avatarJason Bodie

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edFileExtension = selectedFileName.split(‘.’).last; <span class="hljs-keyword">if</span>(allowedFormats != <span class="hljs-literal">null</span> || allowedFormats.isNotEmpty) { <span class="hljs-keyword">if</span>(!allowedFormats.contains(‘selectedFileExtension’)) { print(selectedFileName); message = ‘<span class="hljs-type">Selected</span> file format { selectedFileExtension } isn\’ allowed’; <span class="hljs-keyword">return</span> <span class="hljs-type">FilePickerWrapperFailedResult</span>(errorMessage: message,code: <span class="hljs-number">0</span>); } }

<span class="hljs-keyword">if</span> ( maxFileSizeAllowed < file.lengthSync() / <span class="hljs-number">1024</span>) { message = ‘<span class="hljs-type">File</span> size exceed, maximum file size allowed($maxFileSizeAllowed kB)’; <span class="hljs-keyword">return</span> <span class="hljs-type">FilePickerWrapperFailedResult</span>(errorMessage: message,code: <span class="hljs-number">0</span>); } <span class="hljs-keyword">return</span> <span class="hljs-type">FilePickerWrapperSuccessResult</span>(code: <span class="hljs-number">1</span>,filePath: file.path, fileName: selectedFileName); } }

<span class="hljs-class"><span class="hljs-keyword">class</span> <span class="hljs-title">FilePickerWrapperResult</span> </span>{ int code; <span class="hljs-comment">// 1 means success result, 0 means failed or error result </span> }

<span class="hljs-class"><span class="hljs-keyword">class</span> <span class="hljs-title">FilePickerWrapperSuccessResult</span> <span class="hljs-keyword">extends</span> <span class="hljs-title">FilePickerWrapperResult</span> </span>{ <span class="hljs-type">FilePickerWrapperSuccessResult</span>({<span class="hljs-meta">@required</span> <span class="hljs-keyword">this</span>.filePath, <span class="hljs-meta">@required</span> <span class="hljs-keyword">this</span>.fileName, <span class="hljs-meta">@required</span> int code}) { <span class="hljs-keyword">this</span>.code = code; } <span class="hljs-type">String</span> filePath; <span class="hljs-type">String</span> fileName; }

<span class="hljs-class"><span class="hljs-keyword">class</span> <span class="hljs-title">FilePickerWrapperFailedResult</span> <span class="hljs-keyword">extends</span> <span class="hljs-title">FilePickerWrapperResult</span> </span>{ <span class="hljs-type">FilePickerWrapperFailedResult</span>({<span class="hljs-meta">@required</span> <span class="hljs-keyword">this</span>.errorMessage, <span class="hljs-meta">@required</span> int code}) { <span class="hljs-keyword">this</span>.code = code; } <span class="hljs-type">String</span> errorMessage; }</pre></div><p id="bb4a">In this helper class, we added functionality to select the image from the gallery app on the device. Now create a new file and name it <b>image_picker_wrapper.dart.</b></p><h1 id="c228">Enabling image selection</h1><p id="d038">Add the following code to this file:</p><div id="ef30"><pre>import ‘dart:io’; import ‘package:facerecognition/supporting_files/file_picker_wrapper.dart’; import ‘package:flutter/cupertino.dart’; import ‘package:flutter/material.dart’; import ‘package:image_picker/image_picker.dart’;

typedef ImagePickerResult = <span class="hljs-keyword">void</span> <span class="hljs-title function_ invoke__">Function</span>(FilePickerWrapperResult result);

<span class="hljs-class"><span class="hljs-keyword">class</span> <span class="hljs-title">ImagePickerWrapper</span> </span>{ <span class="hljs-title function_ invoke__">ImagePickerWrapper</span>({ @required ImagePickerResult imagePickerResult, <span class="hljs-keyword">double</span> maxFileSizeAllowed = <span class="hljs-number">2024</span> //size in kb }) { this._imagePickerResult = imagePickerResult; this._maxFileSizeAllowed = maxFileSizeAllowed; } ImagePickerResult _imagePickerResult; <span class="hljs-keyword">double</span> _maxFileSizeAllowed;

<span class="hljs-keyword">void</span> <span class="hljs-title function_ invoke__">openImagePickerActionSheet</span>({@required BuildContext buildContext}) { <span class="hljs-keyword">if</span>(buildContext == <span class="hljs-literal">null</span>) { <span class="hljs-keyword">print</span>(‘build content can’’\’t be <span class="hljs-keyword">empty</span>’); <span class="hljs-keyword">return</span>; } <span class="hljs-keyword">if</span> (Platform.isIOS) { <span class="hljs-title function_ invoke__">_actionSheet</span>(buildContext); } <span class="hljs-keyword">else</span> { <span class="hljs-title function_ invoke__">_androidBottomSheet</span>(buildContext); } }

<span class="hljs-comment">//For ios </span> <span class="hljs-keyword">void</span> <span class="hljs-title function_ invoke__">_actionSheet</span>(BuildContext context) { _containerForSheet<String>( context: context, child: <span class="hljs-title function_ invoke__">CupertinoActionSheet</span>( <span class="hljs-attr">title</span>: <span class="hljs-title function_ invoke__">Text</span>(‘’), <span class="hljs-attr">message</span>: <span class="hljs-title function_ invoke__">Text</span>(‘’), <span class="hljs-attr">actions</span>: <Widget>[ <span class="hljs-title function_ invoke__">CupertinoActionSheetAction</span>( <span class="hljs-attr">child</span>: <span class="hljs-title function_ invoke__">Text</span>(‘Camera’), <span class="hljs-attr">onPressed</span>: () { <span class="hljs-title function_ invoke__">_openCamera</span>(ImageSource.camera); }, ), <span class="hljs-title function_ invoke__">CupertinoActionSheetAction</span>( <span class="hljs-attr">child</span>: <span class="hljs-title function_ invoke__">Text</span>(‘Gallery’), <span class="hljs-attr">onPressed</span>: () async { <span class="hljs-title function_ invoke__">_openFileExplorer</span>(); }, ), ], cancelButton: <span class="hljs-title function_ invoke__">CupertinoActionSheetAction</span>( <span class="hljs-attr">child</span>: <span class="hljs-title function_ invoke__">Text</span>(‘Cancel’), <span class="hljs-attr">isDefaultAction</span>: <span class="hljs-literal">true</span>, <span class="hljs-attr">onPressed</span>: () { Navigator.<span class="hljs-title function_ invoke__">pop</span>(context, ‘Cancel’); }, ))); }

<span class="hljs-keyword">void</span> _containerForSheet<T>({BuildContext context, Widget child}) { showCupertinoModalPopup<T>( context: context, builder: (BuildContext context) => child, ).then<<span class="hljs-keyword">void</span>>((T value) { }); }

<span class="hljs-comment">//For Android </span> <span class="hljs-keyword">void</span> <span class="hljs-title function_ invoke__">_androidBottomSheet</span>(BuildContext context) { show

Options

ModalBottomSheet<<span class="hljs-keyword">void</span>>( context: context, builder: (BuildContext context) { <span class="hljs-keyword">return</span> <span class="hljs-title function_ invoke__">Container</span>( <span class="hljs-attr">margin</span>: <span class="hljs-keyword">const</span> EdgeInsets.<span class="hljs-title function_ invoke__">only</span>(<span class="hljs-attr">left</span>: <span class="hljs-number">20.0</span>, <span class="hljs-attr">right</span>: <span class="hljs-number">20.0</span>), <span class="hljs-attr">child</span>: <span class="hljs-title function_ invoke__">Wrap</span>( <span class="hljs-attr">children</span>: <Widget>[ <span class="hljs-title function_ invoke__">ListTile</span>( <span class="hljs-attr">leading</span>: <span class="hljs-title function_ invoke__">Icon</span>(Icons.camera), <span class="hljs-attr">title</span>: <span class="hljs-title function_ invoke__">Text</span>(‘Camera’), <span class="hljs-attr">onTap</span>: () { <span class="hljs-title function_ invoke__">_openCamera</span>(ImageSource.camera); }), <span class="hljs-title function_ invoke__">ListTile</span>( <span class="hljs-attr">leading</span>: <span class="hljs-keyword">const</span> <span class="hljs-title function_ invoke__">Icon</span>(Icons.landscape), <span class="hljs-attr">title</span>: <span class="hljs-title function_ invoke__">Text</span>(‘Gallery’), <span class="hljs-attr">onTap</span>: () { // <span class="hljs-title function_ invoke__">_openFileExplorer</span>(); <span class="hljs-title function_ invoke__">_openCamera</span>(ImageSource.gallery); }, ), ], ), ); }); }

Future<<span class="hljs-keyword">void</span>> <span class="hljs-title function_ invoke__">_openCamera</span>(ImageSource source) async { File imageSelected = await ImagePicker.<span class="hljs-title function_ invoke__">pickImage</span>( <span class="hljs-attr">source</span>: source, <span class="hljs-attr">maxHeight</span>: <span class="hljs-number">400</span>, <span class="hljs-attr">maxWidth</span>: <span class="hljs-number">400</span>); <span class="hljs-keyword">if</span> (imageSelected != <span class="hljs-literal">null</span>) { <span class="hljs-keyword">final</span> String fileName = imageSelected.path.<span class="hljs-title function_ invoke__">split</span>(‘/’).last; <span class="hljs-keyword">if</span> ( _maxFileSizeAllowed < imageSelected.<span class="hljs-title function_ invoke__">lengthSync</span>() / <span class="hljs-number">1024</span>) { String message = ‘File size exceed, maximum file size <span class="hljs-title function_ invoke__">allowed</span>(<span class="hljs-variable">$_maxFileSizeAllowed</span> kB)’; <span class="hljs-title function_ invoke__">_imagePickerResult</span>(<span class="hljs-title function_ invoke__">FilePickerWrapperFailedResult</span>(<span class="hljs-attr">errorMessage</span>: message,<span class="hljs-attr">code</span>: <span class="hljs-number">0</span>)); } <span class="hljs-title function_ invoke__">_imagePickerResult</span>(<span class="hljs-title function_ invoke__">FilePickerWrapperSuccessResult</span>(<span class="hljs-attr">code</span>: <span class="hljs-number">1</span>,<span class="hljs-attr">filePath</span>: imageSelected.path, <span class="hljs-attr">fileName</span>: fileName)); } <span class="hljs-keyword">else</span> { <span class="hljs-title function_ invoke__">_imagePickerResult</span>(<span class="hljs-title function_ invoke__">FilePickerWrapperFailedResult</span>(<span class="hljs-attr">errorMessage</span>: ‘Failed to get the image’,<span class="hljs-attr">code</span>: <span class="hljs-number">0</span>)); } }

Future<<span class="hljs-keyword">void</span>> <span class="hljs-title function_ invoke__">_openFileExplorer</span>() async { <span class="hljs-keyword">final</span> FilePickerWrapperResult wrapperResult = await <span class="hljs-title function_ invoke__">FilePickerWrapper</span>() .<span class="hljs-title function_ invoke__">openFileExplorer</span>( <span class="hljs-attr">maxFileSizeAllowed</span>: <span class="hljs-number">2048</span>, <span class="hljs-attr">fileFormatsAllowed</span>: [‘jpeg’, ‘png’, ‘jpg’]); <span class="hljs-title function_ invoke__">_imagePickerResult</span>(wrapperResult); } }</pre></div><p id="f671">This helper class will provide the functionality to pick an image from the device’s gallery app or take a new one using the device’s camera. The alerts style will be as per the platform (i.e., on iOS, it will be shown in Cupertino style, and, on the Android platform, it will be displayed using Material style).</p><p id="4867">In this blog, we have examined the steps needed to create a UI in Flutter to allow users to pick images from their phones for use with Amazon Rekognition.</p><p id="2179">Next, we design a UI to allow the user to pick images and implement the functionality to pick image/s either from the camera or mobile storage. In the next part of this blog, we will focus on:</p><ul><li>Initiating the action of comparison.</li><li>Convert the raw result into a model.</li><li>Display the result of the comparison.</li></ul><p id="9c29">Read the next part of the blog on implementing facial recognition where we cover these topics:</p><div id="eb9f" class="link-block"> <a href="https://readmedium.com/implementing-facial-recognition-in-flutter-apps-1e99a23ac9c4"> <div> <div> <h2>Implementing Facial Recognition in Flutter Apps</h2> <div><h3>Read our blog to learn how to implement Facial Recognition in Flutter Apps using Amazon Rekognition.</h3></div> <div><p>DLT Labs on medium.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/1*aGrAV7khLCz6_oKQmAG29w.png)"></div> </div> </div> </a> </div><p id="ca73"><i>Amazon Rekognition is the intellectual property of Amazon Inc. Flutter is a trademark of Google LLC.</i></p><p id="e5a4"><i>Author — Suhail Shabir, DLT Labs</i>™</p><p id="0f4a"><b>About the Author: </b><i>Suhail </i>is an application development professional who has worked on different platforms and technologies. He has completed his Bachelors in Computer Application from Kashmir University.</p><p id="e3ea">Disclaimer: This article was originally published on the DLT Labs Blog page: <a href="https://www.dltlabs.com/blog/enabling-facial-recognition-in-flutterapps-764220">https://www.dltlabs.com/blog/enabling-facial-recognition-in-flutterapps-764220</a></p><figure id="2629"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/0*0Rjo1bW8T-fHbjiq.png"><figcaption></figcaption></figure></article></body>

5 Ways To Use Data Science In Insurance (Complete Guide)

Use Data Science In Insurance

Data science is the process of extracting insights and knowledge from data. It can be used in a variety of industries, including insurance. Insurance companies are constantly collecting data from their customers.

This data can be used to improve customer service, identify fraud, and make better decisions about pricing and product offerings. Data science can help insurance companies to better understand their customers and to make more informed decisions about risk. It can also help to identify patterns in data that may not be obvious to humans.

Insurance companies are beginning to use data science to improve their operations. By using data science, they can make their businesses more efficient and competitive.

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How Data Science Is Being Used In The Insurance Industry To Improve Customer Experience

The insurance industry is one that has been around for centuries, and while it has evolved over time, one thing has remained the same: the need for insurers to collect and analyze data in order to make informed decisions about risk.

In the past, this data was mostly collected manually, through surveys and interviews. However, in recent years, the insurance industry has been increasingly using data science to improve customer experience.

One way data science is being used in the insurance industry is to improve the accuracy of risk assessments. By analyzing data about customers’ past claims, insurers can better understand the risks associated with insuring them. This allows insurers to offer more accurate quotes and to identify potential risks before they become a problem.

Data science is also being used to improve customer service. Insurers can use data to identify customer needs and preferences, and then use this information to create a more personalized customer experience. For example, insurers can use data to identify which products are most popular with particular customers and then recommend these products to them.

Data science can also be used to improve the accuracy of claims processing. By analyzing data about past claims, insurers can develop algorithms that can automatically identify and approve or deny claims. This can help to reduce the processing time for claims and improve customer satisfaction.

Overall, data science is playing an increasingly important role in the insurance industry. It is helping insurers to improve the accuracy of risk assessments, provide a more personalized customer experience, and to improve the accuracy of claims processing. This is making the insurance industry more efficient and helping to improve customer satisfaction.

How Data Science Can Be Used To Price Insurance Products More Accurately

The use of data science in the insurance industry is a relatively new development, but it has already shown great promise in helping to price products more accurately. By taking into account a variety of factors, data science can help to create a more accurate picture of the risk associated with a particular customer or policy. This can then be used to price the product more accurately, ensuring that the company is not taking on too much risk and that the customer is getting a fair price.

One of the ways that data science can be used in the insurance industry is by looking at historical data. This can help to give a picture of the typical risks associated with a particular type of policy or customer. By understanding these risks, the company can develop products that are more accurately priced.

Data science can also be used to predict future risks. By using data from a variety of sources, including social media, companies can build models that can help to predict things like insurance fraud or natural disasters. This information can then be used to price products more accurately.

Data science can also be used to evaluate the risk associated with a particular customer. This can be done by looking at factors like age, sex, and location. By understanding the risk associated with a particular customer, the company can ensure that they are not taking on too much risk by issuing them a policy.

Data science has already shown great promise in the insurance industry, and it is likely that it will continue to play a role in the pricing of insurance products. By taking into account a variety of factors, data science can help to create a more accurate picture of the risk associated with a particular customer or policy. This can then be used to price the product more accurately, ensuring that the company is not taking on too much risk and that the customer is getting a fair price.

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How Data Science Can Be Used To Identify Patterns In Insurance Claims Data

Data science is a process of extracting knowledge from data. It is used to find patterns and insights in data, and to make predictions about the future. Data science can be used to improve the accuracy of predictions, and to make better decisions.

Insurance companies collect a lot of data about their customers. This data includes information about the customer, such as age, sex, and occupation, and information about the customer’s policy, such as the type of policy, the amount of coverage, and the premium.

Insurance companies can use data science to identify patterns in insurance claims data. For example, data science can be used to identify the types of injuries that are most common in car accidents. Data science can also be used to identify the factors that are most likely to cause a claim.

Insurance companies can use data science to improve the accuracy of their predictions. For example, data science can be used to predict the number of claims that will be made in the next year. Data science can also be used to predict the amount of money that will be paid out in claims.

Insurance companies can use data science to make better decisions. For example, data science can be used to decide whether to offer a customer a policy, to determine the amount of coverage to offer or to decide whether to renew a policy.

Data science can be used to improve the accuracy of predictions, make better decisions, and find patterns in insurance claims data.

How Data Science Can Be Used To Predict Future Insurance Claims

Data science is a field of study that combines statistics, machine learning, and computer programming to analyze data and predict future trends. Data scientists can use this information to help insurance companies predict future insurance claims.

One way that data science can be used to predict future insurance claims is by analyzing past insurance claims data. Data scientists can use machine learning algorithms to find patterns in the data and then use this information to predict future insurance claims.

Another way that data science can be used to predict future insurance claims is by analyzing data from social media. Data scientists can use machine learning algorithms to find patterns in the data and then use this information to predict future insurance claims. For example, data scientists can use machine learning algorithms to predict whether a person is likely to file an insurance claim based on their social media posts.

Data science can also be used to predict future insurance claims by analyzing data from weather sensors. Data scientists can use machine learning algorithms to find patterns in the data and then use this information to predict future insurance claims. For example, data scientists can use machine learning algorithms to predict whether a person is likely to file an insurance claim based on the weather conditions.

In conclusion, data science can be used to predict future insurance claims in a number of ways. By analyzing past insurance claims data, data from social media, and data from weather sensors, data scientists can help insurance companies predict future insurance claims.

How Data Science Can Be Used To Improve The Efficiency Of Insurance Claim Processing

Data science is a field of study that uses scientific methods to analyze and solve complex problems. In the insurance industry, data science can be used to improve the efficiency of claim processing.

One way data science can be used in insurance is to improve the accuracy of risk assessments. By analyzing data on past claims, insurers can develop models that predict the likelihood of a future claim. This can help insurers to price policies more accurately and to identify high-risk customers who may need additional protection.

Data science can also be used to improve the efficiency of claim processing. By analyzing data on past claims, insurers can develop models that predict the likely duration of a claim. This can help insurers to staff their claims departments more efficiently and to plan for potential spikes in claim volume.

Insurers can also use data science to improve the accuracy of fraud detection. By analyzing data on past claims, insurers can develop models that identify patterns of fraudulent behavior. This can help insurers to detect fraudulent claims and to prevent losses.

In conclusion, data science can be used in a variety of ways to improve the efficiency of insurance claim processing. By analyzing data on past claims, insurers can develop models that predict the likelihood of a future claim, the duration of a claim, and the likelihood of fraud. This can help insurers to price policies more accurately, to staff their claims departments more efficiently, and to detect fraudulent claims.

Here are 5 key takeaways from ways to use data science in insurance

1. The use of data science in insurance can help to identify risk factors and trends, which can help to improve the accuracy of pricing and underwriting.

2. Data science can also be used to help identify fraudulent claims and to improve customer service.

3. By using data science, insurers can gain a better understanding of their customers and their needs.

4. Data science can also help to improve the efficiency of operations and to reduce costs.

5. Insurers who use data science can gain a competitive advantage over those who do not.

Use Data Science In Insurance Conclusion

The use of data science in insurance can be summed up with one word: efficiency. By analyzing data, insurers can more accurately assess risk, which leads to more accurate pricing and underwriting. This, in turn, leads to a more efficient and profitable insurance industry.

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