orange banana apple orange apple banana price units Apply frequency encoding to product columndf frequency encoded df product map df product value counts normalize True print df The resulting dataframe df will have an new column frequency encoded representing the frequency encoded values of product column The resulting dataframe will look like this productpriceunitsfrequency encodedappleorangebananaappleorangeapplebanana Target EncodingTarget Encoding is a technique used to convert categorical variables into numerical values by representing each category as the mean of the target variable for that category This technique is used when the categorical variable has a large number of levels and is also useful in situations where the data is highly imbalanced For example let s say we have a dataset with a column called product and a target variable called sales that contains the following values productsalesappleorangebananaappleorangeapplebananaTarget encoding would replace each category in the product column with the mean of the sales column for that category The resulting data might look like this productsalestarget encodedappleorangebananaappleorangeapplebananaAs you can see the original product column is still present in the table but a new column target encoded has been added which contains the target encoded values for each product The rows in the target encoded column now contain decimal values representing the mean of the sales column for each product You can use the groupby function from the pandas library to apply target encoding to the product column of your dataframe here is an example of how to do it Create example dataframedf pd DataFrame product apple orange banana apple orange apple banana sales Apply target encoding to product columndf target encoded df groupby product sales transform mean print df The resulting dataframe df will have an new column target encoded representing the mean of sales column for each product The resulting dataframe will look like this productsalestarget encodedappleorangebananaappleorangeapplebanana This blog is a part of a daysdatascience series If you want to follow the whole series then go to the below links GitHub link Complete Data Science Bootcamp Main Post Complete Data Science Bootcamp If you liked the post and wanted me to support then	2023-01-24 06:15:15
医療系	医療介護 CBnews	コロナ患者断る病院、病床確保料の返還も-人員不足で入院要請拒否の有無を調査へ、厚労省	https://www.cbnews.jp/news/entry/20230124152806	人員不足	2023-01-24 16:00:00
金融	JPX マーケットニュース	[東証]新規上場の承認（TOKYO PRO Market）：Ｎｏ．１都市開発（株）	https://www.jpx.co.jp/equities/products/tpm/issues/index.html	tokyopromarketno	2023-01-24 15:30:00
金融	ニッセイ基礎研究所	観光需要回復の兆し－政策の後押しを受けて、国内外の旅行需要は回復するか	https://www.nli-research.co.jp/topics_detail1/id=73712?site=nli	目次ーはじめにー観光客の現状訪日外国人旅行者数国内旅行者数旅行消費額ー水際対策現在の水際対策の概要水際対策の問題点中国はインバウンド回復へ大きな影響を及ぼすー国内旅行需要喚起策政府の旅行需要喚起策の変遷全国旅行支援を受けて日本人宿泊者数は増加宿泊施設は人手不足が深刻化都道府県の差全国旅行支援の使用条件ーおわりに日本で新型コロナウイルスが蔓延して年が経過した。	2023-01-24 15:27:58
金融	ニッセイ基礎研究所	消費者の節電意識と行動～高齢層ほど熱心、若年層の方が消極的	https://www.nli-research.co.jp/topics_detail1/id=73650?site=nli	ーおわりにこれまでみてきたことをまとめると、記録的な物価高を背景に、消費者の節電に対する意識は高まっており、昨年月時点では、割以上の人が何らかの節電行動を取るとしている。	2023-01-24 15:43:39
金融	ニッセイ基礎研究所	三次分配と保険（中国）	https://www.nli-research.co.jp/topics_detail1/id=73630?site=nli	それは、対象としている保険が市政府と民間保険会社が協働で運営する医療保険であること、共同富裕民間企業の寄付の一環で、社会的弱者に提供していること、プラットフォーマーが金融商品における、人と商品の「仲介」というコアコンピタンスを活かした運営をしていること、である。	2023-01-24 15:52:04
金融	日本銀行：RSS	国債補完供給の対象先公募の結果について	http://www.boj.or.jp/mopo/measures/select/s_release/srel230124a.pdf	補完	2023-01-24 16:00:00
海外ニュース	Japan Times latest articles	Kishida’s focus on child care leads to speculation over sale tax hike	https://www.japantimes.co.jp/news/2023/01/24/national/politics-diplomacy/focus-kishida-tax/	economic	2023-01-24 15:11:37
ニュース	BBC News - Home	Oscar nominations 2023: Top Gun leads sequels surge	https://www.bbc.co.uk/news/entertainment-arts-64371559?at_medium=RSS&at_campaign=KARANGA	nominations	2023-01-24 06:28:52
ニュース	BBC News - Home	The Papers: 'Zahawi faces sack' and 'killer posed as child'	https://www.bbc.co.uk/news/blogs-the-papers-64381624?at_medium=RSS&at_campaign=KARANGA	affairs	2023-01-24 06:46:21
ニュース	BBC News - Home	Southampton: Carabao Cup semi-final a boost for Sport Republic owners	https://www.bbc.co.uk/sport/football/64296717?at_medium=RSS&at_campaign=KARANGA	Southampton Carabao Cup semi final a boost for Sport Republic ownersOne year on from Sport Republic assuming control Southampton are bottom of the Premier League but a Carabao Cup semi final hints at a brighter future	2023-01-24 06:17:56
ビジネス	東洋経済オンライン	在宅時ねらう｢関東連続強盗｣家庭での最強対策 侵入者から身を守る｢パニックルーム｣の有効性 | 災害･事件･裁判 | 東洋経済オンライン	https://toyokeizai.net/articles/-/648024?utm_source=rss&utm_medium=http&utm_campaign=link_back	東洋経済オンライン	2023-01-24 15:30:00
ニュース	Newsweek	プーチン邸に防空システム配備、と報道。西側の長距離兵器を警戒？	https://www.newsweekjapan.jp/stories/world/2023/01/post-100678.php	パーンツィリがモスクワの政府機関の屋上に配備されていることを示す証拠がネット上で拡散すると同時に、地対空ミサイルシステム「S」もロシア全土で目撃されている。	2023-01-24 15:44:32
ニュース	Newsweek	ドライブスルー客が店員を拉致しようとする恐怖の瞬間	https://www.newsweekjapan.jp/stories/world/2023/01/post-100675.php		2023-01-24 15:20:00
IT	週刊アスキー	今度はライラがバニーに！『リゼロス』で「シーズンガチャ【シックな宵闇バニー】」が開催	https://weekly.ascii.jp/elem/000/004/121/4121780/	lostinmemories	2023-01-24 15:55:00
IT	週刊アスキー	和紅茶「嘉一」を使用した至高のシフォンケーキ、ホテル日航福岡で3月31日まで販売中	https://weekly.ascii.jp/elem/000/004/121/4121718/	大分県杵築市	2023-01-24 15:30:00
IT	週刊アスキー	朝一で品川の食肉市場から特選したホルモンを味わう「芝浦ホルモン」が西新宿7丁目エリアにグランドオープン	https://weekly.ascii.jp/elem/000/004/121/4121732/	食肉市場	2023-01-24 15:20:00
マーケティング	AdverTimes	アシックス、園児の送迎バス置きざり問題解消へ向け、実証実験を開始	https://www.advertimes.com/20230124/article409578/	アシックス、園児の送迎バス置きざり問題解消へ向け、実証実験を開始アシックスはこのほど、近畿タクシー、神戸常盤大学附属ときわ幼稚と連携し、社会課題となっている園児の送迎バス置きざりを防ぐ実証実験を開始する。	2023-01-24 06:51:32
ニュース	THE BRIDGE	東南アジア発、2023年注目のスタートアップ（７）：デジタル版「母子健康手帳」で母親をユーザ獲得、育児EC市場を狙う「PrimaKu」	https://thebridge.jp/2023/01/capa-2022w-primaku-cyberagentcapital-insight	東南アジア発、年注目のスタートアップデジタル版「母子健康手帳」で母親をユーザ獲得、育児EC市場を狙う「PrimaKu」これは、CyberAgentPitchingArena年冬版の取材の一部だ。	2023-01-24 06:00:53