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Open in app Get started ITNEXT Published in ITNEXT You have 2 free member-only stories left this month. Sign up for Medium and get an extra one Kush Kush Follow Apr 15, 2021 · 7 min read · Listen Save How you can Control your Android Device with Python Photo by Caspar Camille Rubin on Unsplash Photo by Caspar Camille Rubin on Unsplash Introduction A while back I was thinking of ways in which I could annoy my friends by spamming them with messages for a few minutes, and while doing some research I came across the Android Debug Bridge. In this quick guide I will show you how you can interface with it using Python and how to create 2 quick scripts. The ADB (Android Debug Bridge) is a command line tool (CLI) which can be used to control and communicate with an Android device. You can do many things such as install apps, debug apps, find hidden features and use a shell to interface with the device directly. To enable the ADB, your device must firstly have Developer Options unlocked and USB debugging enabled. To unlock developer options, you can go to your devices settings and scroll down to the about section and find the build number of the current software which is on the device. Click the build number 7 times and Developer Options will be enabled. Then you can go to the Developer Options panel in the settings and enable USB debugging from there. Now the only other thing you need is a USB cable to connect your device to your computer. Here is what todays journey will look like: Installing the requirements Getting started The basics of writing scripts Creating a selfie timer Creating a definition searcher Installing the requirements The first of the 2 things we need to install, is the ADB tool on our computer. This comes automatically bundled with Android Studio, so if you already have that then do not worry. Otherwise, you can head over to the official docs and at the top of the page there should be instructions on how to install it. Once you have installed the ADB tool, you need to get the python library which we will use to interface with the ADB and our device. You can install the pure-python-adb library using pip install pure-python-adb. Optional: To make things easier for us while developing our scripts, we can install an open-source program called scrcpy which allows us to display and control our android device with our computer using a mouse and keyboard. To install it, you can head over to the Github repo and download the correct version for your operating system (Windows, macOS or Linux). If you are on Windows, then extract the zip file into a directory and add this directory to your path. This is so we can access the program from anywhere on our system just by typing in scrcpy into our terminal window. Getting started Now that all the dependencies are installed, we can start up our ADB and connect our device. Firstly, connect your device to your PC with the USB cable, if USB debugging is enabled then a message should pop up asking if it is okay for your PC to control the device, simply answer yes. Then on your PC, open up a terminal window and start the ADB server by typing in adb start-server. This should print out the following messages: * daemon not running; starting now at tcp:5037 * daemon started successfully If you also installed scrcpy, then you can start that by just typing scrcpy into the terminal. However, this will only work if you added it to your path, otherwise you can open the executable by changing your terminal directory to the directory of where you installed scrcpy and typing scrcpy.exe. Hopefully if everything works out, you should be able to see your device on your PC and be able to control it using your mouse and keyboard. Now we can create a new python file and check if we can find our connected device using the library: Here we import the AdbClient class and create a client object using it. Then we can get a list of devices connected. Lastly, we get the first device out of our list (it is generally the only one there if there is only one device connected). The basics of writing scripts The main way we are going to interface with our device is using the shell, through this we can send commands to simulate a touch at a specific location or to swipe from A to B. To simulate screen touches (taps) we first need to work out how the screen coordinates work. To help with these we can activate the pointer location setting in the developer options. Once activated, wherever you touch on the screen, you can see that the coordinates for that point appear at the top. The coordinate system works like this: A diagram to show how the coordinate system works A diagram to show how the coordinate system works The top left corner of the display has the x and y coordinates (0, 0) respectively, and the bottom right corners’ coordinates are the largest possible values of x and y. Now that we know how the coordinate system works, we need to check out the different commands we can run. I have made a list of commands and how to use them below for quick reference: Input tap x y Input text “hello world!” Input keyevent eventID Here is a list of some common eventID’s: 3: home button 4: back button 5: call 6: end call 24: volume up 25: volume down 26: turn device on or off 27: open camera 64: open browser 66: enter 67: backspace 207: contacts 220: brightness down 221: brightness up 277: cut 278: copy 279: paste If you wanted to find more, here is a long list of them here. Creating a selfie timer Now we know what we can do, let’s start doing it. In this first example I will show you how to create a quick selfie timer. To get started we need to import our libraries and create a connect function to connect to our device: You can see that the connect function is identical to the previous example of how to connect to your device, except here we return the device and client objects for later use. In our main code, we can call the connect function to retrieve the device and client objects. From there we can open up the camera app, wait 5 seconds and take a photo. It’s really that simple! As I said before, this is simply replicating what you would usually do, so thinking about how to do things is best if you do them yourself manually first and write down the steps. Creating a definition searcher We can do something a bit more complex now, and that is to ask the browser to find the definition of a particular word and take a screenshot to save it on our computer. The basic flow of this program will be as such: 1. Open the browser 2. Click the search bar 3. Enter the search query 4. Wait a few seconds 5. Take a screenshot and save it But, before we get started, you need to find the coordinates of your search bar in your default browser, you can use the method I suggested earlier to find them easily. For me they were (440, 200). To start, we will have to import the same libraries as before, and we will also have our same connect method. In our main function we can call the connect function, as well as assign a variable to the x and y coordinates of our search bar. Notice how this is a string and not a list or tuple, this is so we can easily incorporate the coordinates into our shell command. We can also take an input from the user to see what word they want to get the definition for: We will add that query to a full sentence which will then be searched, this is so that we can always get the definition. After that we can open the browser and input our search query into the search bar as such: Here we use the eventID 66 to simulate the press of the enter key to execute our search. If you wanted to, you could change the wait timings per your needs. Lastly, we will take a screenshot using the screencap method on our device object, and we can save that as a .png file: Here we must open the file in the write bytes mode because the screencap method returns bytes representing the image. If all went according to plan, you should have a quick script which searches for a specific word. Here it is working on my phone: A GIF to show how the definition searcher example works on my phone A GIF to show how the definition searcher example works on my phone Final thoughts Hopefully you have learned something new today, personally I never even knew this was a thing before I did some research into it. The cool thing is, that you can do anything you normal would be able to do, and more since it just simulates your own touches and actions! I hope you enjoyed the article and thank you for reading! 💖 468 9 468 9 More from ITNEXT Follow ITNEXT is a platform for IT developers & software engineers to share knowledge, connect, collaborate, learn and experience next-gen technologies. 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Predicting Loss Ratios for Auto Insurance Portfolios - ITCS 6100 Big Data Analytics for Competitive Advantage

Loss Data Analytics is an interactive, online, freely available text. It provides core training on one of the foundations of actuarial science, loss data modeling.

R Code to Support the Loss Data Analytics Book

This site provides overheads for the drafts of our Loss Data Analytics chapters.

This project provides interactive, online, freely available texts. Our first effort, *Loss Data Analytics*, provides core training on one of the foundations of actuarial science, loss data modeling.

AdventureWorks is a fictitious multinational manufacturing company. The data set used for this report is from the AdventureWorks2014 version. The goal of this project is to demonstrate how building powerful and beautiful (I hope :)) reports in Power BI by following the data visualization best practices and the data modelling patterns, showing the awesome features of Power BI like Report Page Tooltip, Calculated Groups, Forecasting, What-If parameters, Time Intelligence Functions, complex DAX code, custom charts and how it is easy to integrate Power BI with Python/R script in order to build machine learning models and so creating advanced and predictive analytics reports. Reports Pages are as follow: - P&L Overview: Profit & Loss Report, in which I analyzed the economic status of the company from the point of view of Revenues and Expenditures. In this report page I compared the trend of actual amount and budget amount, unfortunately for 2011 year only (missing data for other years). You can notice the powerful feature of “report page tooltip” in this page, by hovering over the bar charts. - Internet Sales Overview: in this report page I analyzed the sales of products to customers via Internet. You can notice the forecasting feature of Power BI and the use of what-if parameters in order to build advanced analytics reports. Also I made use of calculated groups (for further info about visit https://www.sqlbi.com/articles/introducing-calculation-groups/). - Reseller Sales Overview: similar to the previous one, but this time the focus is on the sales of products to resellers. Things to notice in this report page are the comparison of current sales vs last year sales, the comparison of actual sales amount vs budget/quota sales amount, and the running total and moving/running average charts. - Product Inventory Overview: inventory management analysis of the company. I analyzed the current stock on hold vs the recent sales revenue, showing the value of current stock, the current units in stock and the stock ratio. - Products Overview: in this report page I analyzed the products by category, color, size, “ABC” class too. Two very interesting and powerful things to notice: the basket analysis using DAX and the basket/association rules analysis using R script and apriori machine learning model. I made use of the custom chart called “Network Chart” to represent the association rules between products. Through the basket analysis the user could know which product is likely to be bought with another one. - Customers Overview: customer analysis by age group, location, status, and so on. The customer could be classified as “Bike Buyer” and “Non Bike Buyer”. So I decided to build a machine learning model (in this case I used XGBoost algorithm in Python) in order to predict if a (new) customer would be a bike buyer or not. The model has a prediction accuracy of about 84% which it is not bad. - Employees Overview: employee analysis by sales location, title, age. Thing to notice is the ribbon chart through which I can analyze the performance of employees over time. - Reseller Overview: analysis of the performance of the resellers. Important dimensions to analyze are the business type, the product line, the reseller location.

Interview Coding Challenge Data Science Step 1 of the Data Scientist Interview process. Follow the instructions below to complete this portion of the interview. Please note, although we do not set a time limit for this challenge, we recommend completing it as soon as possible as we evaluate candidates on a first come, first serve basis... If you have any questions, please feel free to email support@TheZig.io. We will do our best to clarify any issues you come across. Prerequisites: A Text Editor - We recommend Visual Studio Code for the ClientSide code, its lightweight, powerful and Free! (https://code.visualstudio.com/) SQL Server Management Studio (https://docs.microsoft.com/en-us/sql/ssms/download-sql-server-management-studio-ssms?view=sql-server-2017) R - Software Environment for statitistal computing and graphics. You can download R at the mirrors listed here (https://cran.r-project.org/mirrors.html) Azure - Microsoft's Cloud Computing platform. You can create an account without a credit card by using the Azure Pass available at this link (https://azure.microsoft.com/en-us/offers/azure-pass/) Git - For source control and committing your final solution to a new private repo (https://git-scm.com/downloads) a. If you're not very familiar with git commands, here's a helpful cheatsheet (https://services.github.com/on-demand/downloads/github-git-cheat-sheet.pdf) 'R' Challenge For each numbered section below, write R code and comments to solve the problem or to show your rationale. For sections that ask you to give outputs, provide outputs in separate files and name them with the section number and the word output "Section 1 - Output". Create a private repo and submit your modified R script along with any supporting files. Load in the dataset from the accompanying file "account-defaults.csv" This dataset contains information about loan accounts that either went delinquent or stayed current on payments within the loan's first year. FirstYearDelinquency is the outcome variable, all others are predictors. The objective of modeling with this dataset is to be able to predict the probability that new accounts will become delinquent; it is primarily valuable to understand lower-risk accounts versus higher-risk accounts (and not just to predict 'yes' or 'no' for new accounts). FirstYearDelinquency - indicates whether the loan went delinquent within the first year of the loan's life (values of 1) AgeOldestIdentityRecord - number of months since the first record was reported by a national credit source AgeOldestAccount - number of months since the oldest account was opened AgeNewestAutoAccount - number of months since the most recent auto loan or lease account was opened TotalInquiries - total number of credit inquiries on record AvgAgeAutoAccounts - average number of months since auto loan or lease accounts were opened TotalAutoAccountsNeverDelinquent - total number of auto loan or lease accounts that were never delinquent WorstDelinquency - worst status of days-delinquent on an account in the first 12 months of an account's life; values of '400' indicate '400 or greater' HasInquiryTelecomm - indicates whether one or more telecommunications credit inquires are on record within the last 12 months (values of 1) Perform an exploratory data analysis on the accounts data In your analysis include summary statistics and visualizations of the distributions and relationships. Build one or more predictive model(s) on the accounts data using regression techniques Identify the strongest predictor variables and provide interpretations. Identify and explain issues with the model(s) such as collinearity, etc. Calculate predictions and show model performance on out-of-sample data. Summarize out-of-sample data in tiers from highest-risk to lowest-risk. Split up the dataset by the WorstDelinquency variable. For each subset, run a simple regression of FirstYearDelinquency ~ TotalInquiries. Extract the predictor's coefficient and p-value from each model. Store the in a list where the names of the list correspond to the values of WorstDelinquency. Load in the dataset from the accompanying file "vehicle-depreciation.csv". The dataset contains information about vehicles that our company purchases at auction, sells to customers, repossess from defaulted accounts, and finally re-sell at auction to recover some of our losses. Perform an analysis and/or build a predictive model that provides a method to estimate the depreciation of vehicle worth (from auction purchase to auction sale). Use whatever techniques you want to provide insight into the dataset and walk us through your results - this is your chance to show off your analytical and storytelling skills! CustomerGrade - the credit risk grade of the customer AuctionPurchaseDate - the date that the vehicle was purchased at auction AuctionPurchaseAmount - the dollar amount spent purchasing the vehicle at auction AuctionSaleDate - the date that the vehicle was sold at auction AuctionSaleAmount - the dollar amount received for selling the vehicle at auction VehicleType - the high-level class of the vehicle Year - the year of the vehicle Make - the make of the vehicle Model - the model of the vehicle Trim - the trim of the vehicle BodyType - the body style of the vehicle AuctionPurchaseOdometer - the odometer value of the vehicle at the time of purchase at the auction AutomaticTransmission - indicates (with value of 1) whether the vehicle has an automatic transmission DriveType - the drivetrain type of the vehicle

Google Measurement Controller-Controller Data Protection Terms The Measurement Services customer agreeing to these terms (“Customer”) has entered into an agreement with either Google or a third party reseller (as applicable) for the provision of the Measurement Services (as amended from time to time, the “Agreement”) through which services user interface Customer has enabled the Data Sharing Setting. These Google Measurement Controller-Controller Data Protection Terms (“Controller Terms”) are entered into by Google and Customer. Where the Agreement is between Customer and Google, these Controller Terms supplement the Agreement. Where the Agreement is between Customer and a third party reseller, these Controller Terms form a separate agreement between Google and Customer. For the avoidance of doubt, the provision of the Measurement Services is governed by the Agreement. These Controller Terms set out the data protection provisions relating to the Data Sharing Setting only but do not otherwise apply to the provision of the Measurement Services. Subject to Section 8.2 (Processor Terms), these Controller Terms will be effective, and replace any previously applicable terms relating to their subject matter, from the Terms Effective Date. If you are accepting these Controller Terms on behalf of Customer, you warrant that: (a) you have full legal authority to bind Customer to these Controller Terms; (b) you have read and understand these Controller Terms; and (c) you agree, on behalf of Customer, to these Controller Terms. If you do not have the legal authority to bind Customer, please do not accept these Controller Terms. Please do not accept these Controller Terms if you are a reseller. These Controller Terms set out the rights and obligations that apply between users of the Measurement Services and Google. 1. Introduction These Controller Terms reflect the parties’ agreement on the processing of Controller Personal Data pursuant to the Data Sharing Setting. 2. Definitions and Interpretation 2.1 In these Controller Terms: “Affiliate” means an entity that directly or indirectly controls, is controlled by, or is under common control with, a party. "Confidential Information" means these Controller Terms. “Controller Data Subject” means a data subject to whom Controller Personal Data relates. “Controller MCCs” means the terms at privacy.google.com/businesses/controllerterms/mccs, which are standard data protection clauses for the transfer of personal data to controllers established in third countries which do not ensure an adequate level of data protection, as described in Article 46 of the EU GDPR. “Controller Personal Data” means any personal data that is processed by a party pursuant to the Data Sharing Setting. “Data Protection Legislation” means, as applicable: (a) the GDPR; and/or (b) the Federal Data Protection Act of 19 June 1992 (Switzerland). “Data Sharing Setting” means the data sharing setting which Customer has enabled via the user interface of the Measurement Services and which enables Google and its Affiliates to use personal data for improving Google’s and its Affiliates’ products and services. "EU GDPR" means Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC. “End Controller” means, for each party, the ultimate controller of Controller Personal Data. “European Controller Personal Data” means Controller Personal Data of Controller Data Subjects located in the European Economic Area or Switzerland. “GDPR” means, as applicable: (a) the EU GDPR; and/or (b) the UK GDPR. “Google” means: (a) where a Google Entity is party to the Agreement, that Google Entity. (b) where the Agreement is between Customer and a third party reseller and: (i) the third party reseller is organised in North America or in another region outside Europe, the Middle East, Africa, Asia and Oceania, Google LLC (formerly known as Google Inc.); (ii) the third party reseller is organised in Europe, the Middle East or Africa, Google Ireland Limited; or (iii) the third party reseller is organised in Asia and Oceania, Google Asia Pacific Pte. Ltd. “Google End Controllers” means the End Controllers of Controller Personal Data processed by Google. “Google Entity” means Google LLC, Google Ireland Limited or any other Affiliate of Google LLC. “Measurement Services” means Google Analytics, Google Analytics 360, Google Analytics for Firebase, Google Optimize or Google Optimize 360, as applicable to the Data Sharing Setting for which the parties agreed to these Controller Terms. “Policies” means the Google End User Consent Policy available at https://www.google.com/about/company/user-consent-policy.html. “Processor Terms” means: (a) where Google is a party to the Agreement, the processor terms available at https://privacy.google.com/businesses/processorterms/; or (b) where the Agreement is between Customer and a third party reseller, such terms reflecting a controller-processor relationship (if any) as agreed between the Customer and the third party reseller. “Terms Effective Date” means, as applicable: (a) 25 May 2018, if Customer clicked to accept or the parties otherwise agreed to these Controller Terms before or on such date; or (b) the date on which Customer clicked to accept or the parties otherwise agreed to these Controller Terms, if such date is after 25 May 2018. “UK Controller Personal Data” means Controller Personal Data of Controller Data Subjects located in the UK. “UK GDPR” means the EU GDPR as amended and incorporated into UK law under the UK European Union (Withdrawal) Act 2018, if in force. 2.2 The terms “controller”, “data subject”, “personal data”, “processing” and “processor” as used in these Controller Terms have the meanings given in the GDPR, and the terms “data importer” and “data exporter” have the meanings given in the Controller MCCs. 2.3 Any examples in these Controller Terms are illustrative and not the sole examples of a particular concept. 2.4 Any reference to a legal framework, statute or other legislative enactment is a reference to it as amended or re-enacted from time to time. 2.5 If these Controller Terms are translated into any other language, and there is a discrepancy between the English text and the translated text, the English text will govern. 2.6 References in the Controller MCCs to the “Google Ads Controller-Controller Data Protection Terms” shall be deemed to mean the “Google Measurement Controller-Controller Data Protection Terms”. 3. Application of these Controller Terms 3.1 Application of Data Protection Legislation These Controller Terms will only apply to the extent that the Data Protection Legislation applies to the processing of Controller Personal Data. 3.2 Application to Data Sharing Setting These Controller Terms will only apply to the Data Sharing Setting for which the parties agreed to these Controller Terms (for example, the Data Sharing Setting for which Customer clicked to accept these Controller Terms). 3.3 Duration These Controller Terms will apply from the Terms Effective Date and continue while Google or Customer processes Controller Personal Data, after which these Controller Terms will automatically terminate. 4. Roles and Restrictions on Processing 4.1 Independent Controllers Subject to Section 4.4 (End Controllers), each: (a) is an independent controller of Controller Personal Data under the Data Protection Legislation; (b) will individually determine the purposes and means of its processing of Controller Personal Data; and (c) will comply with the obligations applicable to it under the Data Protection Legislation with respect to the processing of Controller Personal Data. 4.2 Restrictions on Processing Section 4.1 (Independent Controllers) will not affect any restrictions on either party’s rights to use or otherwise process Controller Personal Data under the Agreement. 4.3 End User Consent Customer will comply with the Policies in relation to the Controller Personal Data shared pursuant to the Data Sharing Setting and at all times will bear the burden of proof in establishing such compliance. 4.4 End Controllers Without reducing either party’s obligations under these Controller Terms, each party acknowledges that: (a) the other party’s Affiliates or clients may be End Controllers; and (b) the other party may act as a processor on behalf of its End Controllers. The Google End Controllers are: (i) for European Controller Personal Data processed by Google, Google Ireland Limited; and (ii) for UK Controller Personal Data processed by Google, Google LLC. Each party will ensure that its End Controllers comply with the Controller Terms, including (where applicable) the Controller MCCs. 5. Data Transfers 5.1 Data Transfers Subject to Section 5.2, either party may transfer Controller Personal Data to third countries if it complies with the provisions on the transfer of personal data to third countries in the Data Protection Legislation. 5.2 Transfers of UK Controller Personal Data to Google To the extent that Customer transfers UK Controller Personal Data to Google, Customer as data exporter will be deemed to have entered into the Controller MCCs with Google LLC (the applicable Google End Controller) as data importer and the transfers will be subject to the Controller MCCs, because Google LLC is established in the USA and such transfers are therefore to a third country that is not subject to an adequacy decision under the UK GDPR. For clarity, to the extent Customer transfers European Controller Personal Data to Google, the Controller MCCs are not required because Google Ireland Limited (the applicable Google End Controller) is established in Ireland and such transfers are therefore permitted under the Data Protection Legislation. 5.3 Additional Commercial Clauses for the Controller MCCs Sections 5.4 (Contacting Google) to 5.7 (Third Party Controllers) are additional commercial clauses relating to the Controller MCCs as permitted by Clause VII (Variation of these clauses) of the Controller MCCs. Nothing in Sections 5.4 (Contacting Google) to 5.7 (Third Party Controllers) varies or modifies any rights or obligations of the parties to the Controller MCCs. 5.4 Contacting Google Customer may contact Google Ireland Limited and/or Google LLC in connection with the Controller MCCs at https://support.google.com/policies/troubleshooter/9009584 or through such other means as may be provided by Google from time to time, including for the purposes of: (a) Clause II(e) of the Controller MCCs, to the extent Google LLC acts as data importer and Customer acts as data exporter under the Controller MCCs; and (b) requesting an Audit pursuant to Section 5.6 (a) (Reviews, Audits and Certifications of Compliance) below. 5.5 Responding to Data Subject Enquiries For the purpose of Clause I(d) of the Controller MCCs, the applicable data importer will be responsible for responding to enquiries from data subjects and the authority concerning the processing of applicable Controller Personal Data by the data importer. 5.6 Reviews, Audits and Certifications of Compliance (a) If the Controller MCCs apply under this Section 5 (Data Transfers), the applicable data importer will allow the applicable data exporter or a third party inspection agent or auditor appointed by the data exporter to conduct a review, audit and/or certification as described in Clause II(g) of the Controller MCCs (“Audit”) in accordance with this Section 5.6 (Reviews, Audits and Certifications of Compliance). (b) Following receipt by the data importer of a request for an Audit, the data importer and the data exporter will discuss and agree in advance on the reasonable start date, scope and duration of, and security and confidentiality controls applicable to, the Audit. (c) The data importer may charge a fee (based on the data importer’s reasonable costs) for any Audit. The data importer will provide the data exporter with further details of any applicable fee, and the basis of its calculation, in advance of the Audit. The data exporter will be responsible for any fees charged by any third party inspection agent or auditor appointed by the data exporter to execute the Audit. (d) The data importer may object to any third party inspection agent or auditor appointed by the data exporter to conduct any Audit if the inspection agent or auditor is, in the data importer’s reasonable opinion, not suitably qualified or independent, a competitor of the data importer or otherwise manifestly unsuitable. Any such objection by the data importer will require the data exporter to appoint another inspection agent or auditor or conduct the Audit itself. (e) The data importer will not be required either to disclose to the data exporter or its third party inspection agent or auditor, or to allow the data exporter or its third party inspection agent or auditor to access: (i) any data of any customers of the data importer or any of its Affiliates; (ii) any internal accounting or financial information of the data importer or any of its Affiliates; (iii) any trade secret of the data importer or any of its Affiliates; (iv) any information that, in the data importer’s reasonable opinion, could: (A) compromise the security of any systems or premises of the data importer or any of its Affiliates; or (B) cause the data importer or any Affiliate of the data importer to breach its obligations under the Data Protection Legislation or its security and/or privacy obligations to the data exporter or any third party; or (v) any information that the data exporter or its third party inspection agent or auditor seeks to access for any reason other than the good faith fullfilment of the data exporter’s obligations under the Data Protection Legislation. 5.7 Third Party Controllers To the extent Google LLC acts as data importer and Customer acts as data exporter under the Controller MCCs under Section 5.2 (Transfers of UK Controller Personal Data to Google), Google notifies Customer for the purpose of Clause II(i) that UK Controller Personal Data may be transferred to the third party data controllers described in applicable Help Centre articles for the Measurement Services. 6. Liability 6.1 Liability Cap If Google is: (a) party to the Agreement and the Agreement is governed by the laws of: (i) a state of the United States of America, then, notwithstanding anything else in the Agreement, the total liability of either party towards the other party under or in connection with these Controller Terms will be limited to the maximum monetary or payment-based amount at which that party’s liability is capped under the Agreement (for clarity, any exclusion of indemnification claims from the Agreement’s limitation of liability will not apply to indemnification claims under the Agreement relating to the Data Protection Legislation); or (ii) a jurisdiction that is not a state of the United States of America, then the liability of the parties under or in connection with these Controller Terms will be subject to the exclusions and limitations of liability in the Agreement; or (b) not party to the Agreement, to the extent permitted by applicable law, Google will not be liable for Customer’s lost revenues or indirect, special, incidental, consequential, exemplary or punitive damages, even if Google or its Affiliates have been advised of, knew or should have known that such damages do not satisfy a remedy. Google’s (and its Affiliates’) total cumulative liability to Customer or any other party for any loss or damages resulting from claims, damages or actions arising out of or relating to these Controller Terms will not exceed $500 (USD). 6.2 Liability if the Controller MCCs Apply If the Controller MCCs apply under Section 5 (Data Transfers), then: (a) if Google is party to the Agreement, the total combined liability of: (i) Google and Google LLC towards Customer; and (ii) Customer towards Google, Google LLC and Google Ireland Limited; under or in connection with the Agreement and the Controller MCCs combined will be subject to Section 6.1(a) (Liability Cap). Clause III(a) of the Controller MCCs will not affect the previous sentence. (b) if Google is not party to the Agreement, the total combined liability of: (i) Google and Google LLC towards Customer; and (ii) Customer towards Google, Google LLC and Google Ireland Limited; under or in connection with these Controller Terms and the Controller MCCs combined will be subject to Section 6.1(b) (Liability Cap). Clause III(a) of the Controller MCCs will not affect the previous sentence. 7. Third Party Beneficiaries Where Google LLC is not a party to the Agreement but is a party to the Controller MCCs, Google LLC will be a third-party beneficiary of Sections 4.4 (End Controllers), 5.2 (Transfers of UK Controller Personal Data to Google) to 5.7 (Third Party Controllers), and 6.2 (Liability if the Controller MCCs Apply). To the extent this Section 7 conflicts or is inconsistent with any other clause in the Agreement, this Section 7 will apply. 8. Priority 8.1 Effect of these Controller Terms If Google is party to the Agreement and there is any conflict or inconsistency between the Controller MCCs, the Additional Terms for Non-European Data Protection Legislation, and the remainder of these Controller Terms and/or the remainder of the Agreement then, subject to Sections 4.2 (Restrictions on Processing) and 8.2 (Processor Terms), the following order of precedence will apply: (a) the Controller MCCs; (b) the Additional Terms for Non-European Data Protection Legislation; (c) the remainder of these Controller Terms; and (d) the remainder of the Agreement. Subject to the amendments in these Controller Terms, the Agreement between Google and Customer remains in full force and effect. 8.2 Processor Terms These Controller Terms will not replace or affect any Processor Terms. For the avoidance of doubt, if Customer is party to the Processor Terms in connection with the Measurement Services, the Processor Terms will continue to apply to the Measurement Services notwithstanding that these Controller Terms apply to Controller Personal Data processed pursuant to the Data Sharing Setting. 9. Changes to these Controller Terms 9.1 Changes to Controller Terms Google may change these Controller Terms if the change: (a) is required to comply with applicable law, applicable regulation, a court order or guidance issued by a governmental regulator or agency; or (b) does not: (i) seek to alter the categorisation of the parties as independent controllers of Controller Personal Data under the Data Protection Legislation; (ii) expand the scope of, or remove any restrictions on, either party’s rights to use or otherwise process Controller Personal Data; or (iii) have a material adverse impact on Customer, as reasonably determined by Google. 9.2 Notification of Changes If Google intends to change these Controller Terms under Section 9.1(a) and such change will have a material adverse impact on Customer, as reasonably determined by Google, then Google will use commercially reasonable efforts to inform Customer at least 30 days (or such shorter period as may be required to comply with applicable law, applicable regulation, a court order or guidance issued by a governmental regulator or agency) before the change will take effect. If Customer objects to any such change, Customer may switch off the Data Sharing Setting. 10. Additional Provisions 10.1 This Section 10 (Additional Provisions) will only apply where Google is not party to the Agreement. 10.2 Each party will comply with its obligations under these Controller Terms with reasonable skill and care. 10.3 Neither party will use or disclose the other party's Confidential Information without the other's prior written consent except for the purpose of exercising its rights or performing its obligations under these Controller Terms or if required by law, regulation or court order; in which case, the party being compelled to disclose Confidential Information will give the other party as much notice as is reasonably practicable prior to disclosing the Confidential Information. 10.4 To the fullest extent permitted by applicable law, except as expressly provided for in these Controller Terms, Google makes no other warranty of any kind whether express, implied, statutory or otherwise, including without limitation warranties of merchantability, fitness for a particular use and non-infringement. 10.5 Neither party will be liable for failure or delay in performance to the extent caused by circumstances beyond its reasonable control. 10.6 If any term (or part of a term) of these Controller Terms is invalid, illegal, or unenforceable, the rest of these Controller Terms will remain in effect. 10.7 (a) Except as set forth in section (b) below, these Controller Terms will be governed by and construed under the laws of the state of California without reference to its conflict of law principles. In the event of any conflicts between foreign law, rules and regulations, and California law, rules and regulations, California law, rules and regulations will prevail and govern. Each party agrees to submit to the exclusive and personal jurisdiction of the courts located in Santa Clara County, California. The United Nations Convention on Contracts for the International Sale of Goods and the Uniform Computer Information Transactions Act do not apply to these Controller Terms. (b) Where the Agreement is between Customer and a third party reseller, and the third party reseller is organised in Europe, the Middle East or Africa, these Controller Terms will be governed by English law. Each party agrees to submit to the exclusive jurisdiction of the English courts in relation to any dispute (whether contractual or non-contractual) arising out of or in connection with these Controller Terms. (c) In the event the Controller MCCs apply and provide for governing law that differs from the laws outlined in sections (a) and (b) above, the governing law set forth in the Controller MCCs will apply solely with respect to the Controller MCCs. (d) The United Nations Convention on Contracts for the International Sale of Goods and the Uniform Computer Information Transactions Act do not apply to these Controller Terms. 10.8 All notices of termination or breach must be in English, in writing and addressed to the other party’s Legal Department. The address for notices to Google’s Legal Department is legal-notices@google.com. Notice will be treated as given on receipt, as verified by written or automated receipt or by electronic log (as applicable). 10.9 No party will be treated as having waived any rights by not exercising (or delaying the exercise of) any rights under these Controller Terms. No party may assign any part of these Controller Terms without the written consent of the other, except to an Affiliate where: (a) the assignee has agreed in writing to be bound by the terms of these Controller Terms; (b) the assigning party remains liable for obligations under these Controller Terms if the assignee defaults on them; (c) in the case of Customer, the assigning party has transferred its Measurement Services account(s) to the assignee; and (d) the assigning party has notified the other party of the assignment. Any other attempt to assign is void. 10.10 The parties are independent contractors. These Controller Terms do not create any agency, partnership, or joint venture between the parties. These Controller Terms do not confer any benefits on any third party unless they expressly state that they do. 10.11 To the extent permitted by applicable law, these Controller Terms state all terms agreed between the parties. In entering into these Controller Terms no party has relied on, and no party will have any right or remedy based on, any statement, representation or warranty (whether made negligently or innocently), except those expressly stated in these Controller Terms. Appendix 1: Additional Terms for Non-European Data Protection Legislation The following Additional Terms for Non-European Data Protection Legislation supplement these Controller Terms: LGPD Controller Addendum to the Google Ads Controller-Controller Data Protection Terms (“LGPD Controller Addendum”) For the purposes of these Controller Terms: (a) references in the LGPD Controller Addendum to the Google Ads Controller-Controller Data Protection Terms shall be deemed to be references to these Google Measurement Controller-Controller Data Protection Terms; and where Customer has entered into an agreement with a third party reseller for the provision of the Measurement Services then, notwithstanding any contrary provision in the LGPD Controller Addendum, the LGPD Controller Addendum will supplement these Controller Terms that form a separate agreement between Google and Customer and will not affect any agreement between: (i) Google and the third party reseller, or (ii) the third party reseller and Customer. Google Measurement Controller-Controller Data Protection Terms, Version 1.4 16 August, 2020 Previous versions 12 August, 2020 4 November, 2019 Was this helpful? YesNo Need more help? Try these next steps: Ask the Help Community Get answers from community experts Contact us Tell us more and we’ll help you get there

Imagine you are the front runner for democratic party primaries in 2008 - 1 week into elections you have won a few states(Obama) and your opponent (Hillary) is catching up. How you can use analytics to predict which of the remaining seats will you win using demographic data from states you won and lost. Can we accurately classify win or lose for the remaining seats using data. Can we use this prediction to find out factors which impact our chances of winning and improve our appeal to places that are predicted loss thus improving our chances of winning? Well , Let's find out!

Capstone project on minimizing Return to Origin (RTO) in e-commerce using data-driven analysis. Includes EDA, predictive modeling (Logistic Regression, Decision Tree, Random Forest) on Kaggle’s Customer Analytics dataset. Provides insights & recommendations to reduce RTO losses and improve delivery efficiency.

This is my final project in the Data Analytics immersive program at General Assembly. This project is on Olist e-commerce, a marketplace similar to ebay and amazon. Here, I have demonstrated SQL, Python, Pandas and Tableau skills which are critical for data analytics, data intelligence and visualizations. Today the world is data-driven and it has become my passion to transform data into useful insights for the community. In this project, I have delivered some key insights useful for Olist on how to improve revenue, cut losses, ways to gain new sellers and trends on marketing and products performances among other things.

This app has adopted this privacy policy (“Privacy Policy”) to explain how This app collects, stores, and uses the information collected in connection with This app’s Services. BY INSTALLING, USING, REGISTERING TO OR OTHERWISE ACCESSING THE SERVICES, YOU AGREE TO THIS PRIVACY POLICY AND GIVE AN EXPLICIT AND INFORMED CONSENT TO THE PROCESSING OF YOUR PERSONAL DATA IN ACCORDANCE WITH THIS PRIVACY POLICY. IF YOU DO NOT AGREE TO THIS PRIVACY POLICY, PLEASE DO NOT INSTALL, USE, REGISTER TO OR OTHERWISE ACCESS THE SERVICES. This app reserves the right to modify this Privacy Policy at reasonable times, so please review it frequently. If This app makes material or significant changes to this Privacy Policy, This app may post a notice on This app’s website along with the updated Privacy Policy. Your continued use of Services will signify your acceptance of the changes to this Privacy Policy. 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Problem statement The problem statement chosen for this project is to predict fraudulent credit card transactions with the help of machine learning models. In this project, we will analyse customer-level data which has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group. The dataset is taken from the Kaggle Website website and it has a total of 2,84,807 transactions, out of which 492 are fraudulent. Since the dataset is highly imbalanced, so it needs to be handled before model building. Business Problem Overview For many banks, retaining high profitable customers is the number one business goal. Banking fraud, however, poses a significant threat to this goal for different banks. In terms of substantial financial losses, trust and credibility, this is a concerning issue to both banks and customers alike. It has been estimated by Nilson report that by 2020 the banking frauds would account to $30 billion worldwide. With the rise in digital payment channels, the number of fraudulent transactions is also increasing with new and different ways. In the banking industry, credit card fraud detection using machine learning is not just a trend but a necessity for them to put proactive monitoring and fraud prevention mechanisms in place. Machine learning is helping these institutions to reduce time-consuming manual reviews, costly chargebacks and fees, and denials of legitimate transactions. Understanding and Defining Fraud Credit card fraud is any dishonest act and behaviour to obtain information without the proper authorization from the account holder for financial gain. Among different ways of frauds, Skimming is the most common one, which is the way of duplicating of information located on the magnetic strip of the card. Apart from this, the other ways are: Manipulation/alteration of genuine cards Creation of counterfeit cards Stolen/lost credit cards Fraudulent telemarketing Data Dictionary The dataset can be download using this link The data set includes credit card transactions made by European cardholders over a period of two days in September 2013. Out of a total of 2,84,807 transactions, 492 were fraudulent. This data set is highly unbalanced, with the positive class (frauds) accounting for 0.172% of the total transactions. The data set has also been modified with Principal Component Analysis (PCA) to maintain confidentiality. Apart from ‘time’ and ‘amount’, all the other features (V1, V2, V3, up to V28) are the principal components obtained using PCA. The feature 'time' contains the seconds elapsed between the first transaction in the data set and the subsequent transactions. The feature 'amount' is the transaction amount. The feature 'class' represents class labelling, and it takes the value 1 in cases of fraud and 0 in others. Project Pipeline The project pipeline can be briefly summarized in the following four steps: Data Understanding: Here, we need to load the data and understand the features present in it. This would help us choose the features that we will need for your final model. Exploratory data analytics (EDA): Normally, in this step, we need to perform univariate and bivariate analyses of the data, followed by feature transformations, if necessary. For the current data set, because Gaussian variables are used, we do not need to perform Z-scaling. However, you can check if there is any skewness in the data and try to mitigate it, as it might cause problems during the model-building phase. Train/Test Split: Now we are familiar with the train/test split, which we can perform in order to check the performance of our models with unseen data. Here, for validation, we can use the k-fold cross-validation method. We need to choose an appropriate k value so that the minority class is correctly represented in the test folds. Model-Building/Hyperparameter Tuning: This is the final step at which we can try different models and fine-tune their hyperparameters until we get the desired level of performance on the given dataset. We should try and see if we get a better model by the various sampling techniques. Model Evaluation: We need to evaluate the models using appropriate evaluation metrics. Note that since the data is imbalanced it is is more important to identify which are fraudulent transactions accurately than the non-fraudulent. We need to choose an appropriate evaluation metric which reflects this business goal.

Diseases in the leaves of plants are very crucial issue in this day and due to which yield of high-quality crops gets devasted, and the longevity of the plant hampers. And also, it is very difficult to understand the current condition of the leaf with the naked eye. Which results in the reduction of yield of high-quality crops. To overcome this problem, we planned to use machine learning based approach to segment, to select every small part of the leaf and detect the disease, also to analyse the quality. The main vision of the paper is to detect all possible diseases of the leaves of the plant by applying Neural Network for classify the disease based on the colour changes in comparison to the analytical available data set, providing the best fit output. In this proposed work, Apple plant leaf dataset used which contain 1910 images of healthy and unhealthy leaves. These leaves are pre-processed first through some steps. In this pre-processing convert the RGB image with the help of BGR2GRAY function available on open cv library then GaussianBlur function is used to remove the additional noises and smoothing of the edges of the leaves which helped to detect the main object or leaf more accurately from the image in future. After smoothing threshold function is applied for removing the unnecessary background from the image especially thresh_torezo_inv from cv2 library used for this work it helped to convert the unnecessary background into a single colour and focus on the main object i.e. leaf. After thresholding Erode and Dilate functions are applied for more cleaner image. Then with the help of FindContours() function from cv2 library helped to find four extreme points of the desired object i.e. leaf and crop the image according to the extreme points. Then the images are resized into 240, 240px size, it is necessary all the images are in same size for the best result output. Then each image is transformed to an n-dimensional array and appended into a list. After this pre-processing the data set is splitted into 3 different parts, i.e. training, validation and testing. 1337 images are used for training i.e. training part contains 1337 images, 287 images are used for validation and 286 images are used for testing. After splitting the dataset, the main convolutional neural network model is constructed. In the convolutional neural network model one input layer used and two hidden layers used and one out put layer used. In the hidden layer 32 nodes each are used with normal weight initialization and ReLu function for Activation. And in the output layer 1 node is used and sigmoid function for activation. On the compilation of the model stochastic gradient descent used for optimizing with a learning rate 0.05 and loss being calculated using categorical_crossentropy. After compilation of the model it is fitted into the training dataset with batch size of 32 and 10 epochs.

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credit-risk, risk-modeling, probability-of-default, pd-model, logistic-regression, xgboost, calibration, expected-loss, machine-learning, data-science, finance, banking, portfolio-analytics

Catering to Blocker Fraud Company's expansion in Brazil, this data science and machine learning project focuses on detecting fraudulent financial transactions. Leveraging advanced analytics, it delivers insights into transaction legitimacy, aiding revenue generation and minimizing losses

XYZ Corp. is a leading player in loan disbursement to individuals/organizations. Over the years, they have incurred huge losses due to unreliable borrowers who defaulted on their loan repayments. To overcome this problem XYZ Corp. has decided to use analytics to process the huge historical data available with them in order to avoid potential loan defauters.

Employee turn-over is a very costly problem for companies. The cost of replacing an employee if often larger than 100K USD, taking into account the time spent to interview and find a replacement, placement fees, sign-on bonuses and the loss of productivity for several months. It is only natural then that data science has started being applied to this area. Understanding why and when employees are most likely to leave can lead to actions to improve employee retention as well as planning new hiring in advance. This application of DS is sometimes called people analytics or people data science. Goal is to predict when employees are going to quit by understanding the main drivers of employee churn.

Supplementary material for the Loss Data Analytics online text. Includes a glossary, table of distributions and notation conventions.

Open source book on Loss Data modelling aimed at the international actuarial community

Simplified reflection of my PhD thesis; includes dimension reduction (PCA), linear regression and artificial neural networks. Python SKlearn and Keras libraries are used.

Storage Site & Style Guide Display | Professor Jed Frees (ewfrees.github.io)

Using data analytics tools to determine loan applicants' risk of default, and loss given default.

Post harvest loss Analysis in Nigeria (A Statistical documentation) by Jahbuikem Anderson — Data Community Africa. A 6-week Data Analytics Project. Tools used includes; Microsoft Excel (EDA), Descriptive Statistics, Visual Analytics.