投稿時間:2022-03-30 18:42:57 RSSフィード2022-03-30 18:00 分まとめ(50件)

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TECH	Engadget Japanese	話題のクラフトサバイバル「Core Keeper」レビュー、色々シンプル・戦闘ハード！	https://japanese.engadget.com/core-keeper-080027944.html	話題のクラフトサバイバル「CoreKeeper」レビュー、色々シンプル・戦闘ハードエルデンリングばかりプレイしていて、その他のゲームの話題に関するアンテナが下がりに下がっていた筆者でしたが、そんな中でも耳にした話題のタイトルがあります。	2022-03-30 08:00:27
ROBOT	ロボスタ	ユカイ工学「ユカイなピコハンロボットキット」を新発売！ピコピコハンマーがダイナミックに動作 2台でバトルも可能	https://robotstart.info/2022/03/30/pikohan-robo-kit-yukai-engineering.html	ユカイ工学「ユカイなピコハンロボットキット」を新発売ピコピコハンマーがダイナミックに動作台でバトルも可能シェアツイートはてブ「ロボティクスで、世界をユカイに。	2022-03-30 08:52:31
ROBOT	ロボスタ	経産省が惣菜製造現場に「ロボットフレンドリー」導入を促進　惣菜盛付け現場にロボットと量子技術を正式導入、デモも公開	https://robotstart.info/2022/03/30/robot-friendry-meti.html	経産省が惣菜製造現場に「ロボットフレンドリー」導入を促進惣菜盛付け現場にロボットと量子技術を正式導入、デモも公開シェアツイートはてブ経済産業省は、ロボットを導入しやすい「ロボットフレンドリーな環境」の実現に向けて、官民一体で「惣菜盛付ロボットや製造工程最適化のためのシステム」への取組みを発表した。	2022-03-30 08:36:53
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 東京・丸ビルのプロント新業態、パスタ自動調理ロボット「P-Robo」を導入	https://www.itmedia.co.jp/business/articles/2203/30/news143.html	itmedia	2022-03-30 17:48:00
IT	ITmedia 総合記事一覧	[ITmedia News] 「社長のおごり自販機」全国展開　2人で買えば飲み物無料、社内の雑談促進を支援	https://www.itmedia.co.jp/news/articles/2203/30/news162.html	itmedia	2022-03-30 17:47:00
IT	情報システムリーダーのためのIT情報専門サイト IT Leaders	NEC、作成ファイルをクラウドに保存してPCに残さないサービス「NEC Cloud File Sync」 | IT Leaders	https://it.impress.co.jp/articles/-/22922	NEC、作成ファイルをクラウドに保存してPCに残さないサービス「NECCloudFileSync」ITLeadersNECは年月日、データレスクライアントサービス「NECCloudFileSync」を発表した。	2022-03-30 17:35:00
python	Pythonタグが付けられた新着投稿 - Qiita	Google Colaboratoryを使ったRNNの動作確認その2（2022年3月30日）	https://qiita.com/bugmaker/items/551781a950158ccae0a7	オリジナルの記事では、乱数によりノイズを乗せたサインカーブをテストデータとして用い、それをRNNで学習させています。	2022-03-30 17:33:32
AWS	AWSタグが付けられた新着投稿 - Qiita	RDS for Oracleでインスタンスクラス変更時に気をつけておくこと(初期化パラメータ)	https://qiita.com/asahide/items/be6d37f468ad4294e1c8	ただ、インスタンスクラスを変更する時に、パラメータグループの設定においてデフォルトで利用しているパラメータをそのままサイズアップして利用すると非効率になる場合があるので、その点について記載します。	2022-03-30 17:28:24
Docker	dockerタグが付けられた新着投稿 - Qiita	Laravel docker buildする用のDockerFileの書き方	https://qiita.com/reopa_sharkun/items/bbe523bbf5fb310a15ef	Laraveldockerbuildする用のDockerFileの書き方LaravelのコンテナをビルドしてECRにpushする場合などよく見かける開発用のdocker構成だと、volumeがリンクなので、そのままbuildしても何もファイルがない、という状態になる。	2022-03-30 17:44:08
技術ブログ	Mercari Engineering Blog	メルペイフロントエンドパフォーマンス改善報告	https://engineering.mercari.com/blog/entry/20220329-02e2d821b8/	refhttpsengihellip	2022-03-30 10:00:10
技術ブログ	Developers.IO	API Gateway に AWS CLI でタグを追加する方法を教えてください	https://dev.classmethod.jp/articles/tsnote-how-to-add-tags-for-apigateway-with-aws-cli/	apigateway	2022-03-30 08:15:07
技術ブログ	クックパッド開発者ブログ	CookpadTV の開発スタイルとエンジニアマネージャーの役割	https://techlife.cookpad.com/entry/2022/03/30/175224	CookpadTVの開発スタイルとエンジニアマネージャーの役割メディアプロダクト開発部の長田osadakeです。	2022-03-30 17:52:24
海外TECH	DEV Community	Python : Manipulation des dictionnaires	https://dev.to/ericlecodeur/python-manipulation-des-dictionnaires-2m03	Python Manipulation des dictionnairesLe cours accéléréPython est gratuit et sera publiéici sur dev to Je publierai un nouvel article tous les deux jours environ Pour ne rien manquer vous pouvez me suivre sur twitter Suivez EricLeCodeur Type de variable DictionnairesLes dictionnaires permettent de stocker plusieurs items dans une seule variable sous le format clé valeur Un dictionnaire est très semblable àune List mais au lieu d utiliser des index on utilise des clés Par exemple parfois nous aurions besoin d une liste d étudiant avec leur no leur nom et leur âge Pour ce faire nous pourrions utiliser une variable de type List que nous venons justement d apprendre student Mike Taylor print student Mike Taylor Quoique cet exemple soit fonctionnel il serait beaucoup plus simple et efficace si notre No d étudiant pouvait être la cléd accès Voici un exemple du résultat désirez C est à dire accéder àl information àpartir d un nom de clé print student no print student name Mike Taylorprint student age Ce type de liste avec une clépersonnalisée existe en Python sous le nom de Dictionary Créer un DictionaryVoici comment déclarer un Dictionary on utilisant les accolades student no name Mike Taylor age La structure d un Dictionary est sous forme de clé valeur La syntaxe est la suivante “nom de la clé valeur no Chaque clé valeur est séparée par une virgule student no name Mike Taylor age RécupérationLa récupération peut s effectuer directement ou avec la fonction get Directement print student no KeyError num Si la clén existe pas la syntaxe directe retourne une erreur “KeyError num et du coup arrête l exécution du codeFonction get print student get num NoneSi la clén existe pas la fonction get retourne la valeur “None du coup le code continu son exécution Avec la fonction get il est possible de retourner une valeur par défaut si la clén existe pas print student get num no key no key Ajouter une clé valeurPour ajouter une clé valeur il suffit d assigner student no name Mike Taylor age student city Paris print student no name Mike Taylor age city New York Effacer une clé valeurPour effacer une clé valeur du dictionnaire Il suffit d utiliser le mot clé del student no name Mike Taylor age del student age print student no name Mike Taylor Boucle dans les items d un DictionaryComme pour une List il est possible de faire une boucle dans un Dictionary Le seul truc c est qu il faut identifier àl avance sur quoi nous voulons faire la boucle Les clés Les valeurs Les deux Boucle sur les clés Pour ce faire il faut utiliser la fonction keys Cette fonction retourne une liste de toutes les clés du Dictionarystudent no name Mike Taylor age print student keys dict keys no name age Il sera donc possible de faire une boucle sur cette listestudent no name Mike Taylor age for key in student keys print key no name ageBoucle sur les valeurs Même principe que pour les clés Sauf cette fois il faut utiliser la fonction values student no name Mike Taylor age for value in student values print value Mike Taylor Boucle sur cléet valeurs Il faut utiliser la fonction items qui fait deux retours soit la cléet la valeur student no name Mike Taylor age for key value in student items print key value no name Mike Taylor age List de DictionaryUn pattern très populaire est de créer une List de Dictionary Ce pattern ressemble beaucoup àtable dans une base de données La List représente chaque ligne de la table et le Dictionary représente les colonnes de la table products id name iPadPro id name iPhone id name Charger print products name iPadPro ConclusionC est tout pour aujourd hui je publierai un nouvel article tous les deux jours environ Pour être sûr de ne rien rater vous pouvez me suivre sur twitter Suivre EricLeCodeur	2022-03-30 08:43:10
海外TECH	DEV Community	10 Lean Canvas Examples or How to Create Business Plan for Startup	https://dev.to/kateryna_pakhomova/10-lean-canvas-examples-or-how-to-create-business-plan-for-startup-ebg	Lean Canvas Examples or How to Create Business Plan for StartupThe original article was written by SoftFormance All businesses start with a business plan But what comes into your mind when you hear this word Probably a large document with complex numbers and pages of text Actually this means thorough preparation which is never too bad But in reality business plans never work perfectly So why waste so much time on it In the modern dynamic world you should be quick or be dead There s no time for lengthy business plans It s only about your ability to be as flexible as possible in order to adapt and prevail Imagine two competitive tech founders aiming to take the same market niche The first one aims to take the vacant place as fast as possible and sets flexibility as his top priority The second founder thoroughly analyzes all market entry aspects and prepares a detailed business model plan As a result it is perfectly prepared for taking the target niche but……it s already too late because this niche is taken by the first organization So don t act like a founder of the second business Focus on being flexible instead That s when the lean canvas approach comes into action We at Softformance start our cooperation with each client with a workshop that helps us come up with the most flexible approach Lean startup and its deviations are among our favorites What does this concept mean And how can it help your emerging tech idea Read this article to get your answers Here I provide a comprehensive overview of the lean canvas methodology and support it with the greatest lean canvas examples of successful companies If you read and follow this article s guidelines you ll have a business plan in minutes and your odds for success will grow times What is a lean canvas Before we dive into the promised success stories let s clarify what a lean startup actually is Basically this term derives from the lean startup methodology that was developed in by Eric Reis in his book The Lean Startup And the key point about the latter is flexibility Lean startup companies should be extremely flexible while launching their product and business There may be various deviations of a lean startup but it s always about taking the product development cycle as short as possible Actually there s nothing new about such a strategy To some extent it is as old as time Even a cook that creates a new dish and doesn t clearly understand what reaction to expect from the audience follows this approach You may see a visualization for the core lean startup principles in the image below As for a lean canvas it is the way to visualize the basics of the lean startup approach It goes about a table that includes all key considerations concerning lean startup principles You may see a blank lean canvas below Let s also clarify what each block in this table means ProblemIf the market fails to satisfy people with some services or offers it is a problem In a lean startup a problem means an opportunity In other words it is a specific demand gap or market niche that you aim to take with your product SolutionIt goes about your product created for solving the problem and filling the market gap Unique valueIt is a unique selling point that a startup can offer its clients It is something that helps your startup stand out from the competition Unfair advantageIt is a specific advantage of your solution or business model that cannot be replicated by your competitors Customer segmentsBasically it goes about the essential segments of the target market Existing alternativesThis subsection is about the competitors who provide the same or similar to your startup solutions Key metricsIt goes about specific measures that help you understand whether your business is successful In other words these are specific results of numbers that help you evaluate the overall efficiency of your strategy High level conceptThe high level concept lean canvas part is about how you envision your idea on the highest level In other words what do you want your startup to become and how do you want people to perceive it ChannelsThese are the ways you use for promoting and improving your product It goes about the way you choose to communicate with your audience Early adoptersThese are the basic users of your product Usually it goes about people who interact with the earliest version of your product and provide you with much needed feedback Cost structureBasically it goes about costs that the business occurs through its operations These are expenses on its key operations and practices Revenue streamsThese are lean startup strategies that your business uses to generate revenues from each customer segment There s no specified order for all these blocks As the key feature of this approach is flexibility you re free to experiment I personally prefer going from problem to solution but you may thrive with another approach Everything depends on your needs and if you feel unsure about the way you should approach the development of your startup idea don t hesitate to contact us for a relevant piece of advice Why do we need Lean Canvas From my experience I may say that rapid discovery of new demands and business models as well as continuous experimentation are the pillars of the lean startup strategy Anyway in the diversity of its variations a lean canvas brings software founders a great number of benefits These are The ability to be closer to your customerA lean canvas motivates you to keep in contact with your customer After all user feedback is the best way for you to determine further development directions So if you re building something like a campground bookingapp communication with clients helps you to determine its main issues and ways of adapting its functionality to the clients needs You can run on low marketing salesIf you re launching something like a photostock web portal you may start with testing its basic functionality as an MVP app In this case you should collect general user feedback so there s no need to introduce your early product version to the general audience Can you feel it This meets cutting marketing costs at least during the earliest stages So if you run your marketing at a shoestring budget the lean canvas model is a great solution You have more time to take a specific market nicheWhile using the lean startup approach you may start with a general market domain For example you launch a fintech app that works with various documents As you go further with your project you see that there s a great demand forinvoice analysis software As a result you recognize this niche and adapt your product to it down the road So after starting with a generic fintech app idea you end up with a highly demanded AI powered tool for invoice analysis More efficient innovation and flexibilityOne more time let s return to the question of flexibility As you are free to adapt your product to market demands down the road you are free to foster innovation For example you create a SaaS for hospital management with some basic functionality Down the road you see that there s an emerging trend for building AI powered assistants for medical software As you have not yet developed your final product you are free to adapt it to the trends and add the above mentioned assistant The result is simple you come out with a more innovative version of your app Now that you know what is a lean canvas and why it works perfectly for software products tools and apps we re finally ready to learn from some great lean canvas examples out there examples of software businesses that have relied on a lean canvasIn the list below you ll find some popular industry examples as well as a few of our own client projects where we did workshops along with the lean canvas process GoogleOne of the world s greatest digital businesses has achieved remarkable success largely because of some quality implementation of a lean canvas Back then in the world of Internet search tools was dominated by Excite and Yahoo However two ambitious entrepreneurs namely Sergey Brin and Larry Page saw the gaps in these technologies They decided to build their own search engine that will prevail over the worldwide web Flexibility and experiments became the core values allowing them to achieve eventual success This is what a lean canvas online for Google would look like Problem Existing search engines provided irrelevant results to the users of the Internet due to non flexible algorithms Solution A technology that understands content connections and context which allows it to provide more precise search results Unique value Much faster search users can truly find the content they re searching for Unfair advantage Google CitationRanking technology is a completely unique citation ranking tool owned by Google Customer segments Any web users Main competitors Yahoo AltaVista Excite Key metrics The number of search results provided by a search engine How many users end their search on the first page High level concept Fast web search that is finally convenient to the user Channels Feedback was collected from early users and implemented with attention to the smallest detail Word of mouth Cooperation with websites Early adopters Stanford University students Cost structure Hosting development Revenue streams Advertising investment FacebookOne of the world s most popular social media has also been implemented with a lean startup methodology It started as a project of Harvard students These guys created Facemash a website allowing students to rank each other s photos Eduardo Saverin and Mark Zuckerberg quickly recognized the potential of this idea They developed the concept of Facemash into Thefacebook a rough yet very promising social media platform What came next was the development of this idea according to user needs Nowadays Facebook originated from Thefacebook is a media giant with billion active users If we consider Facebook to be one of the lean canvas examples here s how this model would look like Problem Students needed a solution for fun and online communication Solution Social media is where students can communicate share files connect with friends and have fun with each other Unique value The only online communication platform oriented on Harvard students Later it expanded on the students of other colleges Unfair advantage A brand new type of website a unique student network with the most demanded social features developed by students Customer segments Harvard students students of other universities and colleges Existing alternatives There were literally no full fledged competitors The closest concepts were implemented by MySpace Hi and Friendster Key metrics DAU MAU the North Star metric High level concept A web platform that brings college students great socialization opportunities Channels Expanding the network by referring various college students Collecting user feedback through the app and reviewing the key metrics to adapt the product to the needs of the audience Early adopters Harvard university students Cost structure Hosting development payroll Revenue streams Investment advertising revenue YouTubeThe world s largest video hosting can also be viewed as a perfect example of a venture that has thrived due to its founders flexibility The founders of YouTube namely Jawed Karim Steve Chen and Chad Hurley developed their concept in a small flat above the pizzeria What had started in as an idea of a video hosting that would surpass its competitors by being closer to the audience was acquired by Google for billion in Its founders focus on experiments and are ready to adjust their ideas to the market needs These are important factors behind the eventual success of YouTube What does it mean in the context of this article We may view YouTube as a decent lean startup example The lean canvas for this iconic website would probably look this way Problem An evident lack of adequate video hosting websites on the Internet Solution Creating a website where people will be able to upload and share their amateur videos Unique value The ability to watch and share video content on a single platform Star based rating system TV service where content is created and uploaded by ordinary people Unfair advantage Most competitors were unable to recreate a video hosting of such size because of scalability issues Users of the platform constantly attract new users to it because of various social elements of YouTube Customer segments Bloggers advertisers mass market users Existing alternatives Google Video Vimeo ShareYourWorld Key metrics A number of views per video DAU videos per session the overall timing of watched videos etc High level concept To launch a new Flickr but for the world of videos Channels Expanding the network through referrals Continuously collecting the user feedback and researching Internet trends Promotion through technology magazines Direct communication with clients through emails Early adopters College students and teenagers video enthusiasts filmmakers Cost structure Hosting development payroll Revenue streams Advertising revenue investment KidiBoardIt is one of my favorite success stories on this list One of our clients came up with the idea of a social media platform for parents where they can find courses and attractions for their kids as well as share knowledge with other parents The customer was very much into a flexible approach so we suggested them to go with a lean canvas As a result KidiBoard a unique parent centered platform appeared Here is what the lean canvas for this project looks like Problem Hard to find activities for kids in small locations Weak logistics calendar management on the go mobile Hard to find trusted providers online Solution A social media platform for parents that includes a calendar sync and locations not covered by competitors Unique value Combine a marketplace between parents and providers with a social network exclusively for parents Parents get access to full and organized information they can trust and can easily manage childcare related issues with a click of a button Unfair advantage There are founders all of them are parents Customer segments TIM busy professionals moms and dads Tech savvy moms and dads Mid income moms and dads Parents with kids Existing alternatives KidPass comKey metrics Number of users transactions number of social interactions High level concept KidiBoard should become a TripAdvisor for parents Channels FB groups for parents in North New Jersey Word of mouth Parent references Influencer marketing Offline ads Paid online ads Early adopters Parents from a limited area in New Jersey Cost structure Customer acquisition cost distribution costs hosting monthly support AirbnbThis is by far one of the most inspiring startup stories Back in three neighbors namely Brian Chesky Joe Gebbia Nathan Blecharczyk decided to earn some money by sharing spare space in their San Francisco accommodation with the guests of a popular conference hosted in this city In years it was already one of the world s biggest tourist accommodation platforms with funding that exceeds billion Airbnb is a perfect example of how flexibility and a creative approach helped transform a small idea into a giant of the online tourist market Most probably the lean canvas for this startup would look the following way Problem It is very challenging to find cheap and affordable accommodation for tourists Traditional hotels lack authenticity so travelers may have issues with finding truly authentic accommodations in the countries to which they travel People with some extra space or accommodations may not know how to monetize them Home sharing culture is far from being cultivated Solution A web service that allows travelers to find affordable accommodations and helps homeowners to earn extra money by renting out their accommodations on a day by day basis Unique value Travelers can get an authentic living experience at an affordable price Homeowners can earn extra money by renting out their vacant areas Unfair advantage The option of renting out space is available to any homeowner Flexible bi directional rating system for both tourists and homeowners Insurance by default by hosts Customer segments Travelers searching for affordable and authentic accommodations People that are interested in renting out their extra space Existing alternatives Booking com Hotels com Key metrics Number of bookings number of accommodation hosts on the platform NPS DAU MAU High level concept An app that establishes a host sharing economy Channels Interaction with the audience word of mouth online ads referrals Early adopters People that are ready to share their residences and become hosts Cost structure Development hosting payroll marketing insurance photography Revenue streams Fees from travelers AmazonThe world s largest technology giant started in as a book selling website built by Jeff Bezos Later on the entrepreneur took many experiments and adapted to various market trends to develop his idea and grow his startup What we see now is a ponderous company involved in all tech spheres and e commerce domains In the budget of this platform already reached million This means that Bezos did everything right almost from the very start The lean canvas for his startup would probably look like this Problem Lack of online bookstores People had issues with selecting books in traditional bookstores due to the lack of ratings and reviews Solution An online bookstore with billions of titles Unique value An opportunity to buy books from home by using a PC Unfair advantage No premises and fewer employees which means lower prices No competitors in the domain of e commerce Customer segments Book readers book collectors Existing alternatives Interloc future Alibris local booksellers Barnes amp Noble Key metrics CAC user traffic website s ROI High level concept The biggest bookstore in the world Channels Affiliates resellers Early adopters Book enthusiasts searching for rare books Ordinary Internet users that are interested in online bookstores Cost structure Website development hosting operational costs payroll Revenue streams Direct sales AppleIt is one of the oldest picks on this list Apple started as a startup of ambitious technology enthusiasts Steve Jobs and Stever Wozniak in These guys wanted to change the way people interact with computers and came through many experiments As a result by the end of the th century Apple became one of the global tech leaders while in the st century its dominant place in the technology domain is undoubted Surely in nobody implemented a lean canvas methodology However we think that the lean canvas for Apple would look this way Problem Computers were very inconvenient and far from being user friendly Solution A portable computer that provides an excellent user experience Unique value The portable computer of a new generation with good productivity and colorful design Unfair advantage A clear strategy for further releases such as Apple and unique technical background of the startuppers Customer segments Office workers students Existing alternatives IBM Intel Key metrics ROI the number of loyal customers High level concept A personal computer of a new generation Channels Advertising continuous interaction with users direct communication with business owners word of mouth Early adopters Business executives and office workers Cost structure Production payroll development Revenue streams Public sales TeslaTesla emerged in as a response to the failed attempt of General Motors an automotive giant to launch its electric vehicle EV Two ambitious US entrepreneurs Martin Eberhard and Marc Tarpenning came up with the idea of an affordable electric vehicle What had emerged as an ambitious dream soon became one of the world s most well known automotive brands In Eberhard and Tarpnenning started cooperating with one of the most ambitious modern entrepreneurs namely Elon Musk Since that moment Tesla has been experiencing unprecedented growth Its lean canvas model example would most probably look this way Problem The need to promote the idea of eco friendly electric cars available to a broad audience Solution Production of electric vehicles with resource efficient performance Unique value Highly efficient battery electric cars Unfair advantage Roadster an electric vehicle developed by Tesla became the first well branded and recognized electric vehicle in the industry Customer segments Ecology aware drivers sports car lovers Existing alternatives EV by General Motors Key metrics ROI the number of buyers High level concept The world s first high performance electric vehicle in mass production Channels Advertising branding interaction with potential buyers retail stores word of mouth Early adopters Eco aware and wealthy car enthusiasts Cost structure Car production payroll Revenue stream Advertising venture capital direct product sales SkypeOne of the world s most popular video communication tools had also been only an ambitious startup one day In Niklas Zennstrom amp Janus Friis got inspired by the idea of free Internet voice calls By they had already penetrated the market with their idea of a video calling platform Top notch flexibility was the pillar of Skype s success Hence we can imagine one of the lean canvas examples for this startup Problem A lack of user friendly video calling software in the market Solution A video calling platform with social media components and a live chat Unique selling point Cheap phone calls free video calls Unfair advantage Skype was the first platform to implement free video calls and establish a unique connection quality that left all the competitors behind Customer segments Ordinary web users teams of employees Key metrics CAC user traffic website s ROI High level concept One of the world s most popular social media with free video calls and instant text messaging Channels Constant communication with users online ads referral bonuses cooperation with companies using Skype as a professional communication tool Early adopters Ordinary web users representatives of professional working teams Cost structure Development payroll Revenue stream Acquisition investment paid phone calls advertising ZiscuitIt is one more success story we took part in Ziscuit is a unique grocery store that automatically provides you with the cheapest offers related to your desired products What started as one more grocery store became a unique product with the lean startup strategy Ziscuit provides its users with a roadmap for savings and the platform keeps on improving according to market demands I am proud of helping these guys to come up with a lean canvas example for their project And our team helped them implement their idea and launch a successful application that is demanded in the market Here are some basics of this canvas for Ziscuit Problem High prices for groceries Social distancing makes grocery shopping less convenient Buying groceries may be very time consuming Solution Online app for remote shopping with bidding functions A shopping app with scheduling and matching functions An app that provides the users with the most cost efficient purchase suggestions Unique value A user can pick the cheapest groceries on the market with a single click Unfair advantage Tech founders had many contacts and connections among store owners Customer segments Millennials couples people households female baby boomers males aged Existing alternatives Walmart small offline grocery stores Key metrics Avg ticket transaction size active customers ROI customer consistency High level concept An app that becomes a Priceline for groceries Channels Social media paid and organic ads referrals word of mouth radio management service Early adopters Millennials female baby boomers Cost structure Customer acquisition costs projected from eBay auction site c c Distribution Costs customer support outsource Hosting Revenue streams Subscriptions like in the case of Amazon Prime Pay as you go per transaction fee Ads from retailers Cashflow emerging from the growing user base Best books on lean startupThe material in this article will be more than enough to put your software lean canvas quickly But if you want to dive deeper into the topic here we prepared for you further reading The Lean Startup Eric RiesIt is one of the most valuable business texts ever In this work Eric Ries defines his principles of the lean startup methodology This is where the concept that shapes today s digital world begins The book is largely centered around the concept of a minimum viable product MVP It also cherishes the adventurous spirit of experiments in entrepreneurship A great source of both motivation and knowledge on how you should act if you want to embrace the unknown Running Lean Ash MauryaThe author of this book business expert Ash Maurya certainly understands the key principles of the lean methodology And he willingly shares his insights with the reader The author outlines strategies that will help you find your perfect market fit The book is supported with many useful examples of the author s personal experience Besides Ash Maurya advises you to test a variety of hypotheses and explore various customer segments All in all Ash Maurya motivates you to never be afraid of experiments and go for the most ambitious goals The Four Steps to the Epiphany Steve BlankTo my mind it is one of the most influential and practical business publications for startuppers ever It helps you define the differences between startups and existing businesses The book also tells you about the core measures and principles of success in entrepreneurship Such questions as assumption testing customer feedback and engagement and speed product iteration are explored and discussed in detail in this book Wrapping upTo summarize the main purpose of a lean canvas is to let you identify who your customers are what problems they have and how you want to solve them We introduced lean canvas examples and process implications within our discovery workshop for our clients about years ago Since then the success ratio of new startups has risen by almost So I d highly encourage you to build your own lean canvas before writing the first line of code with your developers And if you need some help with it we have something special for you Apply for our unique startup workshop and let s create your business plan together We cannot grant you the success of Google and Amazon but be sure that our startup practices will help you develop a product that thrives in the market	2022-03-30 08:32:24
海外TECH	DEV Community	Laravel cron scheduling and its secrets	https://dev.to/inspector/laravel-cron-scheduling-and-its-secrets-4601	Laravel cron scheduling and its secretsHi I m Valerio Barbera software engineer founder and CTO at Inspector One of the most useful features of Laravel is the cron scheduling system The official documentation clearly explains what it is for In the past you may have written a cron configuration entry for each task you needed to schedule on your server However this can quickly become a pain because your task schedule is no longer in source control and you must SSH into your server to view your existing cron entries or add additional entries Laravel s command scheduler offers a fresh approach to managing scheduled tasks on your server The scheduler allows you to fluently and expressively define your command schedule within your Laravel application itself When using the scheduler only a single cron entry is needed on your server Some common use cases for scheduled tasks Daily Weekly Monthly summary reports Garbage collection Import Export processes Notifying customers of upcoming expirations account credit cards etc I myself have worked a lot with this component of the framework because there are some parts of the Inspector backend system that depend on it Experimenting with the first tasks can give you a lot of happiness but when the number of tasks increase or their internal effort becomes heavy there are some non intuitive behaviors you need to know to avoid big headaches later and to be ready for a sustainable application growth How Laravel scheduling worksLaravel tasks scheduling is designed like a proxy for your cron scheduling list You only need one line on the cron file in your server cd path to your project amp amp php artisan schedule run gt gt dev null gt amp It instructs the cron scheduler to run this artisan command every minute Without going too deep analyzing the implementation of the Illuminate Console Scheduling ScheduleRunCommand class you can see that it iterates events defined in the App Console Kernel class that are ready to be executed in a foreach cycle and runs them with runEvent method foreach this gt schedule gt dueEvents this gt laravel as event this gt runEvent event It is in charge of executing all the commands defined in the App Console Kernel class based on the frequency you have configured for each of them How to schedule a taskImmagine you need to check your blog posts every ten minutes to send an alert if they are unreachable You can schedule this command as shown below namespace App Console use App Console Commands PostsCehckCommand use Illuminate Console Scheduling Schedule use Illuminate Foundation Console Kernel as ConsoleKernel class Kernel extends ConsoleKernel Define the application s command schedule param Illuminate Console Scheduling Schedule schedule return void protected function schedule Schedule schedule schedule gt command PostsCheckCommand class gt everyTenMinutes Laravel scheduler provides a fluent and expressive APIs to help you define the way a command must be run Every time you need to run a task you can add a new line in the scheduler to define what task should be executed and its frequency Parallel executionChecking out the code of the ScheduleRunCommand class by default multiple tasks scheduled at the same time will execute sequentially based on the order they are defined in your schedule method The command uses a foreach cycle to iterate events ready to be executed so if you have long running tasks or the list of tasks is lengthy this may cause subsequent tasks to start much later than anticipated To simulate this scenario I created two commands SleepCommand that contains a seconds sleep Another command that simply write a new log line Then add the appropriate line to the scheduler Define the application s command schedule param Illuminate Console Scheduling Schedule schedule return void protected function schedule Schedule schedule schedule gt command SleepCommand class gt everyMinute schedule gt command AnotherCommand class gt everyMinute Type the command below in your terminal to run the test php artisan schedule runLogs will report the two lines with a seconds interval To prevent tasks from stepping on each other s toes you would like to run tasks in the background so that they may all run simultaneously you may use the runInBackground method Define the application s command schedule param Illuminate Console Scheduling Schedule schedule return void protected function schedule Schedule schedule schedule gt command SleepCommand class gt everyMinute gt runInBackground schedule gt command AnotherCommand class gt everyMinute Logs confirm that the sleep hasn t any effect on the time the second command runs I personally use the runInBackground option in every task by default Don t worry about exceptionsSince the commands are executed in sequential order via the foreach loop you may worry that an exception in one command will stop the entire cycle It is not so fortunately The SchduleRunCommand simply reports exceptions via the Laravel exception handler without breaking the cycle so the next commands in the list can be executed as expected Run the given event param Illuminate Console Scheduling Event event return void protected function runEvent event this gt dispatcher gt dispatch new ScheduledTaskStarting event try event gt run this gt laravel this gt dispatcher gt dispatch new ScheduledTaskFinished event catch Throwable e this gt dispatcher gt dispatch new ScheduledTaskFailed event e this gt handler gt report e VisibilityScheduled tasks are like a hidden part of your application because they run away from the users eyes They are not tied to the user interaction like the code you write in the controllers Their presence pushes you to continuously check the logs even on Saturday and Sunday to be sure that no errors appear If something goes wrong during an HTTP request it will causes red bubbles or messages that inform the user immediately of the problem It s quite easy to discover relevant errors before releasing the software in production using the application yourself If a scheduled command fails he will do it silently without anyone noticing Inspector is designed to remove these concerns from your daily effort monitoring what happens inside your artisan commands with a unique level of visibility Get a monitoring environment specifically designed for software developers avoiding any server or infrastructure configuration Inspector works with a lightweight software library that you can install in your application like any other dependencies based on the technology you are using to develop your backend Checkout the supported technology on our GitHub Visit our website for more details	2022-03-30 08:29:28
海外TECH	DEV Community	How To See The Inverted Treasury Yield Curve With OpenBB Terminal	https://dev.to/danglewood/how-to-see-the-inverted-treasury-yield-curve-with-openbb-terminal-1l92	How To See The Inverted Treasury Yield Curve With OpenBB TerminalInverted yield curves are a hot topic these days here s where to find it in the Terminal Enter the Economy Menu from any menu submenu with the command economyThis is true for navigating the entire directory tree of features For example stocks ta vwap jupyter dashboards correlation There are a few methods to visualize this information For a one day chart across maturities the command is simply yieldFrom any menu this chart can be retrieved by entering economy yield Help dialogue is shown by attaching h To view a specific date in history attach d year mm dd economy yield d To build a time series of one or more maturity use treasury economy treasury h Mar economy treasury sm Maturity options per instrument ┏ー┳ー┓┃Instrument ┃Maturities ┃┡ー╇ー┩│nominal │m m m y y y y y y y y │├ー┼ー┤│inflation │y y y y y │├ー┼ー┤│average │Defined by function │├ー┼ー┤│secondary │w m m y │└ー┴ー┘To draw multiple maturities enter them with a space economy treasury m m y y y yAnother way to view this data and search other series like FEDFUNDS use fred economy fred h Mar economy fred q treasury l Search results for treasury ┏ー┳ー┳ー┓┃Series ID ┃Title ┃Description ┃┡ー╇ー╇ー┩│TYY │ Year Treasury Constant Maturity Minus Year │Starting with the update on June the Treasury bond data used in calculating interest rate │││Treasury Constant Maturity │spreads is obtained directly from the U S Treasury Department ││││center data chart center interest rates Pages TextView aspx data yield Series is calculated as ││││the spread between Year Treasury Constant Maturity BC YEAR and Year Treasury Constant ││││Maturity BC YEAR Both underlying series are published at the U S Treasury Department ││││ ││││rates Pages TextView aspx data yield │├ー┼ー┼ー┤│TYYM │ Year Treasury Constant Maturity Minus Year │Series is calculated as the spread between Year Treasury Constant Maturity BC YEARM and │││Treasury Constant Maturity │ Year Treasury Constant Maturity BC YEARM Starting with the update on June the ││││Treasury bond data used in calculating interest rate spreads is obtained directly from the U S ││││Treasury Department ││││rates Pages TextView aspx data yield │├ー┼ー┼ー┤│DFII │Market Yield on U S Treasury Securities at │For further information regarding treasury constant maturity data please refer to the Board of │││ Year Constant Maturity Inflation Indexed │Governors and the Treasury ││││ │├ー┼ー┼ー┤│FII │Market Yield on U S Treasury Securities at │For further information regarding treasury constant maturity data please refer to │││ Year Constant Maturity Inflation Indexed │ and ││││center data chart center interest rates Pages yieldmethod aspx │├ー┼ー┼ー┤│WFII │Market Yield on U S Treasury Securities at │For further information regarding treasury constant maturity data please refer to │││ Year Constant Maturity Inflation Indexed │ and ││││center data chart center interest rates Pages yieldmethod aspx │├ー┼ー┼ー┤│TYM │ Year Treasury Constant Maturity Minus Month │Series is calculated as the spread between Year Treasury Constant Maturity BC YEAR and │││Treasury Constant Maturity │ Month Treasury Constant Maturity BC MONTH Starting with the update on June the ││││Treasury bond data used in calculating interest rate spreads is obtained directly from the U S ││││Treasury Department ││││rates Pages TextView aspx data yield │├ー┼ー┼ー┤│TYMM │ Year Treasury Constant Maturity Minus Month │Series is calculated as the spread between Year Treasury Constant Maturity BC YEARM and │││Treasury Constant Maturity │ Month Treasury Constant Maturity BC MONTHM Starting with the update on June the ││││Treasury bond data used in calculating interest rate spreads is obtained directly from the U S ││││Treasury Department ││││rates Pages TextView aspx data yield │├ー┼ー┼ー┤│DGS │Market Yield on U S Treasury Securities at │For further information regarding treasury constant maturity data please refer to the H │││ Year Constant Maturity │Statistical Release notes and Treasury ││││Yield Curve Methodology ││││rates Pages yieldmethod aspx │├ー┼ー┼ー┤│GS │Market Yield on U S Treasury Securities at │Averages of business days For further information regarding treasury constant maturity data please │││ Year Constant Maturity │refer to the Board of Governors and the ││││Treasury ││││rates Pages yieldmethod aspx │├ー┼ー┼ー┤│WGSYR │Market Yield on U S Treasury Securities at │Averages of business days For further information regarding treasury constant maturity data please │││ Year Constant Maturity │refer to the Board of Governors and the ││││Treasury ││││rates Pages yieldmethod aspx │├ー┼ー┼ー┤│DFII │Market Yield on U S Treasury Securities at Year │For further information regarding treasury constant maturity data please refer to │││Constant Maturity Inflation Indexed │ and ││││center data chart center interest rates Pages yieldmethod aspx │├ー┼ー┼ー┤│RRPONTSYD │Overnight Reverse Repurchase Agreements Treasury │This series is constructed as the aggregated daily amount value of the RRP transactions reported by │││Securities Sold by the Federal Reserve in the │the New York Fed as part of the Temporary Open Market Operations Temporary open market operations │││Temporary Open Market Operations │involve short term repurchase and reverse repurchase agreements that are designed to temporarily add ││││or drain reserves available to the banking system and influence day to day trading in the federal ││││funds market A reverse repurchase agreement known as reverse repo or RRP is a transaction in ││││which the New York Fed under the authorization and direction of the Federal Open Market Committee ││││sells a security to an eligible counterparty with an agreement to repurchase that same security at a ││││specified price at a specific time in the future For these transactions eligible securities are ││││U S Treasury instruments federal agency debt and the mortgage backed securities issued or fully ││││guaranteed by federal agencies For more information see ││││ │├ー┼ー┼ー┤│FII │Market Yield on U S Treasury Securities at Year │For further information regarding treasury constant maturity data please refer to │││Constant Maturity Inflation Indexed │ and ││││center data chart center interest rates Pages yieldmethod aspx │├ー┼ー┼ー┤│WFII │Market Yield on U S Treasury Securities at Year │For further information regarding treasury constant maturity data please refer to │││Constant Maturity Inflation Indexed │ and ││││center data chart center interest rates Pages yieldmethod aspx │├ー┼ー┼ー┤│TBMS │ Month Treasury Bill Secondary Market Rate │Averages of Business Days Discount Basis │├ー┼ー┼ー┤│DTB │ Month Treasury Bill Secondary Market Rate │Discount Basis │├ー┼ー┼ー┤│WTBMS │ Month Treasury Bill Secondary Market Rate │Averages of Business Days Discount Basis │├ー┼ー┼ー┤│DGS │Market Yield on U S Treasury Securities at Year │For further information regarding treasury constant maturity data please refer to the Board of │││Constant Maturity │Governors and ││││ │├ー┼ー┼ー┤│DGS │Market Yield on U S Treasury Securities at Year │For further information regarding treasury constant maturity data please refer to the Board of │││Constant Maturity │Governors and the Treasury ││││ │├ー┼ー┼ー┤│DGS │Market Yield on U S Treasury Securities at Year │For further information regarding treasury constant maturity data please refer to the Board of │││Constant Maturity │Governors and the Treasury ││││ │└ー┴ー┴ー┘Overnight Reverse Repurchase Agreements temporary operations economy fred p RRPONTSYD s To see todays rates yields and change in yields use overview usbonds Mar economy overview usbonds US Bonds ┏ー┳ー┳ー┳ー┓┃┃Rate ┃Yld ┃Yld Chg ┃┡ー╇ー╇ー╇ー┩│ Year Bond │ │ │ │├ー┼ー┼ー┼ー┤│ Year Note │ │ │ │├ー┼ー┼ー┼ー┤│ Year Note │ │ │ │├ー┼ー┼ー┼ー┤│ Year Note │ │ │ │├ー┼ー┼ー┼ー┤│ Year Note │ │ │ │├ー┼ー┼ー┼ー┤│ Year Note │ │ │ │├ー┼ー┼ー┼ー┤│ Year Bill │ │ │ │├ー┼ー┼ー┼ー┤│ Month Bill │ │ │ │├ー┼ー┼ー┼ー┤│ Month Bill │ │ │ │├ー┼ー┼ー┼ー┤│ Month Bill │ │ │ │└ー┴ー┴ー┴ー┘That s a lot of way to look at these things and that isn t even all the ways to query them fred p FEDFUNDS DGSThanks for reading What are your go to series in the FRED database Visit the OpenBB website and say hello	2022-03-30 08:20:07
海外TECH	DEV Community	Installation of Perl on Mac 2022	https://dev.to/yukikimoto/installation-of-perl-on-mac-2022-31k4	Installation of Perl on Mac Perl is already installed on Mac Execute the Terminal application and input the following command to check the version of Perl This article is originally Installation of Perl on Mac Perl ABCperl vYou can see the version of Perl This is perl version subversion v built for darwin thread multi level with registered patches see perl V for more detail Installation of Perl on your home directoryIf you want to install the specific version of Perl on your home directory you can use perlbrew or plenv according to your taste perlbrew Install Perl on your home directoryplenv Install Perl on your home directory	2022-03-30 08:18:08
海外TECH	DEV Community	Success Story: Know How an Ireland Based Music Store Witnessed 2X Traffic Growth with Magento and Customisation	https://dev.to/web_meridian/success-story-know-how-an-ireland-based-music-store-witnessed-2x-traffic-growth-with-magento-and-customisation-3il7	Success Story Know How an Ireland Based Music Store Witnessed X Traffic Growth with Magento and CustomisationThe article was initially published in WebMeridian blog Clients OverviewEverestmusic is a music and piano centre that has been in existence for over years It has rooted itself as one of the top music platforms across Ireland The company does so by committing itself to making the satisfaction of its customers the top priority Everestmusic is a home for a wide range of quality musical instruments with enticing prices These instruments include the guitar piano and many other musical accessories The store employs a team of musical experts who provide one on one music lessons for both old and young students Migration This is one of the most common challenges our clients face before reaching out to us In almost all cases the Magento migration process is very tedious for the client to handle Migrating from OSCommerce to Magento is an essential but delicate process This is why our team s primary focus is the website migration from OSCommerce to Magento We also focus on the resynchronisation of rd party extensions for the online shop The new release of Magento is more efficient and has a better design to bring the best out of the music store The platform also has improved security faster checkout and has high scalability What You Can Learn From This StoryUpgrading to Magento comes with a lot of improved features Some include the upgraded database and architecture design This provides a user friendly interface for customers to make the site navigation smoother Core Steps in Migrating to Magento ーWe would explain the step by step process needed for Magento migration First you would have to clone your previous version of the Magento store Hence you have to install and set up the new Magento software and also install themes and extensions Note that every code customisation should be rewritten alongside products categories orders settings and so on Hence you integrate with third party modules Then you run a series of tests to be assured of its stability Next you optimise the new store for efficiency Then you can finally go live with your new eCommerce store with the latest Magento version Timeline ーmigrating from Magento to Magento takes about to hours or an average of about months The importation of your previous data takes about to hours Migrating compatible third party extensions only takes a few days about to hours Non compatible extensions usually take longer but still work in the same range Setting up the theme takes a very long time ranging from to hours The customisation of codes usually takes about to hours It depends on the code used in the previous version of the Magento However the installation of Magento can take up to days to complete Testing the new version should take at least a week to complete depending on the taste of the user but the average time for the project is about hours Magento Migration Cost ーthe cost of Magento migration is affected by a lot of factors Two common ones are the choice of opting for the full plan or only a few important features to reduce the cost The main features include UI frontend backend data migration and licensing The price here however varies You are going to spend between to If you head on to our site you will find the full breakdown of the total cost of the Magento migration Products StructureOne of the main aims of upgrading to a newer version of Magento is to provide an improved interface with a solid product structure for customers Another is to also make sure the newer interface is easy to navigate by the customers This is why we import all categories attributes images customers base CMS pages and email templates We do this while keeping the current product structure of the website without data loss A proper product structure is key to an efficient eCommerce store This is because it also makes the interface easy to navigate while being attractive to the user Our Point of ViewA proper product structure is one of the best ways to keep users engaged on the platform It helps them find products faster and it is appealing to the eyes Homepage and Product Page Design Tips A good web page would help you portray your business ideas and also go a long way in gaining visitors attention This is because when anyone logs on to your website it is the job of your web page to help them navigate easily to their desired destination Here are a few tips you should put into consideration when building a web page on your website First you must have a view of what you want your visitors to do when they log on to your website This makes sure you reduce the number of steps that would help them find products and make purchases And also you have to take note of the amount of information they would need to access what they need “Buy Now Magic and Power of One Step Checkout Many online stores are always left with the problems of having high traffic but not enough sales to reciprocate the visiting rate In short they are not converting their clients This is due to a lot of factors but one that stands out is the checkout process According to a study the average cart abandonment rate is about The study also points out that abandoned their cart due to a long checkout process This is why the best solution to this problem is to streamline the checkout process to the shortest it can be Employing tactics like “Buy Now to gain customer attention and a short one step checkout process is necessary here It would boost the conversion rate immensely To do this the user must be able to check out a product with ease This means checkouts that only need a payment method and shipping address Advanced SearchEvery online store visitor would love to be able to navigate through products with ease This made Everestmusic and piano upgrade their search engine on their website It made it easier for their visitors to get to their desired products fast On Everestmusic s online store you can search for your favourite products with ease Here you will also get an autocomplete feature that intuitively guesses what you are looking for You also get a customisable search bar to help you find products faster This feature has played a very crucial role in the development of the organisation s eCommerce store Recipe for Conversion GrowthAdvanced search is a great way to convert the visitors on your store and make more sales It allows them to find the products they need with ease and speed It also gives alternative suggestions in case the product is not in stock at that moment Top Popular custom search features to speed up the search process Now that advanced search is beneficial to conversion rates and the growth of the store On Magento there are many popular custom search features that you can use to enhance your store One is to add custom filters to help users narrow down what they are looking for on your site The filter should include price range product type size brand colour and much more This makes finding products a lot more comfortable Unlike a single search that would bring up a lot of products at a time The Solution ーThe Core Part of Our Companies SynergyAfter smooth website migration to Magento our team embodied a “Buy Now function that enables buyers to proceed to checkout right from “Product page or “Catalogue page Now they can skip the Shopping cart and avoid extra actions The store also retains the original product structure This significantly streamlines the checkout process for the customer It also enables new customers to buy goods without the stress of registration A whole scope of the WebMeridian team s work include Integration with payments systems such as Realex and PayPal as they are the most reliable and popular in Ireland These payment methods are also very secure and protect user data Email marketing service integration Mailchimp to reach out to new and old customers This is done with newsletters for promotions and other important information The software synchronises user information and order data It enables you to create targeted ad campaigns This way you get to generate leads and increase potential revenue Custom design based on the Ultimo theme to give the interface a nice and welcoming appearance while being easy to navigate Multi stock functionality that allows you to monitor the inventory of all your warehouses on one software and all at once Get access to your stock to track update and move them as required Implementation of custom filtering by brands and “featured products to widen the categories of each product Also to provide more efficient product searches This helps new customers find products faster At the same time it recommends better alternatives to what they need Bringing into action a search autocomplete function It works seamlessly with brands product SKUs and product names This helps users to make faster and effective product searches At the same time making the search process comfortable Search customisation enabling search by full input with a dash in the name It helps users find products by using the available search filters The filters separate the products into categories What the Client SaysWhat were your goals for this project New BC eCommerce store to sell music instruments Describe the project in detail WebMeridian provided the team for store development and customization of some features What was the team composition devs project manager DevOps and QA Varies throughout the project timeline Can you share any outcomes from the project that demonstrate progress or success We got a fully functional eCommerce store How effective was the workflow between your team and theirs The project managers were effective They were available at all designated times provided clear milestones and managed the team effectively to meet all the deadlines What did you find most impressive about the WebMeridian team Willingness to adapt to what you need Flexibility They are professional and friendly and despite a language barrier communication was not an issue They showed a high quality of work and met all the deadlines We felt good support from the WebMeridian team The Results Everestmusic Witnessed X Traffic GrowthWe assisted in boosting the shopping experience in the Irish music store by implementing a number of unique features of Magento Nowadays the Everestmusic website is successfully running on Magento and flawlessly operates over unique visitors monthly The platform allows them to search specifically for the products they need thanks to the advanced search features implemented on the site It also allows them to pick up and buy necessary items and accessories in a few clicks thanks to the intuitive user interface and product structure “Good working with the WebMeridian team Understood the project well and added additional expertise that was appreciated Aoife Ryan CEO In a NutshellIt is safe to say that Magento improves the quality of your eCommerce store while implementing a lot of useful and unique features It increases your conversion rate by making the shopping experience of the customers a comfortable and smooth one If you are running your store on an old platform and you want to enjoy the benefits of Magento then reach out to us Our professionals will help you through it all with our all in one service Let s talk Numbers Website smoothly processes K of monthly website visits Bounce rate dropped to It s a peak Magento Version to be precise	2022-03-30 08:14:47
海外TECH	DEV Community	Chapter 5: IAM Policy	https://dev.to/nurulramadhona/chapter-5-iam-policy-cg1	Chapter IAM PolicySince from Chapter I ve mentioned policy What s the best practice to attach policies and more Now we ll discuss about Identity based policy which is consist categories Managed policy and Inline policy Managed policy divided into categories AWS managed policy and customer managed policy AWS managed policy is what already available and customer managed policy is what we pull from the AWS one but we can make it custom based on what we need and push it as the new policy with the new name Inline policy is policy that you attach directly to an identity It s trust relationship When you delete the user the inline policy will go along with it This is not the best practice but here I m just gonna show you that we can do it with ansible For IAM Inline Policy we use community aws iam policy module For IAM Managed Policy we use community aws iam managed policy module Inline PolicyAdd variable to inventory policy new inline user user policy IAMListUsers Roles template lookup template inline policy json j Create json file Version Statement Effect Allow Action iam ListUsers iam ListRoles Resource Task name create inline policy community aws iam policy iam type user iam name item user policy name item policy state present policy json item template loop policy new inline tags iam policy new inlineBefore we run the playbook we need an IAM user to be used I ll create one more along with access key Here are the updated variable and the task user new user user user user user user key name user name user name user name user name user name user ansible playbook i host yml iam yml t iam user keyPLAY iam TASK create user changed localhost gt item daffa TASK create user s key changed localhost gt item name daffa Run the playbook ansible playbook i host yml iam yml t iam policy new inlinePLAY iam TASK create inline policy changed localhost gt item user daffa policy IAMListUsers Roles template Version Statement Effect Allow Action iam ListUsers iam ListRoles Resource Check if the policy works Please setup the new IAM user on the AWS CLI first aws iam list users profile daffa grep UserName UserName aira UserName beny UserName daffa UserName nurul UserName rahman UserName rama aws iam list roles profile daffa grep RoleName RoleName aws ec spot fleet tagging role RoleName AWSServiceRoleForAmazonElasticFileSystem RoleName AWSServiceRoleForSupport RoleName AWSServiceRoleForTrustedAdvisor RoleName ECDemoRole RoleName IAM RoleName IAM Policy aws iam list groups profile daffaAn error occurred AccessDenied when calling the ListGroups operation User arn aws iam user daffa is not authorized to perform iam ListGroups on resource arn aws iam group As we can see user daffa only allowed to list users and roles as mentioned in the inline policy document Managed PolicyAdd variable to inventory policy new managed name IAMGetUser Only policy lookup template managed policy json j Create json file Version Statement Effect Allow Action iam GetUser Resource Task name create managed policy community aws iam managed policy policy name item name policy item policy state present loop policy new managed tags iam policy new managedRun the playbook ansible playbook i host yml iam yml t iam policy new managedPLAY iam TASK create managed policy changed localhost gt item name IAMGetUser Only policy Version Statement Effect Allow Action iam GetUser Resource The task above only create a managed policy To attach it to an IAM group and user I ll use the same task as before I just need to change some variables should be look like this group new members name group members user user name group members user user new policy name user policy arn aws iam aws policy IAMFullAccess name user policy arn aws iam policy IAMGetUser Only group new policy name group policy arn aws iam aws policy IAMReadOnlyAccess name group policy arn aws iam policy IAMGetUser Only Then I ll run existing playbook with multiple tags ansible playbook i host yml iam yml t iam user new policy iam group new policy iam group new members PLAY iam TASK create group and add existing users as members changed localhost gt item name developer members rahman TASK create a user and attach a managed policy changed localhost gt item name beny policy arn aws iam policy IAMGetUser Only TASK create group attach managed policy changed localhost gt item name developer policy arn aws iam policy IAMGetUser Only The task above does attach policy directly to user beny and to group developer and add user beny into it So the user beny and all developer group s members have same policy that s IAMGetUser Only Check if the policy works aws iam get user user name nurul profile beny grep UserName UserName nurul aws iam get user user name nurul profile rahman grep UserName UserName nurul aws iam list users profile benyAn error occurred AccessDenied when calling the ListUsers operation User arn aws iam user beny is not authorized to perform iam ListUsers on resource arn aws iam user aws iam list users profile rahmanAn error occurred AccessDenied when calling the ListUsers operation User arn aws iam user rahman is not authorized to perform iam ListUsers on resource arn aws iam user As we can see the users can do get operation but not for list So we already reached to the end of IAM section In the next chapter we ll delete all the things we just created from Chapter to Chapter It s optional but in case you need it I m gonna show you for it Let s move to the last chapter of this series	2022-03-30 08:13:20
海外TECH	DEV Community	Learn Python Programming	https://dev.to/k_proxima/learn-python-programming-lp	Learn Python ProgrammingYou have to learn the authentically basics of Python syntax before you dive deeper into your opted area You need to spend the minimal measure of time on this as it is n t genuinely motivating Once you ve got the essential syntax it s attainable to start making projects on your own Projects are a good way to learn because they let you pertain your knowledge Keep accelerating the difficulty and scope of your projects However it means it s time to try individual harder If you re fully comfy with what you re structure	2022-03-30 08:10:06
海外TECH	DEV Community	2022’s Trending Products to Sell	https://dev.to/web_meridian/2022s-trending-products-to-sell-2eho	s Trending Products to SellThe article was initially published in WebMeridian blog Are you looking for goods to market in that is on the rise Many goods are in high demand all you have to do is figure out where to look for them To learn more read the tips below In What Are the Hottest Selling Products and Ideas Do you want to sell goods on the internet but don t know where to start Many goods are common and easy to sell on the internet Here are a few pointers to help you figure out what you should be looking for Brainstorm Product Ideas You might need to think long and hard about some of your ideas It assists you in finding the best goods for you What items do you see at the top of your favourite category s list the majority of the time while shopping online Answering this and other questions will help you figure out where you should focus your online sales efforts Get Inspiration from Successful eCommerce Sites To find out what s hot right now browse the catalogues of the top eCommerce stores These shops have a solid reputation and a large following Any patterns you see on their site are likely to be real Collect SEO Data from Well Known Websites Another perfect way to identify trending goods is to collect SEO data The most common terms consumers use when looking for items that they need normally make up the SEO data of top websites You ve discovered the platform s most common goods when you can find terms with higher SEO counts Gather Sales and Volume Data The more popular a product is the higher its search volume or sales on active eCommerce platforms Google Trends Another great way to find out how many people search for a certain word product on the internet is to use Google Trends It examines a word s success across several channels and geographical areas This will show you which products are common and which are not in your field Choose a Product from a Successful Niche As you might have noted specific niches have more successful products than others Some devices for example will always be more common than certain toiletries It s up to you to decide which one is best for you When You re Stuck on the Internet Here s How to Find Trending Products to Sell OnlineIt can be challenging to come up with things to sell online It would be difficult to determine which ones would be in high demand and which would eventually sell Here are some guidelines to follow if you re unsure what to do in this situation Ride the Trends When you re stuck on product ideas use patterns to your advantage Some goods are more common than others People buy more of the commodity than others making it a trend Best selling lists top picks consumer likes and monthly picks all have items like these On sites like eBay Amazon and even Etsy you can find these lists and choices These lists will give you an idea of the goods and you will be able to start selling them right away You can sell a few items based on current events Doormats Kitchen towels Computer parts Toys Car carpets Temporary tattoos Hairstyling equipmentThere are only a few of the things on the list According to a report conducted by Shopify these goods will have the most vital trends in Their place on the other hand has an effect The Use of Healthy MarginYou must keep an eye on your profit margin even though you want to market the best items and make as many sales as possible The money profit you hold after making sales is referred to as your sales margin Make every effort as a company owner to invest in goods that can sell quickly and profitably Many eCommerce companies have a profit margin of or more However doing your own research to determine which product is right for you will be beneficial Reusable packaging Exercise equipment Shapewear Beard oil Minimalist watches Fairy lights Minimalist jewellery These items are inexpensive simple to ship and have a good profit margin When you first start selling goods online it s best if you think about them first Other eCommerce Industry Leaders Will Teach You a Thing or TwoYou will need the expertise and success of other eCommerce leaders regardless of how skilled or experienced you are in your profession You may simply start by looking at the most common items on popular eCommerce platforms According to Amazon s numbers there are a plethora of products that are in high demand Electronics Toys Games Books Clothes Kitchen utensils Jewellery On the other hand you can use the tools at your disposal to rise to the level of successful eCommerce behemoths For example there are books written by successful eCommerce entrepreneurs and business people that can help you learn more about the industry These books will provide you with information that will allow you to outperform your competitors and quickly rise to the top Some books are free while others are reasonably priced It s essential to invest in yourself by learning from others in the field how to sell products online Selling Online a Guide to Getting StartedNow that you ve decided what product you d like to sell online you ll need to follow a few pointers in order to achieve faster performance Use Sales Gateways That Are Already in PlaceYou should use existing sales gateways to reach a broader audience for your goods Amazon eBay and Etsy are the most popular These platforms each have their own set of advantages and disadvantages and it is up to you to determine which is right for you Using these channels for example allows you to reach a larger audience This can be advantageous for many people who are new to selling online However before you start you should think about their commission fees and other costs As a result you are free to use these sites as you please Social Media Platforms for Online SalesWith each passing day selling on social media grows in popularity Platforms like Facebook Twitter and Instagram are quickly gaining traction as cost effective ways to reach a large audience On the bright side some sites allow you to sign up for a business account For example the “Facebook Shop allows you to post your items on Facebook in the same way as you would on an online store You may want to check out Facebook s other business oriented features on your own Using influencers to promote your goods and services is another common aspect of making sales on social media In the vast majority of cases you will not be charged for their services You may send them a preview of your goods in exchange for a mention on their website Drop Shipping Website of Your OwnIt s never been easier to start your own drop shipping store You can check out any of these services for free on many sites available online You can access your inventory on the go by downloading the drop shipping app Drop shipping is a cost effective way to sell online It s a great way for newcomers to gain experience selling goods on the internet Be prepared however to deal with the annoyances of rivalry and consumer expectations Finding a good supplier could be the most difficult problem you face in some cases Just make sure to do some additional research before you start the business Online Selling Marketing StrategyA strong online selling marketing strategy will help you boost your sales and make more profit after considering all of the above points on selling goods online You must realise that there is no such thing as a “one size fits all plan for all business owners And it s for this reason that we ve compiled a list of items to think about as you formulate your own plan What Are You Hoping to Accomplish How much can you expect to make after a month of online sales and how many items will you sell in the beginning Before starting a company you should ask yourself these basic questions It aids in the estimation of inventory and resources as well as the time and effort taken to complete a full sale Before getting into the market make sure you do your homework Some businesses necessitate a greater inventory while others can necessitate a long term sales emphasis Your target will become simpler with these in mind The Margin of Profit Even before you begin your company you should have determined the amount of money you want to make per product How much would it cost to buy a certain amount of the commodity and how much does it cost to sell it at a reasonable price Consider the price your consumers are willing to pay for that type of product as well as the price your competitors may offer This will help you understand what you re up against In almost every industry there are rivals If you want to meet your revenue goals you must be willing to go head to head with your competitors If they aren t in direct competition with you though you can benefit from them as well To make sure you re on the right track look over their list product descriptions website layout and more If they hold conferences go to one to learn something new You d eventually figure out a way to surpass them and establish yourself as a distinct brand Your Customers Financial Capacity The better you know your customers the easier it will be to satisfy their needs One of the quickest ways to make money is by meeting people s needs You can start a company by fulfilling the need until you know what they want and how much they re willing to pay for it Your Exposure The majority of people devote their time to generating revenue They don t think about how potential customers could find them on the internet You want to reach the largest potential audience and there are a number of channels that can help you do so You can write blogs collaborate with social media influencers and even send out email marketing campaigns Your potential customers will increase as long as you are meeting more people You should be able to grasp the online market well enough to build your own plan if you will accept these five points ConclusionOnline product sales can be lucrative but they can also be time consuming It does not cause a high level of expertise but it does necessitate time and business awareness If you need any additional assistance in determining which trending items you may sell please contact us Our experts and consultants are available and we have a one stop shop for all of your requirements Contact us as soon as possible	2022-03-30 08:05:11
海外TECH	DEV Community	Machine Learning: Predicting Heart Disease From Patients' Medical Data	https://dev.to/nicolasvallee/machine-learning-predicting-heart-disease-from-patients-medical-data-535n	Machine Learning Predicting Heart Disease From Patients x Medical DataThis tutorial introduces some fundamental Machine Learning and Data Science concepts by exploring the problem of heart disease classification It is intended to be an end to end example of what a Data Science and Machine Learning proof of concept looks like I completed this milestone project in March as part of the Complete Machine Learning amp Data Science Bootcamp taught by Daniel Bourke and Andrei Neagoie You can also see the final version of this notebook in my GitHub repo What is classification Classification involves deciding whether a sample is part of one class or another single class classification If there are multiple class options we refer to the problem as multi class classification What we ll end up withSince we already have a dataset we ll follow this step Machine Learning modelling framework More specifically we ll look at the following topics Exploratory data analysis EDA the process of going through a dataset to find out more about it Model training create model s to predict a target variable based on other variables Model evaluation evaluating a model s predictions using problem specific evaluation metrics Model comparison comparing several different models to find the best one Model fine tuning once we ve found a good model how can we improve it Feature importance since we re predicting the presence of heart disease are there some things which are more important for prediction Cross validation if we build a good model can we be sure it will work on unseen data Reporting what we ve found if we had to present our work what would we show someone To work through these topics we ll use pandas Matplotlib and NumPy for data anaylsis as well as Scikit Learn for machine learning and modelling tasks We ll work through each step and by the end of the notebook we ll have a handful of models These models can predict whether or not a person has heart disease based on a number of parameters with a considerable accuracy We ll also be able to describe which parameters are more indicative than others for example sex may be more important than age Problem DefinitionThe problem we will explore is a binary classification which means a sample can only be one of two things This is because we re going to use a number of different features about a person to predict whether or not they have heart disease In a statement Given clinical parameters about a patient can we predict whether or not they have heart disease DataHere we want to dive into the data that our problem definition is based on This may involve sourcing defining different parameters talking to experts about it and finding out what we should expect The original data comes from the Cleveland database from UCI Machine Learning Repository Howevever we ve downloaded it in a formatted way from Kaggle The original database contains attributes but here only attributes are used Attributes also called features are the variables that we ll use to predict our target variable Attributes and features are also referred to as independent variables and a target variable can be referred to as a dependent variable We use the independent variables to predict our dependent variable In our case the independent variables are a patient s medical attributes and the dependent variable is whether or not they have heart disease EvaluationThe evaluation metric is something we define at the start of a project Since machine learning is very experimental we might say something like If we can reach accuracy at predicting whether or not a patient has heart disease during the proof of concept phase we ll pursue this project This is helpful because it provides a rough goal for a machine learning engineer or data scientist to work towards However due to the nature of experimentation the evaluation metric may change over time FeaturesFeatures are different parts of the data During this step we want to find out what we can about the data One of the most common ways to do this is to create a data dictionary Heart disease data dictionaryA data dictionary describes the data we re dealing with Not all datasets come with them so this is where we may have to do our research or ask a subject matter expert someone who knows about the data for more information The following are the features we ll use to predict our target variable heart disease or no heart disease age age in yearssex male female cp chest pain type Typical angina chest pain related decrease blood supply to the heart Atypical angina chest pain not related to heart Non anginal pain typically esophageal spasms non heart related Asymptomatic chest pain not showing signs of diseasetrestbps resting blood pressure in mmHg on admission to the hospital anything above is typically cause for concernchol serum cholestoral in mg dlserum LDL HDL triglyceridesabove is cause for concernfbs fasting blood sugar gt mg dl true false gt mg dL signals diabetesrestecg resting electrocardiographic results Nothing to note ST T Wave abnormalitycan range from mild symptoms to severe problemssignals non normal heart beat Possible or definite left ventricular hypertrophyEnlarged heart s main pumping chamberthalach maximum heart rate achievedexang exercise induced angina yes no oldpeak ST depression induced by exercise relative to restlooks at stress of heart during exerciseunhealthy heart will stress moreslope the slope of the peak exercise ST segment Upsloping better heart rate with exercise uncommon Flatsloping minimal change typical healthy heart Downsloping signs of unhealthy heartca number of major vessels colored by fluoroscopycolored vessel means the doctor can see the blood passing throughthe more blood movement the better no clots thal thalium stress result normal fixed defect used to be defect but ok now reversable defect no proper blood movement when exercisingtarget have disease or not yes no the predicted attribute Note No personal identifiable information PPI can be found in the dataset It s a good idea to save these to a Python dictionary or in an external file so we can look at them later without coming back here Preparing the toolsAt the start of any project it s common to see the required libraries imported in a big chunk like we can see below However in practice our projects may import libraries as we go After we ve spent a couple of hours working on our problem we ll probably want to do some tidying up This is where we may want to consolidate every library we ve used at the top of our notebook like in the cell below The libraries we use will differ from project to project But there are a few which will we ll likely take advantage of during almost every structured data project pandas for data analysis NumPy for numerical operations Matplotlib seaborn for plotting or data visualization Scikit Learn for machine learning modelling and evaluation Regular EDA and plotting librariesimport numpy as np import pandas as pd import matplotlib pyplot as pltimport seaborn as sns We want our plots to appear in the notebook matplotlib inline Modelsfrom sklearn linear model import LogisticRegressionfrom sklearn neighbors import KNeighborsClassifierfrom sklearn ensemble import RandomForestClassifier Model evaluatorsfrom sklearn model selection import train test split cross val scorefrom sklearn model selection import RandomizedSearchCV GridSearchCVfrom sklearn metrics import confusion matrix classification reportfrom sklearn metrics import precision score recall score f scorefrom sklearn metrics import plot roc curve Loading dataThere are many ways to store data The typical way of storing tabular data data similar to what you d see in an Excel file is in csv format csv stands for comma separated values Pandas has a built in function to read csv files called read csv which takes the file pathname of our csv file We ll likely use this one often df pd read csv heart disease csv DataFrame shortened to df df shape rows columns Data exploration exploratory data analysis or EDA Once we ve imported a dataset the next step is to explore it There s no set way of doing this but we should try to become more familiar with the dataset Comparing different columns to each other or comparing them to the target variable Referring back to our data dictionary and reminding ourself of what different columns mean Our goal is to become a subject matter expert on the dataset we re working with So if someone asks us a question about it we can give them an explanation and when we start building models we can sound check them to make sure they re not performing too well overfitting or understand why they might be performing poorly underfitting Since EDA has no real set methodology the following is a short check list we might want to walk through What question s are we trying to solve or prove wrong What kind of data do we have and how do we treat different types What s missing from the data and how do we deal with it Where are the outliers and why should we care about them How can we add change or remove features to get more out of our data One of the quickest and easiest ways to check our data is with the head function Calling it on any dataframe will print the top rows and tail calls the bottom We can also pass a number to them like head to show the top rows Let s check the top rows of our dataframedf head age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal target And the bottom df tail age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal target value counts allows us to show how many times each of the values of a categorical column appear Let s see how many positive and negative samples we have in our dataframedf target value counts Name target dtype intSince these two values are close to each other our target column can be considered balanced An unbalanced target column when some classes have far more samples can be harder to model than a balanced set Ideally all of our target classes have the same number of samples If we d prefer these values in percentages value counts takes a parameter normalize which can be set to True Normalized value countsdf target value counts normalize True Name target dtype floatWe can plot the target column value counts by calling the plot function and telling it what kind of plot we d like in this case bar is good Plot the value counts with a bar graphdf target value counts plot kind bar color salmon lightblue df info shows the number of missing values we have and what type of data we re working with In our case there are no missing values and all of our columns are numerical Another way to get some quick insights on our dataframe is to use df describe describe shows a range of different metrics about our numerical columns such as mean max and standard deviation Heart disease frequency according to genderIf we want to compare two columns we can use the function pd crosstab column column This is helpful when we want to gain an intuition about how our independent variables interact with our dependent variables Let s compare our target column with the sex column In our data dictionary for the target column heart disease present no heart disease And for sex male female df sex value counts There are males and females in our study compare target column with sex columnpd crosstab df target df sex sex target What can we infer from this Let s make a simple heuristic Since there are about women and of them have a positive value of heart disease being present we might infer based on this one variable that if the participant is a woman there s a chance she has heart disease As for males there s about total with around half indicating a presence of heart disease So we might predict if the participant is male that of the time he will have heart disease Averaging these two values we can assume based on no other parameter if there s a person there s a chance they have heart disease This can be our very simple baseline and we ll try to beat it with machine learning Making our crosstab visualWe can plot the crosstab by using the plot function and passing it a few parameters such as kind the type of plot we want figsize length width how big we want it to be and color color color the different colors we d like to use Different metrics are best represented with different kinds of plots In our case a bar graph is great We ll see more examples later And with a bit of practice we ll gain an intuition of which plot to use with different variables Create a plotpd crosstab df target df sex plot kind bar figsize color salmon lightblue Add some attributes to itplt title Heart disease frequency for sex plt xlabel No disease Disease plt ylabel Amount plt legend Female Male plt xticks rotation keeps the labels on the x axis vertical Age vs max heart rate for heart diseaseLet s try combining a couple of independent variables such as age and thalach maximum heart rate and then compare them to our target variable Because there are so many different values for age and thalach we ll use a scatter plot Create another figureplt figure figsize Start with positve examplesplt scatter df age df target df thalach df target c salmon Now for negative examples we want them on the same plot so we call plt againplt scatter df age df target df thalach df target c lightblue Add some helpful infoplt title Heart disease in function of Age and Max Heart Rate plt xlabel Age plt ylabel Max Heart Rate plt legend Disease No Disease What can we infer from this It seems the younger someone is the higher their max heart rate dots are higher on the left of the graph and the older someone is the more light blue dots there are But this may be because there are more dots all together on the right side of the graph older participants Both of these are observational of course but this is what we re trying to do get an understanding of the data Now let s check the age distribution Histograms are a great way to check the distribution of a variabledf age plot hist We can see that it s a normal distribution but slightly skewed to the right which is reflected in the scatter plot above Let s keep going Heart disease frequency per chest pain typeLet s try another independent variable This time cp chest pain We ll use the same process as we did before with sex pd crosstab df cp df target arget cp Create a new crosstab and base plotpd crosstab df cp df target plot kind bar figsize color lightblue salmon Add attributes to the plot to make it more readableplt title Heart Disease Frequency per chest pain type plt xlabel Chest Pain Type plt ylabel Amount plt legend No Disease Disease plt xticks rotation What can we infer from this Let s check in our data dictionary what the different levels of chest pain are cp chest pain type Typical angina chest pain related decrease blood supply to the heart Atypical angina chest pain not related to heart Non anginal pain typically esophageal spasms non heart related Asymptomatic chest pain not showing signs of diseaseIt s interesting that the atypical angina value of states that it s not related to the heart but seems to have a higher ratio of participants with heart disease than not But what does atypical angina even means At this point it s important to remember if our data dictionary doesn t supply enough information we may want to do further research on our values This research may come in the form of asking a subject matter expert such as a cardiologist or the person who gave us the data or Googling to find out more According to PubMed it seems even some medical professionals are confused by the term Today years later “atypical chest pain is still popular in medical circles Its meaning however remains unclear A few articles have the term in their title but do not define or discuss it in their text In other articles the term refers to noncardiac causes of chest pain Although not conclusive this graph above is a hint at the confusion of definitions being represented in data Correlation between independent variablesFinally we ll compare all of the independent variables This may give us an idea of which independent variables may or may not have an impact on our target variable We can do this using df corr which will create a correlation matrix for us in other words a big table of numbers telling us how related each variable is to the others Find the correlation between our independent variablescorr matrix df corr Let s make our correlation matrix look a little prettiercorr matrix df corr fig ax plt subplots figsize ax sns heatmap corr matrix annot True linewidths fmt f cmap YlGnBu A higher positive value means a potential positive correlation increase and a higher negative value means a potential negative correlation decrease Enough EDA let s model We ve done exploratory data analysis EDA to start building an intuition about the dataset What have we learned so far Aside from our baseline estimate using sex the rest of the data seems to be pretty distributed So what we ll do next is model driven EDA meaning we ll use machine learning models to drive our next questions A few extra things to remember Not every EDA will look the same what we ve seen here is an example of what we could do for structured tabular dataset We don t necessarily have to do the same plots as we ve done here there are many more ways to visualize data We want to quickly find Distributions df column hist Missing values df info OutliersLet s build some models ModelingWe ve explored the data now we ll try to use Machine Learning to predict our target variable based on the independent variables What is the problem we re solving Given clinical parameters about a patient can we predict whether or not they have heart disease That s what we ll be trying to answer And remember our evaluation metric If we can reach accuracy at predicting whether or not a patient has heart disease during the proof of concept we ll pursue this project That s what we ll be aiming for But before we build a model we have to get our dataset ready Let s look at it again with df head We re trying to predict our target variable using all of the other variables To do this we ll split the target variable from the rest Everything except target variableX df drop target axis Target variabley df target Training and test splitNow comes one of the most important concepts in Machine Learning the training test split This is where we split our data into a training set and a test set We use our training set to train our model and our test set to test it The test set must remain separate from our training set Why not use all the data to train a model Let s say we wanted to take our model into the hospital and start using it on patients How would we know how well our model performs on a new patient not included in the original full dataset we had This is where the test set comes in It s used to mimic taking our model to a real environment as much as possible And it s why it s important to never let our model learn from the test set it should only be evaluated on it To split our data into a training and test set we can use Scikit Learn s train test split and feed it our independent and dependent variables X amp y Random seed for reproducibilitynp random seed Split into train amp test setsX train X test y train y test train test split X y test size percentage of data to use for test setThe test size parameter is used to tell the train test split function how much of our data we want in the test set A rule of thumb is to use of our data to train on and the other to test on For our problem a train and test set are enough But for other problems we could also use a validation train validation test set or cross validation we ll see this later But again each problem will differ The post How and why to create a good validation set by Rachel Thomas is a good place to learn more Let s look at our training data we can see we re using samples to train on Let s look at our test data we ve got examples we ll test our model s on Model choicesNow that we ve got our data prepared we can start to fit models We ll be using the following and comparing their results Logisitc Regression LogisticRegression K Nearest Neighbors Classifier KNeighborsClassifier Random Forest Classifier RandomForestClassifier Why these If we look at the Scikit Learn algorithm cheat sheet we can see that we re working on a classification problem and these are the algorithms that it suggests plus a few more Wait I don t see Logistic Regression and why not use LinearSVC Good questions It is confusing that Logistic Regression isn t listed as well because it s a model for classification Let s pretend that we ve tried LinearSVC and that it doesn t work so now we re following other options in the map For now knowing each of these algorithms inside and out is not essential Machine Learning and Data Science is an iterative practice These algorithms are tools in our toolbox In the beginning on our way to becoming a practitioner it s more important to understand our problem such as classification versus regression and then knowing what tools we can use to solve it Since our dataset is relatively small we can experiment to find which algorithm performs best All of the algorithms in the Scikit Learn library use the same functions for training a model model fit X train y train and for scoring a model model score X test y test score returns the ratio of correct predictions correct Since the algorithms we ve chosen implement the same methods for fitting them to the data as well as evaluating them let s put them in a dictionary and create a function which fits and scores them Put models in a dictionarymodels Logistic Regression LogisticRegression KNN KNeighborsClassifier Random Forest RandomForestClassifier Create a function to fit and score modelsdef fit and score models X train X test y train y test Fits and evaluates given machine learning models models a dict of different Scikit Learn machine learning models X train training data X test testing data y train labels associated with training data y test labels associated with test data Set random seed for reproducible results np random seed Make a list to keep model scores model scores Loop through models for name model in models items Fit the model to the data model fit X train y train Evaluate the model and append its score to model scores model scores name model score X test y test return model scores model scores fit and score models X train X test y train y test model scores Logistic Regression KNN Random Forest Since our models are fitting let s compare them visually Model comparisonSince we ve saved our models scores to a dictionary we can plot them by first converting them to a DataFrame model compare pd DataFrame model scores index accuracy model compare T plot bar We can t really see it from the graph but looking at the dictionary the LogisticRegression model performs best We ve found the best model Now let s put together a classification report to show to the team including a confusion matrix and the cross validated precision recall and F scores We d also want to see which features are most important And look at a ROC curve Let s briefly go through each before we see them in action Hyperparameter tuning Each model we use has a series of dials we can turn to dictate how they perform Changing these values may increase or decrease model performance Feature importance If there are a large amount of features we re using to make predictions do some have more importance than others For example for predicting heart disease which is more important sex or age Confusion matrix Compares the predicted values with the true values in a tabular way if correct all values in the matrix will be top left to bottom right diagonal line Cross validation Splits our dataset into multiple parts to train and test our model on each part then evaluates performance as an average Precision Proportion of true positives over total number of samples Higher precision leads to less false positives Recall Proportion of true positives over total number of true positives and false negatives Higher recall leads to less false negatives F score Combines precision and recall into one metric is best is worst Classification report Sklearn has a built in function called classification report which returns some of the main classification metrics such as precision recall and f score ROC Curve Receiver Operating Characteristic is a plot of true positive rate versus false positive rate Area Under Curve AUC The area underneath the ROC curve A perfect model achieves a score of Hyperparameter tuning and cross validationTo cook our favourite dish we know to set the oven to degrees and turn the grill on But when our roommate cooks their favourite dish they use degrees and the fan forced mode Same oven different settings different outcomes The same can be done for machine learning algorithms We can use the same algorithms but change the settings hyperparameters and get different results But just like turning the oven up too high can burn our food the same can happen for machine learning algorithms We change the settings and it works so well that it overfits the data We re looking for the goldilocks model One which does well on our dataset but also does well on unseen examples To test different hyperparameters we could use a validation set but since we don t have much data we ll use cross validation The most common type of cross validation is k fold It involves splitting our data into k fold s and then testing a model on each For example let s say we have folds k We ll be using this setup to tune the hyperparameters of some of our models and then evaluate them We ll also get a few more metrics like precision recall F score and ROC at the same time Here s the game plan Tune model hyperparameters see which performs bestPerform cross validationPlot ROC curvesMake a confusion matrixGet precision recall and F score metricsFind the most important model features Tune KNeighborsClassifier K Nearest Neighbors or KNN by handThere s one main hyperparameter we can tune for the K Nearest Neighbors KNN algorithm and that is the number of neighbors The default is n neigbors What are neighbors KNN works by assuming that dots which are close to each other belong to the same class If n neighbors then it assumes a dot with the closest dots around it are in the same class Note We re leaving out some details here like what defines close or how distance is calculated For now let s try a few different values of n neighbors Create a list of train scorestrain scores Create a list of test scorestest scores Create a list of different values for n neighborsneighbors range to Setup algorithmknn KNeighborsClassifier Loop through different neighbors valuesfor i in neighbors knn set params n neighbors i set neighbors value Fit the algorithm knn fit X train y train Update the training scores train scores append knn score X train y train Update the test scores test scores append knn score X test y test Let s look at KNN s train scores and test scores train scores test scores These are hard to understand so let s plot them plt plot neighbors train scores label Train score plt plot neighbors test scores label Test score plt xticks np arange plt xlabel Number of neighbors plt ylabel Model score plt legend print f Maximum KNN score on the test data max test scores f Looking at the graph n neighbors seems best Even knowing this the KNN s model performance didn t get near what LogisticRegression or the RandomForestClassifier did Because of this we ll discard KNN and focus on the other two We ve tuned KNN by hand but let s see how we can tune LogisticsRegression and RandomForestClassifier using RandomizedSearchCV Instead of manually trying different hyperparameters by hand RandomizedSearchCV tries a number of different combinations evaluates them and saves the best Tuning models with RandomizedSearchCVReading the Scikit Learn documentation for LogisticRegression we find there s a number of different hyperparameters we can tune The same for RandomForestClassifier Let s create a hyperparameter grid a dictionary of different hyperparameters for each and then test them out Different LogisticRegression hyperparameterslog reg grid C np logspace solver liblinear Different RandomForestClassifier hyperparametersrf grid n estimators np arange max depth None min samples split np arange min samples leaf np arange Now let s use RandomizedSearchCV to tune our LogisticRegression model We ll pass it the different hyperparameters from log reg grid as well as set n iter This means RandomizedSearchCV will try different combinations of hyperparameters from log reg grid and save the best ones Setup random seednp random seed Setup random hyperparameter search for LogisticRegressionrs log reg RandomizedSearchCV LogisticRegression param distributions log reg grid cv n iter verbose True Fit random hyperparameter search modelrs log reg fit X train y train Fitting folds for each of candidates totalling fitsRandomizedSearchCV cv estimator LogisticRegression n iter param distributions C array e e e e e e e e e e e e e e e e e e e e solver liblinear verbose True rs log reg best params solver liblinear C rs log reg score X test y test Now that we ve tuned LogisticRegression using RandomizedSearchCV we ll do the same for RandomForestClassifier Setup random seednp random seed Setup random hyperparameter search for RandomForestClassifierrs rf RandomizedSearchCV RandomForestClassifier param distributions rf grid cv n iter verbose True Fit random hyperparameter search modelrs rf fit X train y train Fitting folds for each of candidates totalling fitsRandomizedSearchCV cv estimator RandomForestClassifier n iter param distributions max depth None min samples leaf array min samples split array n estimators array verbose True Find the best hyperparametersrs rf best params n estimators min samples split min samples leaf max depth Evaluate the randomized search RFC modelrs rf score X test y test Tuning the hyperparameters for each model saw a slight performance boost in both RandomForestClassifier and LogisticRegression This is akin to tuning the settings on our oven and getting it to cook our favourite dish just right But since LogisticRegression is ahead we ll try tuning it further with GridSearchCV Tuning a model with GridSearchCVThe difference between RandomizedSearchCV and GridSearchCV is that RandomizedSearchCV searches over a grid of hyperparameters performing n iter combinations but GridSearchCV will test every single possible combination In short RandomizedSearchCV tries n iter combinations of hyperparameters and saves the best GridSearchCV tries every single combination of hyperparameters and saves the best Let s see it in action Different LogisticRegression hyperparameterslog reg grid C np logspace solver liblinear Setup grid hyperparameter search for LogisticRegressiongs log reg GridSearchCV LogisticRegression param grid log reg grid cv verbose True Fit grid hyperparameter search modelgs log reg fit X train y train Check the best parametersgs log reg best params C solver liblinear Evaluate the modelgs log reg score X test y test In this case we get the same results as before since our grid only has a maximum of different hyperparameter combinations Note If there are a large amount of hyperparameters combinations in our grid GridSearchCV may take a long time to try them all out This is why it s a good idea to start with RandomizedSearchCV try a certain amount of combinations and then use GridSearchCV to refine them Evaluating a classification model beyond accuracyNow that we ve got a tuned model let s get some of the metrics we discussed before We want ROC curve and AUC score plot roc curve Confusion matrix confusion matrix Classification report classification report Precision precision score Recall recall score F score f score Luckily Scikit Learn has these all built in To access them we ll have to use our model to make predictions on the test set We can make predictions by calling predict on a trained model and passing it the data we d like to predict on We ll make predictions on the test data Make predictions on test datay preds gs log reg predict X test Let s see them Since we ve got our prediction values we can find the metrics we want Let s start with the ROC curve and AUC scores ROC curve and AUC scoresWhat s a ROC curve It s a way of understanding how our model is performing by comparing the true positive rate to the false positive rate In our case To get an appropriate example in a real world problem consider a diagnostic test that seeks to determine whether a person has a certain disease A false positive in this case occurs when the person tests positive but does not actually have the disease A false negative on the other hand occurs when the person tests negative suggesting they are healthy when they actually do have the disease Scikit Learn implements a function plot roc curve which can help us create a ROC curve as well as calculate the area under the curve AUC metric Reading the documentation on the plot roc curve function we can see it takes estimator X y as inputs Where estimator is a fitted machine learning model and X and y are the data we d like to test it on In our case we ll use the GridSearchCV version of our LogisticRegression estimator gs log reg as well as the test data X test and y test Plot ROC curve and calculate AUC metricplot roc curve gs log reg X test y test Our model does far better than guessing which would be a line going from the bottom left corner to the top right corner AUC But a perfect model would achieve an AUC score of so there s still room for improvement Let s move onto the next evaluation request a confusion matrix Confusion matrixA confusion matrix is a visual way to show where our model made the right predictions and where it made the wrong predictions or in other words got confused Scikit Learn allows us to create a confusion matrix using confusion matrix and passing it the true labels and predicted labels Because Scikit Learn s built in confusion matrix is a bit bland we probably want to make it visual Let s create a function which uses Seaborn s heatmap for doing so sns set font scale Increase font sizedef plot conf mat y test Y preds Plots a confusion matrix using Seaborn s heatmap fig ax plt subplots figsize ax sns heatmap confusion matrix y test y preds annot True Annotate the boxes cbar False plt xlabel Predicted label plt ylabel True label plot conf mat y test y preds We can see the model gets confused predicts the wrong label relatively the same across both classes In essence there are occasions where the model predicted when it should have been false negative and occasions where the model predicted instead of false positive Classification reportWe can make a classification report using classification report and passing it the true labels as well as our models predicted labels A classification report will also give us information of the precision and recall of our model for each class Show classification reportprint classification report y test y preds precision recall f score support accuracy macro avg weighted avg What s going on here Let s refresh our memory Precision Indicates the proportion of positive identifications model predicted class which were actually correct A model which produces no false positives has a precision of Recall Indicates the proportion of actual positives which were correctly classified A model which produces no false negatives has a recall of F score A combination of precision and recall A perfect model achieves an F score of Support The number of samples each metric was calculated on Accuracy The accuracy of the model in decimal form Perfect accuracy is equal to Macro avg Short for macro average the average precision recall and F score between classes Macro avg doesn t class imbalance into effort so if you do have class imbalances pay attention to this metric Weighted avg Short for weighted average the weighted average precision recall and F score between classes Weighted means each metric is calculated with respect to how many samples there are in each class This metric will favour the majority class e g will give a high value when one class out performs another due to having more samples Ok now we ve got a few deeper insights on our model But these were all calculated using a single training and test set What we ll do to make them more solid is calculate them using cross validation How We ll take the best model along with the best hyperparameters and use cross val score along with various scoring parameter values cross val score works by taking an estimator machine learning model along with data and labels It then evaluates the machine learning model on the data and labels using cross validation and a defined scoring parameter Let s remind ourselves of the best hyperparameters and then see them in action Instantiate best model with best hyperparameters found with GridSearchCV clf LogisticRegression C solver liblinear Now that we ve got an instantiated classifier let s find some cross validated metrics Cross validate accuracy scorecv acc cross val score clf X y cv fold cross validation scoring accuracy cv acc np mean cv acc since there are metrics here we ll take the averagecv accNow we ll do the same for other classification metrics Cross validated precision scorecv precision cross val score clf X y cv scoring precision cv precision np mean cv precision cv precision Cross validated recall scorecv recall cross val score clf X y cv scoring recall cv recall np mean cv recall cv recall Cross validated F scorecv f cross val score clf X y cv scoring f cv f np mean cv f cv fWe ve got cross validated metrics Let s visualize them Visualizing cross validated metricscv metrics pd DataFrame Accuracy cv acc Precision cv precision Recall cv recall F cv f index cv metrics T plot bar title Cross Validated Classification Metrics legend False The final thing to check off the list of our model evaluation techniques is feature importance Feature importanceFeature importance is another way of asking which features contribute most to the outcomes of the model Or for our problem trying to predict heart disease using a patient s medical characteristics which characteristics contribute most to a model predicting whether someone has heart disease or not Unlike some of the other functions we ve seen because how each model finds patterns in data is slightly different how a model judges how important those patterns are is different as well This means for each model there s a slightly different way of finding which features were most important We can usually find an example via the Scikit Learn documentation or via searching for something like MODEL TYPE feature importance such as random forest feature importance Since we re using LogisticRegression we ll look at one way we can calculate feature importance for it To do so we ll use the coef attribute Looking at the Scikit Learn documentation for LogisticRegression the coef attribute is the coefficient of the features in the decision function We can access the coef attribute after we ve fit an instance of LogisticRegression Fit an instance of LogisticRegression taken from above clf fit X train y train Check coef clf coef array Looking at this it might not make much sense But these values are how much each feature contributes to how a model makes a decision on whether patterns in a sample of patient s health data leans more towards having heart disease or not Even knowing this in its current form this coef array still doesn t mean much But it will if we combine it with the columns features of our dataframe Match features to columnsfeature dict dict zip df columns list clf coef feature dict age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal Now let s visualize them Visualize feature importancefeature df pd DataFrame feature dict index feature df T plot bar title Feature Importance legend False We notice some are negative and some are positive The larger the value bigger bar the more the feature contributes to the model s decision If the value is negative it means there s a negative correlation And vice versa for positive values For example the sex attribute has a negative value of which means as the value for sex increases the target value decreases We can see this by comparing the sex column to the target column pd crosstab df sex df target arget sex We can see when sex is female there are almost times as many vs people with heart disease target than without And then as sex increases to male the ratio goes down to almost to vs of people who have heart disease and who don t What does this mean It means the model has found a pattern which reflects the data Looking at these figures and this specific dataset it seems if the patient is female they re more likely to have heart disease How about a positive correlation Contrast slope positive coefficient with targetpd crosstab df slope df target arget slope Looking back at the data dictionary we see slope is the slope of the peak exercise ST segment where Upsloping better heart rate with excercise uncommon Flatsloping minimal change typical healthy heart Downslopins signs of unhealthy heartAccording to the model there s a positive correlation of not as strong as sex but still more than This positive correlation means our model is picking up the pattern that as slope increases so does the target value What can we do with this information This is something we might want to talk to a subject matter expert about They may be interested in seeing where machine learning model is finding the most patterns highest correlation as well as where it s not lowest correlation Doing this has a few benefits Finding out more If some of the correlations and feature importances are confusing a subject matter expert may be able to shed some light on the situation and help us figure out more Redirecting efforts If some features offer far more value than others this may change how we collect data for different problems See point Less but better Similar to above if some features are offering far more value than others we could reduce the number of features our model tries to find patterns in as well as improve the ones which offer the most This could potentially lead to saving on computation by having a model find patterns across less features whilst still achieving the same performance levels ExperimentationWe ve completed all the metrics requested We should be able to put together a great report containing a confusion matrix a handful of cross validated metrics such as precision recall and F as well as which features contribute most to the model making a decision But after all this we might be wondering where step in the framework is experimentation The whole thing is experimentation From trying different models to tuning different models to figuring out which hyperparameters were best What we ve worked through so far has been a series of experiments And we could keep going But of course things can t go on forever So by this stage after trying a few different things we d ask ourselves did we meet the evaluation metric We defined one in step If we can reach accuracy at predicting whether or not a patient has heart disease during the proof of concept we ll pursue this project In this case we didn t The highest accuracy our model achieved was below What s next What happens when the evaluation metric doesn t get hit Is everything we ve done wasted No It means we know what doesn t work In this case we know the current model we re using a tuned version of LogisticRegression along with our specific data set doesn t hit the target we set ourselves This is where step comes into its own A good next step would be to discuss with our team or research on our own different options for going forward Could we collect more data Could we try a better model If we re working with structured data we might want to look into CatBoost or XGBoost Could we improve the current models beyond what we ve done so far If our model is good enough how would we export it and share it with others Hint check out Scikit Learn s documentation on model persistance The key here is to remember our biggest restriction will be time Hence why it s paramount to minimise delay between experiments The more we try the more we figure out what doesn t work the more we ll start to get a hang of what does	2022-03-30 08:04:41
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