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  • xbox sdkKevin Parrish 凯文·帕里什 Xbox Game Pass is a great way to play over 100 games for a monthly fee. If the library gets boring and/or you want to drop the service, this guide shows you how to ...
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    How to Cancel Xbox Game Pass
    Kevin Parrish
    凯文·帕里什

    Xbox Game Pass is a great way to play over 100 games for a monthly fee. If the library gets boring and/or you want to drop the service, this guide shows you how to cancel an Xbox Game Pass subscription.

    Xbox Game Pass是每月付费玩100多种游戏的好方法。 如果库变得无聊和/或您要删除该服务,此指南将向您显示如何取消Xbox Game Pass订阅。

    Microsoft introduced Xbox Game Pass in June 2017. For $9.99 per month, Xbox owners had access to a rotating library of more than 100 games. The service also provides discounts on these games if you want to keep them in your digital library. Moreover, users had access to the Xbox Play Anywhere games on a Windows PC.

    微软于2017年6月推出了Xbox GamePass。Xbox所有者每月只需支付9.99美元,就可以访问包含100多个游戏的旋转库。 如果您想将这些游戏保留在数字图书馆中,则该服务还提供折扣。 此外,用户可以访问Windows PC上的Xbox Play Anywhere游戏。

    Two years later, Microsoft introduced a similar, stand-alone, all-you-can-eat service for Windows 10, costing $9.99 per month. The company also launched Xbox Game Pass Ultimate, which combines both along with Xbox Live Gold for $14.99 per month.

    两年后,微软推出了类似的,独立的,随便吃的Windows 10服务,每月收费9.99美元。 该公司还推出了Xbox Game Pass Ultimate,将二者与Xbox Live Gold结合在一起,每月收费14.99美元。

    That said, this guide shows you how to cancel these subscriptions. Ultimately, it’s all done through Microsoft’s website, but you can cancel Xbox Game Pass, Xbox Game Pass Ultimate, and Xbox Live Gold on the console without accessing a PC. Cancelling Xbox Game Pass for PC requires a computer.

    也就是说,本指南向您展示了如何取消这些订阅。 最终,所有这些都可以通过Microsoft网站完成,但是您可以在不访问PC的情况下在控制台上取消Xbox Game Pass,Xbox Game Pass Ultimate和Xbox Live Gold。 取消PC的Xbox Game Pass需要一台计算机。

    使用PC取消Xbox Game Pass订阅 (Cancel Your Xbox Game Pass Subscription Using a PC)

    First, open any browser and navigate to your Microsoft account’s Services & Subscriptions page. Log in to your account if needed.

    首先,打开任何浏览器,然后导航到您的Microsoft帐户的“服务和订阅”页面 。 如果需要,登录到您的帐户。

    Next, locate your Xbox Game Pass subscription and click the “Manage” link under the Xbox logo.

    接下来,找到您的Xbox Game Pass订阅,然后单击Xbox徽标下的“管理”链接。

    Manage Xbox Game Pass on PC

    In our example, we’re canceling Xbox Game Pass Ultimate. Again, if you don’t have this specific plan, you’ll see Xbox Game Pass and/or Xbox Game Pass for PC on the list. You’ll see the Xbox Live Gold subscription as well.

    在我们的示例中,我们取消了Xbox Game Pass Ultimate。 同样,如果您没有此特定计划,您会在列表中看到Xbox Game Pass和/或PC的Xbox Game Pass。 您还将看到Xbox Live Gold订阅。

    On the following page, click the “Cancel” link.

    在下一页上,单击“取消”链接。

    Cancel Xbox Game Pass on PC

    使用Xbox取消Xbox游戏通行证订阅 (Cancel Your Xbox Game Pass Subscription Using an Xbox)

    On the Xbox home screen, open the guide by using your controller to highlight your profile icon and then pressing the “A” button. Next, navigate through the menu’s tabs and select the gear icon. This loads the “System” tab.

    在Xbox主屏幕上,通过使用控制器突出显示您的配置文件图标,然后按“ A”按钮,打开指南。 接下来,浏览菜单中的标签并选择齿轮图标。 这将加载“系统”选项卡。

    Navigate down, highlight “Settings,” and then press the “A” button.

    向下导航,突出显示“设置”,然后按“ A”按钮。

    Xbox Open System Settings

    On the following screen, highlight “Account” and then navigate to the right to select “Subscriptions.” Press the “A” button to proceed.

    在以下屏幕上,突出显示“帐户”,然后导航到右侧以选择“订阅”。 按下“ A”按钮继续。

    Xbox Account Access Subscriptions

    Highlight your subscription and press the “A” button to continue.

    突出显示您的订阅,然后按“ A”按钮继续。

    Xbox Select Subscription

    Under “Payment and Billing,” highlight “View and Manage Subscription” and then press the “A” button.

    在“付款和帐单”下,突出显示“查看和管理订阅”,然后按“ A”按钮。

    Xbox View and Manage Subscription

    Microsoft Edge loads your account on Microsoft.com. Use your controller to find the subscription you want to cancel. Highlight the “Manage” link under the Xbox logo and then press the “A” button on your controller.

    Microsoft Edge在Microsoft.com上加载您的帐户。 使用控制器查找要取消的订阅。 突出显示Xbox徽标下的“管理”链接,然后按控制器上的“ A”按钮。

    Manage Xbox Game Pass Console

    Use the controller to move the onscreen cursor and highlight “Cancel.” Press the “A” button to finish.

    使用控制器移动屏幕上的光标并突出显示“取消”。 按“ A”按钮完成。

    Cancel Xbox Game Pass Console

    翻译自: https://www.howtogeek.com/525398/how-to-cancel-your-xbox-game-pass-subscription/

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  • xbox可以录视频声音吗Microsoft’sXbox Game Pass promises access to over 100 games for a $10 per month subscription fee.Microsoft wants Xbox Game Pass to be the “Netflix of video games”—but is it ...
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    Microsoft’s Xbox Game Pass promises access to over 100 games for a $10 per month subscription fee. Microsoft wants Xbox Game Pass to be the “Netflix of video games”—but is it really worth it?

    微软的Xbox Game Pass承诺以每月10美元的订阅费访问100多种游戏。 微软希望Xbox Game Pass成为“视频游戏的Netflix”,但真的值得吗?

    Update: We originally reviewed Xbox Game Pass in 2017 and found it a little lacking. But, at the end of 2019, Xbox Game Pass now offers a more compelling library of games to play. Check out the current games list. Some new games, like The Outer Worlds, have even been added to Xbox Game Pass at release. Beyond that, Xbox Game Pass is now available for Windows 10 PCs, although the library of games available on PC is different.

    更新:我们最初在2017年回顾了Xbox Game Pass,发现有点不足。 但是,到2019年底,Xbox Game Pass现在提供了一个更具吸引力的游戏库。 查看当前游戏列表。 发行时,Xbox Game Pass还添加了一些新游戏,例如The Outer Worlds 。 除此之外,尽管PC上可用的游戏库有所不同,但Xbox Game Pass现在可用于Windows 10 PC

    什么是Xbox Game Pass? (What Is Xbox Game Pass?)

    Xbox Game Pass gives you unlimited access to a game library for one monthly fee. Rather than paying for each game you want to play, you pay $10 per month for unlimited access to a catalog of games. You can play these games all you like. There’s also a fourteen-day free trial to get you started.

    Xbox Game Pass可让您无限制地访问游戏库,而每月需付费一次。 您无需每月为要玩的游戏付费,而是每月支付10美元,即可无限制地访问游戏目录。 您可以随心所欲地玩这些游戏。 还有一个为期14天的免费试用版,可以帮助您入门。

    Unlike Sony’s PlayStation Now, which streams games over the Internet, Xbox Game Pass isn’t doing anything too unconventional. Paying the subscription fee allows you to download games to your Xbox One and play them like you would any other game you purchased from the Xbox Store.

    与通过互联网流式传输游戏的索尼PlayStation Now有所不同,Xbox Game Pass所做的事情并不过分传统。 支付订阅费后,您便可以将游戏下载到Xbox One并像从Xbox商店购买的任何其他游戏一样进行游戏。

    This service requires an Xbox One. Microsoft may one day extend Xbox Game Pass to Windows 10 PCs, but that hasn’t happened yet. While it does incorporate Xbox 360 games, those games can only be played in backwards compatibility mode on an Xbox One—not on an Xbox 360.

    此服务需要Xbox One。 微软可能有一天将Xbox Game Pass扩展到Windows 10 PC,但这还没有发生。 虽然它确实包含Xbox 360游戏,但是这些游戏只能在Xbox One上以向后兼容模式进行播放,而不能在Xbox 360上进行。

    Note that Xbox Game Pass is separate from Xbox Live Gold, Microsoft’s subscription service that enables online multiplayer gameplay, allows access to game deals, and offers free games every month. You can use Xbox Game Pass without paying for Xbox Live Gold. However, if a game available via Xbox Game Pass has online multiplayer features, you can only play multiplayer if you’re also paying for Xbox Live Gold.

    请注意,Xbox Game Pass与Microsoft的订阅服务Xbox Live Gold是分开的,它可以启用在线多人游戏,允许访问游戏交易,并每月提供免费游戏。 您可以使用Xbox Game Pass,而无需支付Xbox Live Gold。 但是,如果通过Xbox Game Pass提供的游戏具有在线多人游戏功能,则只有在您还支付Xbox Live Gold费用的情况下,您才可以玩多人游戏。

    有多少种游戏可用? (How Many Games Are Available?)

    So Xbox Game Pass is pretty simple: For $10 per month, you get access to a catalog of games and you can download and play them all you want on your Xbox One.

    因此,Xbox Game Pass非常简单:每月10美元,您就可以访问游戏目录,然后可以在Xbox One上下载并播放所有想要的游戏。

    What makes or breaks a service like this is the selection of games. As of July 12, 2017, there are 119 games available in the catalog. They aren’t all Xbox One games. In fact, the majority of them are Xbox 360 games that you can play on your Xbox One through the backwards compatibility feature.

    决定服务成败的是游戏的选择。 截至2017年7月12日,目录中提供119种游戏。 它们并非全部都是Xbox One游戏。 实际上,其中大多数是Xbox 360游戏,您可以通过向后兼容功能在Xbox One上进行游戏。

    You will see some big-name games here. You’ll find five games from the Gears of War series, Halo 5: Guardians, all three BioShock games, NBA 2K16, Saints Row IV: Re-Elected, and more. But the list is rounded out with older Xbox 360 games and smaller indie games. That doesn’t mean they’re bad, but you certainly aren’t getting all the latest full-price Xbox One games. You can view the full list of Xbox Game Pass games on Microsoft’s website. Microsoft adds new games every month.

    您会在这里看到一些大牌游戏。 您会发现《战争机器》系列中的五款游戏,《光晕5:守护者》 ,全部三本《生化奇兵》游戏,《 NBA 2K16》 ,《圣徒街IV:重新当选》等等。 但是,该列表在较旧的Xbox 360游戏和较小的独立游戏中更为完整。 这并不意味着它们很糟糕,但是您肯定不会获得所有最新的全价Xbox One游戏。 您可以在Microsoft网站上查看Xbox Game Pass游戏的完整列表。 微软每个月都会增加新游戏。

    记住,您必须先下载它们 (Remember, You’ll Have to Download Them First)

    There’s one major way this experience doesn’t compare to Netflix or even Sony’s PlayStation Now. While both Netflix and PlayStation Now allow you to start streaming a video or game immediately, Xbox Game Pass requires you download a game to your Xbox One before you play it.

    与Netflix甚至索尼的PlayStation Now相比,这种体验有一种主要的方法。 尽管Netflix和PlayStation Now现在都允许您立即开始流式传输视频或游戏,但Xbox Game Pass要求您在播放之前将游戏下载到Xbox One。

    For example, BioShock Infinite, which is 13 GB in size, took nearly an hour to download on our fairly speedy Internet connection.

    例如,大小为13 GB的BioShock Infinite花费了将近一个小时的时间才能通过我们相当快速的Internet连接进行下载。

    If you plan on sticking with a game, that’s good news. You’ll have better performance with the game installed on your Xbox One. But you can’t just sit down and flip through a few games, trying each for a few minutes and seeing what you like. You’ll need to download each game in full before you play it.

    如果您打算坚持玩游戏,那将是个好消息。 Xbox One上安装的游戏将为您带来更好的性能。 但是,您不能只是坐下来翻阅一些游戏,尝试每个游戏几分钟,然后看看自己喜欢什么。 在玩游戏之前,您需要完整下载每个游戏。

    That’s fine, really—it’s the standard experience with Xbox One games, after all. Just don’t expect anything else. How long you have to wait before playing a game will depend on the speed of your Internet connection.

    真的,那很好-毕竟,这是Xbox One游戏的标准体验。 只是别指望。 您必须等待多长时间才能玩游戏,这取决于您的Internet连接速度。

    Once a game is installed, you can play it all you like. If your Xbox Game Pass subscription expires, the game will remain installed, but you won’t be able to play it until you either resubscribe or purchase the game. You don’t get to keep games you download via Xbox Game Pass—you lose access to them when your subscription stops.

    安装游戏后,您可以随心所欲地玩游戏。 如果您的Xbox Game Pass订阅到期,则该游戏将保持安装状态,但是您必须重新订阅或购买游戏才能玩它。 您不必保留通过Xbox Game Pass下载的游戏-当订阅停止时,您将无法访问它们。

    那么,值得吗? (So, Is It Worth It?)

    Whether Xbox Game Pass is worth it is a tough question. If you have a lot of time and want to play a lot of games, you’ll get a month of access to over 100 games for $10, and that’s quite the deal.

    Xbox Game Pass是否值得,这是一个棘手的问题。 如果您有很多时间并且想玩很多游戏,那么您一个月就能以10美元的价格使用100多个游戏,这很划算。

    The limiting factor, of course, is time. How many of those games do you really want to play, and how fast will you play them?

    限制因素当然是时间。 您真的想玩多少个这些游戏,您将玩多快?

    The real issue here is that Xbox Game Pass largely gives you access to older games. You can often pick up used copies of these older games for very low prices, so that $10 a month may not be quite as awesome as it seems.

    真正的问题是Xbox Game Pass在很大程度上允许您访问较旧的游戏。 您通常可以很低的价格买到这些旧游戏的二手副本,这样每月10美元的费用似乎就不那么令人敬畏了。

    Let’s say you’re interested in the BioShock series, all three games of which are available on the $10 per month Xbox Game Pass. A quick glance at eBay reveals you can currently buy the Xbox 360 version of BioShock Infinite for $4.59 with free shipping on eBay. So, if finishing the game will take you a few weeks, it’s less expensive to simply buy a used copy and play it at your leisure. On the other hand, if you plan on tearing through BioShock Infinite in just a few days and moving on to a new game, Xbox Game Pass looks like a great value.

    假设您对《生化奇兵》系列感兴趣,可以在每月10美元的Xbox Game Pass上获得所有这三款游戏。 快速浏览一下eBay,即可了解到您目前可以以4.59美元的价格购买Xbox 360版本的BioShock Infinite,并在eBay上免费送货。 因此,如果完成游戏将花费您数周时间,那么只需购买二手副本并在闲暇时玩就可以省钱了。 另一方面,如果您打算在短短几天内突破BioShock Infinite并继续开发新游戏,则Xbox Game Pass看起来很有价值。

    Be sure to consider which games you actually want to play and decide whether it’s a better deal to buy them separately, considering how much time you have for games. Personally, considering how much time I have for games these days, I know Xbox Game Pass is a worse deal than simply buying the games I want to play. I’m not sold, myself.

    确保考虑自己有多少时间玩游戏,并考虑单独购买它们是否是更好的选择。 就个人而言,考虑到这些天我有多少游戏时间,我知道Xbox Game Pass比单纯购买我想玩的游戏更糟糕。 我自己没有被卖掉。

    But you can try Xbox Game Pass for yourself thanks to that fourteen day free trial. If nothing else, it’ll let you play some games for free for two weeks.

    但是由于十四天的免费试用期,您可以自己尝试Xbox Game Pass。 如果没有别的,它会让您免费玩两个星期的游戏。

    Just be sure to cancel the free trial before the fourteen days are up if you don’t want to keep it. If you forget, Microsoft will begin charging you $10 per month until you remember to cancel. You can view and cancel this subscription on your Microsoft account’s Services & subscriptions page, if you like.

    如果您不想保留免费试用,请务必在十四天之内取消免费试用。 如果您忘记了,Microsoft将开始每月向您收取10美元的费用,直到您记得取消为止。 如果愿意,您可以在Microsoft帐户的“服务和订阅”页面上查看和取消此订阅。

    翻译自: https://www.howtogeek.com/317745/what-is-xbox-game-pass-and-is-it-worth-it/

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  • origination fee  that Lending Club charges. The borrower then makes monthly payments back to Lending Club either over 36 months or over 60 months. Lending Club redistributes these payments to the ...

    1: Introduction

    In this course, we will walk through the full data science life cycle, from data cleaning and feature selection to machine learning. We will focus on credit modelling, a well known data science problem that focuses on modeling a borrower's credit risk. Credit has played a key role in the economy for centuries and some form of credit has existed since the beginning of commerce. We'll be working with financial lending data from Lending Club. Lending Club is a marketplace for personal loans that matches borrowers who are seeking a loan with investors looking to lend money and make a return. You can read more about their marketplace here.

    Each borrower fills out a comprehensive application, providing their past financial history, the reason for the loan, and more. Lending Club evaluates each borrower's credit score using past historical data (and their own data science process!) and assign an interest rate to the borrower. The interest rate is the percent in addition to the requested loan amount the borrower has to pay back. You can read more about the interest rate that Lending Club assigns here. Lending Club also tries to verify each piece of information the borrower provides but it can't always verify all of the information (usually for regulation reasons).

    A higher interest rate means that the borrower is riskier and more unlikely to pay back the loan while a lower interest rate means that the borrower has a good credit history is more likely to pay back the loan. The interest rates range from 5.32% all the way to 30.99% and each borrower is given a grade according to the interest rate they were assigned. If the borrower accepts the interest rate, then the loan is listed on the Lending Club marketplace.

    Investors are primarily interested in receiveing a return on their investments. Approved loans are listed on the Lending Club website, where qualified investors can browse recently approved loans, the borrower's credit score, the purpose for the loan, and other information from the application. Once they're ready to back a loan, they select the amount of money they want to fund. Once a loan's requested amount is fully funded, the borrower receives the money they requested minus the origination fee that Lending Club charges.

    The borrower then makes monthly payments back to Lending Club either over 36 months or over 60 months. Lending Club redistributes these payments to the investors. This means that investors don't have to wait until the full amount is paid off to start to see money back. If a loan is fully paid off on time, the investors make a return which corresponds to the interest rate the borrower had to pay in addition the requested amount. Many loans aren't completely paid off on time, however, and some borrowers default on the loan.

    Here's a diagram from Bible Money Matters that sums up the process:

    Imgur

    While Lending Club has to be extremely savvy and rigorous with their credit modelling, investors on Lending Club need to be equally as savvy about determining which loans are more likely to be paid off. While at first, you may wonder why investors would put money into anything but low interest loans. The incentive investors have to back higher interest loans is, well, the higher interest! If investors believe the borrower can pay back the loan, even if he or she has a weak financial history, then investors can make more money through the larger additional amount the borrower has to pay.

    Most investors use a portfolio strategy to invest small amounts in many loans, with healthy mixes of low, medium, and interest loans. In this course, we'll focus on the mindset of a conservative investor who only wants to invest in the loans that have a good chance of being paid off on time. To do that, we'll need to first understand the features in the dataset and then experiment with building machine learning models that reliably predict if a loan will be paid off or not.

    2: Introduction To The Data

    Lending Club releases data for all of the approved and declined loan applications periodically on their website. You can select a few different year ranges to download the datasets (in CSV format) for both approved and declined loans.

    You'll also find a data dictionary (in XLS format) which contains information on the different column names towards the bottom of the page. We recommend downloading the data dictionary to so you can refer to it whenever you want to learn more about what a column represents in the datasets. Here's a link to the data dictionary file hosted on Google Drive.

    Before diving into the datasets themselves, let's get familiar with the data dictionary. The LoanStats sheet describes the approved loans datasets and the RejectStats describes the rejected loans datasets. Since rejected applications don't appear on the Lending Club marketplace and aren't available for investment, we'll be focusing on data on approved loans only.

    The approved loans datasets contain information on current loans, completed loans, and defaulted loans. Let's now define the problem statement for this machine learning project:

    • Can we build a machine learning model that can accurately predict if a borrower will pay off their loan on time or not?

    Before we can start doing machine learning, we need to define what features we want to use and which column repesents the target column we want to predict. Let's start by reading in the dataset and exploring it.

    3: Reading In To Pandas

    In this mission, we'll focus on approved loans data from 2007 to 2011, since a good number of the loans have already finished. In the datasets for later years, many of the loans are current and still being paid off.

    To ensure that code runs fast on our platform, we reduced the size of LoanStats3a.csv by:

    • removing the first line:
      • because it contains the extraneous text Notes offered by Prospectus (https://www.lendingclub.com/info/prospectus.action)instead of the column titles, which prevents the dataset from being parsed by the pandas library properly
    • removing the desc column:
      • which contains a long text explanation for each loan
    • removing the url column:
      • which contains a link to each loan on Lending Club which can only be accessed with an investor account
    • removing all columns containing more than 50% missing values:
      • which allows us to move faster since we can spend less time trying to fill these values

    The following code replicates this process, if you want to replicate the dataset to work with it on your own:

     

     
    import pandas as pd
    loans_2007 = pd.read_csv('LoanStats3a.csv', skiprows=1)
    half_count = len(loans_2007) / 2
    loans_2007 = loans_2007.dropna(thresh=half_count, axis=1)
    loans_2007 = loans_2007.drop(['desc', 'url'],axis=1)
    loans_2007.to_csv('loans_2007.csv', index=False)

    We named the filtered dataset loans_2007.csv instead in case we want to explore the raw dataset (LoanStats3a.csv) without mixing up the two. First things first, let's read in the dataset into a Dataframe so we can start to explore the data and explore the remaining features.

    Instructions

    • Read loans_2007.csv into a DataFrame named loans_2007and use the print function to display the first row of the Dataframe.

    • Use the print function to:

      • display the first row ofloans_2007 and
      • the number of columns inloans_2007.

    import pandas as pd
    loans_2007 = pd.read_csv("loans_2007.csv")
    loans_2007.drop_duplicates()
    print(loans_2007.iloc[0])
    print(loans_2007.shape[1])

     

    4: First Group Of Columns

    The Dataframe contains many columns and can be cumbersome to try to explore all at once. Let's break up the columns into 3 groups of 18 columns and use the data dictionary to become familiar with what each column represents. As you understand each feature, you want to pay attention to any features that:

    • leak information from the future (after the loan has already been funded)
    • don't affect a borrower's ability to pay back a loan (e.g. a randomly generated ID value by Lending Club)
    • formatted poorly and need to be cleaned up
    • require more data or a lot of processing to turn into a useful feature
    • contain redundant information

    We need to especially pay attention to data leakage, since it can cause our model to overfit. This is because the model would be using data about the target column that wouldn't be available when we're using the model on future loans. We encourage you to spend as much time as you need to understand each column, because a poor understanding could cause you to make mistakes in the data analysis and modeling process. As you go through the dictionary, keep in mind that we need to select one of the columns as the target column we want to use when we move on to the machine learning phase.

    In this screen and the next few screens, let's focus on just columns that we need to remove from consideration. Then, we can circle back and further dissect the columns we decided to keep.

    To make this process easier, we created a table that contains the name, data type, first row's value, and description from the data dictionary for the first 18 rows.

    namedtypefirst valuedescription
    idobject1077501A unique LC assigned ID for the loan listing.
    member_idfloat641.2966e+06A unique LC assigned Id for the borrower member.
    loan_amntfloat645000The listed amount of the loan applied for by the borrower.
    funded_amntfloat645000The total amount committed to that loan at that point in time.
    funded_amnt_invfloat6449750The total amount committed by investors for that loan at that point in time.
    termobject36 monthsThe number of payments on the loan. Values are in months and can be either 36 or 60.
    int_rateobject10.65%Interest Rate on the loan
    installmentfloat64162.87The monthly payment owed by the borrower if the loan originates.
    gradeobjectBLC assigned loan grade
    sub_gradeobjectB2LC assigned loan subgrade
    emp_titleobjectNaNThe job title supplied by the Borrower when applying for the loan.
    emp_lengthobject10+ yearsEmployment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years.
    home_ownershipobjectRENTThe home ownership status provided by the borrower during registration. Our values are: RENT, OWN, MORTGAGE, OTHER.
    annual_incfloat6424000The self-reported annual income provided by the borrower during registration.
    verification_statusobjectVerifiedIndicates if income was verified by LC, not verified, or if the income source was verified
    issue_dobjectDec-2011The month which the loan was funded
    loan_statusobjectCharged OffCurrent status of the loan
    pymnt_planobjectnIndicates if a payment plan has been put in place for the loan
    purposeobjectcarA category provided by the borrower for the loan request.

    After analyzing each column, we can conclude that the following features need to be removed:

    • id: randomly generated field by Lending Club for unique identification purposes only
    • member_id: also a randomly generated field by Lending Club for unique identification purposes only
    • funded_amnt: leaks data from the future (after the loan is already started to be funded)
    • funded_amnt_inv: also leaks data from the future (after the loan is already started to be funded)
    • grade: contains redundant information as the interest rate column (int_rate)
    • sub_grade: also contains redundant information as the interest rate column (int_rate)
    • emp_title: requires other data and a lot of processing to potentially be useful
    • issue_d: leaks data from the future (after the loan is already completed funded)

    Recall that Lending Club assigns a grade and a sub-grade based on the borrower's interest rate. While the grade and sub_grade values are categorical, the int_rate column contains continuous values, which are better suited for machine learning.

    Let's now drop these columns from the Dataframe before moving onto the next group of columns.

    Instructions

    Use the Dataframe method drop to remove the following columns from theloans_2007 Dataframe:

    • id
    • member_id
    • funded_amnt
    • funded_amnt_inv
    • grade
    • sub_grade
    • emp_title
    • issue_d


    loans_2007 = loans_2007.drop(["id", "member_id", "funded_amnt", "funded_amnt_inv", "grade", "sub_grade", "emp_title", "issue_d"], axis=1)

     

    5: Second Group Of Features
    Let's now look at the next 18 columns:

    name    dtype    first value    description
    title    object    Computer    The loan title provided by the borrower
    zip_code    object    860xx    The first 3 numbers of the zip code provided by the borrower in the loan application.
    addr_state    object    AZ    The state provided by the borrower in the loan application
    dti    float64    27.65    A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income.
    delinq_2yrs    float64    0    The number of 30+ days past-due incidences of delinquency in the borrower's credit file for the past 2 years
    earliest_cr_line    object    Jan-1985    The month the borrower's earliest reported credit line was opened
    inq_last_6mths    float64    1    The number of inquiries in past 6 months (excluding auto and mortgage inquiries)
    open_acc    float64    3    The number of open credit lines in the borrower's credit file.
    pub_rec    float64    0    Number of derogatory public records
    revol_bal    float64    13648    Total credit revolving balance
    revol_util    object    83.7%    Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit.
    total_acc    float64    9    The total number of credit lines currently in the borrower's credit file
    initial_list_status    object    f    The initial listing status of the loan. Possible values are – W, F
    out_prncp    float64    0    Remaining outstanding principal for total amount funded
    out_prncp_inv    float64    0    Remaining outstanding principal for portion of total amount funded by investors
    total_pymnt    float64    5863.16    Payments received to date for total amount funded
    total_pymnt_inv    float64    5833.84    Payments received to date for portion of total amount funded by investors
    total_rec_prncp    float64    5000    Principal received to date
    Within this group of columns, we need to drop the following columns:

    zip_code: redundant with the addr_state column since only the first 3 digits of the 5 digit zip code are visible (which only can be used to identify the state the borrower lives in)
    out_prncp: leaks data from the future, (after the loan already started to be paid off)
    out_prncp_inv: also leaks data from the future, (after the loan already started to be paid off)
    total_pymnt: also leaks data from the future, (after the loan already started to be paid off)
    total_pymnt_inv: also leaks data from the future, (after the loan already started to be paid off)
    total_rec_prncp: also leaks data from the future, (after the loan already started to be paid off)
    The out_prncp and out_prncp_inv both describe the outstanding principal amount for a loan, which is the remaining amount the borrower still owes. These 2 columns as well as the total_pymnt column describe properties of the loan after it's fully funded and started to be paid off. This information isn't available to an investor before the loan is fully funded and we don't want to include it in our model.

    Let's go ahead and remove these columns from the Dataframe.

    Instructions
    Use the Dataframe method drop to remove the following columns from the loans_2007 Dataframe:
    zip_code
    out_prncp
    out_prncp_inv
    total_pymnt
    total_pymnt_inv
    total_rec_prncp

     


    loans_2007 = loans_2007.drop(["zip_code", "out_prncp", "out_prncp_inv", "total_pymnt", "total_pymnt_inv", "total_rec_prncp"], axis=1)

     

    6: Third Group Of Features

    Let's now move on to the last group of features:

    namedtypefirst valuedescription
    total_rec_intfloat64863.16Interest received to date
    total_rec_late_feefloat640Late fees received to date
    recoveriesfloat640post charge off gross recovery
    collection_recovery_feefloat640post charge off collection fee
    last_pymnt_dobjectJan-2015Last month payment was received
    last_pymnt_amntfloat64171.62Last total payment amount received
    last_credit_pull_dobjectJun-2016The most recent month LC pulled credit for this loan
    collections_12_mths_ex_medfloat640Number of collections in 12 months excluding medical collections
    policy_codefloat641publicly available policy_code=1 new products not publicly available policy_code=2
    application_typeobjectINDIVIDUALIndicates whether the loan is an individual application or a joint application with two co-borrowers
    acc_now_delinqfloat640The number of accounts on which the borrower is now delinquent.
    chargeoff_within_12_mthsfloat640Number of charge-offs within 12 months
    delinq_amntfloat640The past-due amount owed for the accounts on which the borrower is now delinquent.
    pub_rec_bankruptciesfloat640Number of public record bankruptcies
    tax_liensfloat640Number of tax liens

    In the last group of columns, we need to drop the following columns:

    • total_rec_int: leaks data from the future, (after the loan already started to be paid off),
    • total_rec_late_fee: also leaks data from the future, (after the loan already started to be paid off),
    • recoveries: also leaks data from the future, (after the loan already started to be paid off),
    • collection_recovery_fee: also leaks data from the future, (after the loan already started to be paid off),
    • last_pymnt_d: also leaks data from the future, (after the loan already started to be paid off),
    • last_pymnt_amnt: also leaks data from the future, (after the loan already started to be paid off).

    All of these columns leak data from the future, meaning that they're describing aspects of the loan after it's already been fully funded and started to be paid off by the borrower.

    Instructions

    Use the Dataframe method drop to remove the following columns from theloans_2007 Dataframe:

    • total_rec_int
    • total_rec_late_fee
    • recoveries
    • collection_recovery_fee
    • last_pymnt_d
    • last_pymnt_amnt

    Use the print function to:

    • display the first row ofloans_2007 and
    • the number of columns inloans_2007.


    loans_2007 = loans_2007.drop(["total_rec_int", "total_rec_late_fee", "recoveries", "collection_recovery_fee", "last_pymnt_d", "last_pymnt_amnt"], axis=1)
    print(loans_2007.iloc[0])
    print(loans_2007.shape[1])

     

    7: Target Column

    Just by becoming familiar with the columns in the dataset, we were able to reduce the number of columns from 52 to 34 columns. We now need to decide on a target column that we want to use for modeling.

    We should use the loan_status column, since it's the only column that directly describes if a loan was paid off on time, had delayed payments, or was defaulted on the borrower. Currently, this column contains text values and we need to convert it to a numerical one for training a model. Let's explore the different values in this column and come up with a strategy for converting the values in this column.

    Instructions

    • Use the Dataframe methodvalue_counts to return the frequency of the unique values in the loan_status column.
    • Display the frequency of each unique value using the printfunction.


    print(loans_2007['loan_status'].value_counts())

    8: Binary Classification

    There are 8 different possible values for the loan_status column. You can read about most of the different loan statuses on the Lending Clube webste. The 2 values that start with "Does not meet the credit policy" aren't explained unfortunately. A quick Google search takes us to explanations from the lending comunity here and here.

    We've compiled the explanation for each column as well as the counts in the Dataframe in the following table:

    Loan StatusCountMeaning
    Fully Paid33136Loan has been fully paid off.
    Charged Off5634Loan for which there is no longer a reasonable expectation of further payments.
    Does not meet the credit policy. Status:Fully Paid1988While the loan was paid off, the loan application today would no longer meet the credit policy and wouldn't be approved on to the marketplace.
    Does not meet the credit policy. Status:Charged Off761While the loan was charged off, the loan application today would no longer meet the credit policy and wouldn't be approved on to the marketplace.
    In Grace Period20The loan is past due but still in the grace period of 15 days.
    Late (16-30 days)8Loan hasn't been paid in 16 to 30 days (late on the current payment).
    Late (31-120 days)24Loan hasn't been paid in 31 to 120 days (late on the current payment).
    Current961Loan is up to date on current payments.
    Default3Loan is defaulted on and no payment has been made for more than 121 days.

    From the investor's perspective, we're interested in trying to predict which loans will be paid off on time and which ones won't be. Only theFully Paid and Charged Off values describe the final outcome of the loan. The other values describe loans that are still on going and where the jury is still out on if the borrower will pay back the loan on time or not. While the Default status resembles the Charged Offstatus, in Lending Club's eyes, loans that are charged off have essentially no chance of being repaid while default ones have a small chance. You can read about the difference here.

    Since we're interesting in being able to predict which of these 2 values a loan will fall under, we can treat the problem as a binary classification one. Let's remove all the loans that don't contain either Fully Paid and Charged Off as the loan's status and then transform the Fully Paid values to 1 for the positive case and the Charged Off values to 0 for the negative case. While there are a few different ways to transform all of the values in a column, we'll use the Dataframe method replace. According to the documentation, we can pass the replace method a nested mapping dictionary in the following format:

     

     
    mapping_dict = {
       "date": {
           "january": 1,
           "february": 2,
           "march": 3
       }
    }
    df = df.replace(mapping_dict)

    Lastly, one thing we need to keep in mind is the class imbalance between the positive and negative cases. While there are 33,136 loans that have been fully paid off, there are only 5,634 that were charged off. This class imbalance is a common problem in binary classification and during training, the model ends up having a strong bias towards predicting the class with more observations in the training set and will rarely predict the class with less observations. The stronger the imbalance, the more biased the model becomes. There are a few different ways to tackle this class imbalance, which we'll explore later.

    Instructions

    • Remove all rows fromloans_2007 that contain values other than Fully Paid orCharged Off for theloan_status column.
    • Use the Dataframe methodreplace to replace:
      • Fully Paid with 1
      • Charged Off with 0


    loans_2007 = loans_2007[(loans_2007['loan_status'] == "Fully Paid") | (loans_2007['loan_status'] == "Charged Off")]

    status_replace = {
        "loan_status" : {
            "Fully Paid": 1,
            "Charged Off": 0,
        }
    }

    loans_2007 = loans_2007.replace(status_replace)

     

    9: Removing Single Value Columns

    To wrap up this mission, let's look for any columns that contain only one unique value and remove them. These columns won't be useful for the model since they don't add any information to each loan application. In addition, removing these columns will reduce the number of columns we'll need to explore further in the next mission.

    We'll need to compute the number of unique values in each column and drop the columns that contain only one unique value. While the Series method unique returns the unique values in a column, it also counts the Pandas missing value object nan as a value:

     

     
    # Returns 0 and nan.
    unique_values = loans['tax_liens'].unique()

    Since we're trying to find columns that contain one true unique value, we should first drop the null values then compute the number of unique values:

     

     
    non_null = loans_2007['tax_liens'].dropna()
    unique_non_null = non_null.unique()
    num_true_unique = len(len_unique_non_null)

    Instructions

    • Remove any columns fromloans_2007 that contain only one unique value:
      • Create an empty list,drop_columns to keep track of which columns you want to drop
      • For each column:
        • Use the Series method dropna to remove any null values and then use the Series methodunique to return the set of non-null unique values
        • Use the len()function to return the number of values in that set
        • Append the column to drop_columns if it contains only 1 unique value
      • Use the Dataframe methoddrop to remove the columns in drop_columnsfrom loans_2007
    • Use the print function to display drop_columns so we know which ones were removed

     


    orig_columns = loans_2007.columns
    drop_columns = []
    for col in orig_columns:
        col_series = loans_2007[col].dropna().unique()
        if len(col_series) == 1:
            drop_columns.append(col)
    loans_2007 = loans_2007.drop(drop_columns, axis=1)
    print(drop_columns)

    10: Next Steps

    It looks we we were able to remove 9 more columns since they only contained 1 unique value.

    In this mission, we started to become familiar with the columns in the dataset and removed many columns that aren't useful for modeling. We also selected our target column and decided to focus our modeling efforts on binary classification. In the next mission, we'll explore the individual features in greater depth and work towards training our first machine learning model.

     

     

    转载于:https://my.oschina.net/Bettyty/blog/752993

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