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class="work-thumbnail" src="https://attachments.academia-assets.com/118138108/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/123786942/Determining_the_feasibility_of_using_the_automated_market_basket_analysis_method_to_investigate_a_cause_and_effect_pattern_of_construction_accidents_and_its_safety_associations">Determining the feasibility of using the automated market basket analysis method to investigate a cause-and-effect pattern of construction accidents and its safety associations</a></div><div class="wp-workCard_item"><span>Internet of Things and Artificial Intelligence Journal</span><span>, Aug 8, 2023</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4491bdf54842afc5ba2a435bc3b0126a" 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Vasista","url":"https://king-saud.academia.edu/TgkVasista"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":472,"name":"Human Computer Interaction","url":"https://www.academia.edu/Documents/in/Human_Computer_Interaction"},{"id":5673,"name":"Augmented Reality","url":"https://www.academia.edu/Documents/in/Augmented_Reality"},{"id":21843,"name":"Metaverse","url":"https://www.academia.edu/Documents/in/Metaverse"},{"id":30947,"name":"The Internet","url":"https://www.academia.edu/Documents/in/The_Internet"},{"id":44244,"name":"OPERATING SYSTEM","url":"https://www.academia.edu/Documents/in/OPERATING_SYSTEM"},{"id":50642,"name":"Virtual Reality","url":"https://www.academia.edu/Documents/in/Virtual_Reality"},{"id":126300,"name":"Big Data","url":"https://www.academia.edu/Documents/in/Big_Data"},{"id":141114,"name":"World Wide 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wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/123786940/Prediction_of_Sales_Based_on_an_Effective_Advertising_Media_Sale_Data_A_Python_Implementation_Approach">Prediction of Sales Based on an Effective Advertising Media Sale Data: A Python Implementation Approach</a></div><div class="wp-workCard_item"><span>Academy of Strategic Management Journal</span><span>, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="de4bd3681b0679f5f553df46a1779bb8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118138106,"asset_id":123786940,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118138106/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa 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})(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "de4bd3681b0679f5f553df46a1779bb8" } } $('.js-work-strip[data-work-id=123786940]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":123786940,"title":"Prediction of Sales Based on an Effective Advertising Media Sale Data: A Python Implementation Approach","translated_title":"","metadata":{"publisher":"Allied Academies","grobid_abstract":"Sales forecasting is an essential task in the retail management field. Intelligent forecasting using machine learning techniques can help discover the selection of feature variables that influence prediction of sales growth. A Python program implementation is adopted to compute; develop and visualizing forecasting model of historical sales data based on advertising media opted to do the effective sales promotion. Python supports working on predictive algorithms through accessing from Python libraries. For this purpose, it relies on the past observations based transaction data set file as an input to produce output without worrying about the underlying mechanism. The results indicated that TV media is the feature variable that influences the prediction of sales of linear regression model. Regression results of OLS model type are displayed with coefficient values to substitute in the linear regression equation. 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Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":43619,"name":"Feature Selection","url":"https://www.academia.edu/Documents/in/Feature_Selection"},{"id":3336228,"name":"python programming language","url":"https://www.academia.edu/Documents/in/python_programming_language"}],"urls":[{"id":44614138,"url":"https://www.abacademies.org/articles/prediction-of-sales-based-on-an-effective-advertising-media-sale-data-a-python-implementation-approach.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="123786939"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/123786939/Predicting_Telecom_Customer_Churn_An_Example_of_Implementing_in_Python"><img alt="Research paper thumbnail of Predicting Telecom Customer Churn: An Example of Implementing in Python" class="work-thumbnail" src="https://attachments.academia-assets.com/118138104/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/123786939/Predicting_Telecom_Customer_Churn_An_Example_of_Implementing_in_Python">Predicting Telecom Customer Churn: An Example of Implementing in Python</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The telecom industry is facing fierce competition. Customer retention is becoming a real challeng...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The telecom industry is facing fierce competition. Customer retention is becoming a real challenge. Telecom companies do not want their customers to leave them and look for other service providers. Thus addressing customer churn is becoming a problem. This paper determines the customer churn percentage for a given case of transaction data set as a secondary data. The objective of this research is to identify the probability of customer churn using predictive analytics technique using logistic regression model in order to assess the tendency of probability of customer churn. The result of model accuracy got is 0.8. Based on the existing telecom case study data, customer churn percentage is determined as shown in the graph in the main body of the paper and weighting factors of model function are computed using Python programming language and its libraries.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7da15df1b94c888f634633de24471e19" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118138104,"asset_id":123786939,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118138104/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="123786939"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="123786939"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123786939; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=123786939]").text(description); $(".js-view-count[data-work-id=123786939]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 123786939; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='123786939']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 123786939, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "7da15df1b94c888f634633de24471e19" } } $('.js-work-strip[data-work-id=123786939]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":123786939,"title":"Predicting Telecom Customer Churn: An Example of Implementing in Python","translated_title":"","metadata":{"abstract":"The telecom industry is facing fierce competition. Customer retention is becoming a real challenge. Telecom companies do not want their customers to leave them and look for other service providers. Thus addressing customer churn is becoming a problem. This paper determines the customer churn percentage for a given case of transaction data set as a secondary data. The objective of this research is to identify the probability of customer churn using predictive analytics technique using logistic regression model in order to assess the tendency of probability of customer churn. The result of model accuracy got is 0.8. Based on the existing telecom case study data, customer churn percentage is determined as shown in the graph in the main body of the paper and weighting factors of model function are computed using Python programming language and its libraries.","publication_date":{"day":null,"month":null,"year":2021,"errors":{}}},"translated_abstract":"The telecom industry is facing fierce competition. Customer retention is becoming a real challenge. Telecom companies do not want their customers to leave them and look for other service providers. Thus addressing customer churn is becoming a problem. This paper determines the customer churn percentage for a given case of transaction data set as a secondary data. The objective of this research is to identify the probability of customer churn using predictive analytics technique using logistic regression model in order to assess the tendency of probability of customer churn. The result of model accuracy got is 0.8. Based on the existing telecom case study data, customer churn percentage is determined as shown in the graph in the main body of the paper and weighting factors of model function are computed using Python programming language and its 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data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/123786933/Benefits_Barriers_and_Applications_of_Information_Communication_Technology_in_Construction_Industry_a_Contemporary_Study"><img alt="Research paper thumbnail of Benefits, Barriers and Applications of Information Communication Technology in Construction Industry: a Contemporary Study" class="work-thumbnail" src="https://attachments.academia-assets.com/118138095/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/123786933/Benefits_Barriers_and_Applications_of_Information_Communication_Technology_in_Construction_Industry_a_Contemporary_Study">Benefits, Barriers and Applications of Information Communication Technology in Construction Industry: a Contemporary Study</a></div><div class="wp-workCard_item"><span>International Journal of Engineering &amp; Technology</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the construction industry there is a necessity for improving the construction efficiency throu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In the construction industry there is a necessity for improving the construction efficiency through adopting innovative methods. Information and Communication Technologies (ICT) help in increasing the client satisfaction reduce coordination errors and provide a greater understanding among project participants in terms of better handling issues and requirements. Better efficiency can be achieved by making a building smarter. Construction projects are faced with many challenges that must not be ignored or underestimated. Some of the wicked problems and challenges such as understanding how one decision will impact decisions in other areas; problem of addressing the risk due to dynamic and uncertainty existing in the environment; resolving conflict where there are competing claims resulting both good and bad outcomes from the same value system are required to be well managed, if required using innovative methods such as using ICT and software packages. Therefore Objective of this resear...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2f157d170063c5d0fd1d80ec8c254a71" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118138095,"asset_id":123786933,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118138095/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="123786933"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="123786933"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123786933; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=123786933]").text(description); $(".js-view-count[data-work-id=123786933]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 123786933; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='123786933']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 123786933, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2f157d170063c5d0fd1d80ec8c254a71" } } $('.js-work-strip[data-work-id=123786933]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":123786933,"title":"Benefits, Barriers and Applications of Information Communication Technology in Construction Industry: a Contemporary Study","translated_title":"","metadata":{"abstract":"In the construction industry there is a necessity for improving the construction efficiency through adopting innovative methods. Information and Communication Technologies (ICT) help in increasing the client satisfaction reduce coordination errors and provide a greater understanding among project participants in terms of better handling issues and requirements. Better efficiency can be achieved by making a building smarter. Construction projects are faced with many challenges that must not be ignored or underestimated. Some of the wicked problems and challenges such as understanding how one decision will impact decisions in other areas; problem of addressing the risk due to dynamic and uncertainty existing in the environment; resolving conflict where there are competing claims resulting both good and bad outcomes from the same value system are required to be well managed, if required using innovative methods such as using ICT and software packages. Therefore Objective of this resear...","publisher":"Science Publishing Corporation","ai_title_tag":"ICT in Construction: Benefits, Barriers, and Applications","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"International Journal of Engineering \u0026amp; Technology"},"translated_abstract":"In the construction industry there is a necessity for improving the construction efficiency through adopting innovative methods. Information and Communication Technologies (ICT) help in increasing the client satisfaction reduce coordination errors and provide a greater understanding among project participants in terms of better handling issues and requirements. Better efficiency can be achieved by making a building smarter. Construction projects are faced with many challenges that must not be ignored or underestimated. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="92761631"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/92761631/Applications_of_Emerging_Technological_Models_in_Smart_City_Construction"><img alt="Research paper thumbnail of Applications of Emerging Technological Models in Smart City Construction" class="work-thumbnail" src="https://attachments.academia-assets.com/95685107/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/92761631/Applications_of_Emerging_Technological_Models_in_Smart_City_Construction">Applications of Emerging Technological Models in Smart City Construction</a></div><div class="wp-workCard_item"><span>High Technology Letters</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The construction industry is considered as one of the key sectors and a tool to manage the nation...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The construction industry is considered as one of the key sectors and a tool to manage the national economy by the government as it has a strong linkage with other industries. The construction investment indicates the growth and creation of employment and is recognized as an important public policy tool. Currently Government of India is on its way to invest on many large construction projects. Large construction projects are powerful strategic weapons that are creating economic value, but are inherently complex and dynamic. Delay in large project construction has been becoming very usual across many developing countries and reflecting a national waste. Completing huge and complex construction projects by considering more important features of project management has been becoming a challenge. When the construction industry is facing with numerous challenges including low productivity, lack of research and development, poor technology advancements, the concept of digital twin as a model for data driven management is believed to provide a suitable solution, especially when combined with emerging strategies such as lean construction and emerging technologies such as Building Information Modeling, IoT, Big data analytics, cloud & edge computing and automated & intelligent management systems etc. can provide many useful solutions especially in case of huge construction kind of projects like smart city construction planning and management. It is also concluded that a Digital Triplet Architectural Model can be proposed to be made adoptable with the idea of using the above mentioned emerging technologies in the construction of Smart Cities.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9057755ac43badc2f0e33ca2eed78fac" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":95685107,"asset_id":92761631,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/95685107/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="92761631"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="92761631"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 92761631; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=92761631]").text(description); $(".js-view-count[data-work-id=92761631]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 92761631; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='92761631']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 92761631, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "9057755ac43badc2f0e33ca2eed78fac" } } $('.js-work-strip[data-work-id=92761631]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":92761631,"title":"Applications of Emerging Technological Models in Smart City Construction","translated_title":"","metadata":{"abstract":"The construction industry is considered as one of the key sectors and a tool to manage the national economy by the government as it has a strong linkage with other industries. 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When the construction industry is facing with numerous challenges including low productivity, lack of research and development, poor technology advancements, the concept of digital twin as a model for data driven management is believed to provide a suitable solution, especially when combined with emerging strategies such as lean construction and emerging technologies such as Building Information Modeling, IoT, Big data analytics, cloud \u0026 edge computing and automated \u0026 intelligent management systems etc. can provide many useful solutions especially in case of huge construction kind of projects like smart city construction planning and management. It is also concluded that a Digital Triplet Architectural Model can be proposed to be made adoptable with the idea of using the above mentioned emerging technologies in the construction of Smart Cities.","publication_date":{"day":null,"month":null,"year":2022,"errors":{}},"publication_name":"High Technology Letters"},"translated_abstract":"The construction industry is considered as one of the key sectors and a tool to manage the national economy by the government as it has a strong linkage with other industries. The construction investment indicates the growth and creation of employment and is recognized as an important public policy tool. Currently Government of India is on its way to invest on many large construction projects. Large construction projects are powerful strategic weapons that are creating economic value, but are inherently complex and dynamic. Delay in large project construction has been becoming very usual across many developing countries and reflecting a national waste. Completing huge and complex construction projects by considering more important features of project management has been becoming a challenge. When the construction industry is facing with numerous challenges including low productivity, lack of research and development, poor technology advancements, the concept of digital twin as a model for data driven management is believed to provide a suitable solution, especially when combined with emerging strategies such as lean construction and emerging technologies such as Building Information Modeling, IoT, Big data analytics, cloud \u0026 edge computing and automated \u0026 intelligent management systems etc. can provide many useful solutions especially in case of huge construction kind of projects like smart city construction planning and management. It is also concluded that a Digital Triplet Architectural Model can be proposed to be made adoptable with the idea of using the above mentioned emerging technologies in the construction of Smart Cities.","internal_url":"https://www.academia.edu/92761631/Applications_of_Emerging_Technological_Models_in_Smart_City_Construction","translated_internal_url":"","created_at":"2022-12-12T23:17:27.727-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":805201,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":95685107,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/95685107/thumbnails/1.jpg","file_name":"1_june2022_HTL.pdf","download_url":"https://www.academia.edu/attachments/95685107/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Applications_of_Emerging_Technological_M.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95685107/1_june2022_HTL-libre.pdf?1670917856=\u0026response-content-disposition=attachment%3B+filename%3DApplications_of_Emerging_Technological_M.pdf\u0026Expires=1733098142\u0026Signature=U5CijTNkdJiVe7KZz3hhRUV-IayUuttqb9RFb3sYBCymgT0FqSrN8rwFzRenvRY~sFV9GzBwOsIsgU7GH9bnoDPKvfUqlzZ5ZDQjqpM9PuyGM7d8XZdEsZLEpYKiKQrav0ajAG2IUDJA~NEZXStr~MHgtivMbBbtINH2at-NI4dM24J-ua2QLVSl9dr2nkavmk-4IoUL5EIXvPdyQSoma0383dMPrI1LpsLzvt~1~3o3cQWFXabdH8m4NEnWOhxLsvJi1dAPUPUljF5HKOPkEQr7yjy395Sh8M6CfL8tyf7xvtKftcUsM4V~XceGw3DUJJyWeaulXuCCCO7HHzyusg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Applications_of_Emerging_Technological_Models_in_Smart_City_Construction","translated_slug":"","page_count":11,"language":"en","content_type":"Work","owner":{"id":805201,"first_name":"Tgk","middle_initials":null,"last_name":"Vasista","page_name":"TgkVasista","domain_name":"king-saud","created_at":"2011-10-01T22:36:22.310-07:00","display_name":"Tgk Vasista","url":"https://king-saud.academia.edu/TgkVasista"},"attachments":[{"id":95685107,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/95685107/thumbnails/1.jpg","file_name":"1_june2022_HTL.pdf","download_url":"https://www.academia.edu/attachments/95685107/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Applications_of_Emerging_Technological_M.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95685107/1_june2022_HTL-libre.pdf?1670917856=\u0026response-content-disposition=attachment%3B+filename%3DApplications_of_Emerging_Technological_M.pdf\u0026Expires=1733098142\u0026Signature=U5CijTNkdJiVe7KZz3hhRUV-IayUuttqb9RFb3sYBCymgT0FqSrN8rwFzRenvRY~sFV9GzBwOsIsgU7GH9bnoDPKvfUqlzZ5ZDQjqpM9PuyGM7d8XZdEsZLEpYKiKQrav0ajAG2IUDJA~NEZXStr~MHgtivMbBbtINH2at-NI4dM24J-ua2QLVSl9dr2nkavmk-4IoUL5EIXvPdyQSoma0383dMPrI1LpsLzvt~1~3o3cQWFXabdH8m4NEnWOhxLsvJi1dAPUPUljF5HKOPkEQr7yjy395Sh8M6CfL8tyf7xvtKftcUsM4V~XceGw3DUJJyWeaulXuCCCO7HHzyusg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":489384,"name":"Urban Planning, Smart Cities","url":"https://www.academia.edu/Documents/in/Urban_Planning_Smart_Cities"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="92760933"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/92760933/Applications_of_Emerging_Technological_Models_in_Smart_City_Construction"><img alt="Research paper thumbnail of Applications of Emerging Technological Models in Smart City Construction" class="work-thumbnail" src="https://attachments.academia-assets.com/95684555/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/92760933/Applications_of_Emerging_Technological_Models_in_Smart_City_Construction">Applications of Emerging Technological Models in Smart City Construction</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://king-saud.academia.edu/TgkVasista">Tgk Vasista</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/DRamana4">D. Ramana</a></span></div><div class="wp-workCard_item"><span>High Technology Letters</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The construction industry is considered as one of the key sectors and a tool to manage the nation...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The construction industry is considered as one of the key sectors and a tool to manage the national economy by the government as it has a strong linkage with other industries. The construction investment indicates the growth and creation of employment and is recognized as an important public policy tool. Currently Government of India is on its way to invest on many large construction projects. Large construction projects are powerful strategic weapons that are creating economic value, but are inherently complex and dynamic. Delay in large project construction has been becoming very usual across many developing countries and reflecting a national waste. Completing huge and complex construction projects by considering more important features of project management has been becoming a challenge. When the construction industry is facing with numerous challenges including low productivity, lack of research and development, poor technology advancements, the concept of digital twin as a model for data driven management is believed to provide a suitable solution, especially when combined with emerging strategies such as lean construction and emerging technologies such as Building Information Modeling, IoT, Big data analytics, cloud & edge computing and automated & intelligent management systems etc. can provide many useful solutions especially in case of huge construction kind of projects like smart city construction planning and management. It is also concluded that a Digital Triplet Architectural Model can be proposed to be made adoptable with the idea of using the above mentioned emerging technologies in the construction of Smart Cities.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2c8a060c021f12c1edaef1186d0b05aa" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":95684555,"asset_id":92760933,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/95684555/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="92760933"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="92760933"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 92760933; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=92760933]").text(description); $(".js-view-count[data-work-id=92760933]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 92760933; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='92760933']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 92760933, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2c8a060c021f12c1edaef1186d0b05aa" } } $('.js-work-strip[data-work-id=92760933]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":92760933,"title":"Applications of Emerging Technological Models in Smart City Construction","translated_title":"","metadata":{"abstract":"The construction industry is considered as one of the key sectors and a tool to manage the national economy by the government as it has a strong linkage with other industries. The construction investment indicates the growth and creation of employment and is recognized as an important public policy tool. Currently Government of India is on its way to invest on many large construction projects. Large construction projects are powerful strategic weapons that are creating economic value, but are inherently complex and dynamic. Delay in large project construction has been becoming very usual across many developing countries and reflecting a national waste. Completing huge and complex construction projects by considering more important features of project management has been becoming a challenge. When the construction industry is facing with numerous challenges including low productivity, lack of research and development, poor technology advancements, the concept of digital twin as a model for data driven management is believed to provide a suitable solution, especially when combined with emerging strategies such as lean construction and emerging technologies such as Building Information Modeling, IoT, Big data analytics, cloud \u0026 edge computing and automated \u0026 intelligent management systems etc. can provide many useful solutions especially in case of huge construction kind of projects like smart city construction planning and management. It is also concluded that a Digital Triplet Architectural Model can be proposed to be made adoptable with the idea of using the above mentioned emerging technologies in the construction of Smart Cities.","publication_date":{"day":null,"month":null,"year":2022,"errors":{}},"publication_name":"High Technology Letters"},"translated_abstract":"The construction industry is considered as one of the key sectors and a tool to manage the national economy by the government as it has a strong linkage with other industries. The construction investment indicates the growth and creation of employment and is recognized as an important public policy tool. Currently Government of India is on its way to invest on many large construction projects. Large construction projects are powerful strategic weapons that are creating economic value, but are inherently complex and dynamic. Delay in large project construction has been becoming very usual across many developing countries and reflecting a national waste. Completing huge and complex construction projects by considering more important features of project management has been becoming a challenge. When the construction industry is facing with numerous challenges including low productivity, lack of research and development, poor technology advancements, the concept of digital twin as a model for data driven management is believed to provide a suitable solution, especially when combined with emerging strategies such as lean construction and emerging technologies such as Building Information Modeling, IoT, Big data analytics, cloud \u0026 edge computing and automated \u0026 intelligent management systems etc. can provide many useful solutions especially in case of huge construction kind of projects like smart city construction planning and management. It is also concluded that a Digital Triplet Architectural Model can be proposed to be made adoptable with the idea of using the above mentioned emerging technologies in the construction of Smart Cities.","internal_url":"https://www.academia.edu/92760933/Applications_of_Emerging_Technological_Models_in_Smart_City_Construction","translated_internal_url":"","created_at":"2022-12-12T23:05:39.321-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":805201,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":39189135,"work_id":92760933,"tagging_user_id":805201,"tagged_user_id":185710531,"co_author_invite_id":null,"email":"t***a@gmail.com","display_order":1,"name":"Gopi Vasista","title":"Applications of Emerging Technological Models in Smart City Construction"},{"id":39189136,"work_id":92760933,"tagging_user_id":805201,"tagged_user_id":254769646,"co_author_invite_id":7721874,"email":"d***a@pallaviengineeringcollege.ac.in","display_order":2,"name":"D. Ramana","title":"Applications of Emerging Technological Models in Smart City Construction"}],"downloadable_attachments":[{"id":95684555,"title":"","file_type":"docx","scribd_thumbnail_url":"https://attachments.academia-assets.com/95684555/thumbnails/1.jpg","file_name":"Smart_Cities15.docx","download_url":"https://www.academia.edu/attachments/95684555/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Applications_of_Emerging_Technological_M.docx","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95684555/Smart_Cities15.docx?1670915134=\u0026response-content-disposition=attachment%3B+filename%3DApplications_of_Emerging_Technological_M.docx\u0026Expires=1733098142\u0026Signature=axZ38uUzjEochV~LhdUArC9KcoFJtSIvn9pmcpbFk44KciP6UFHWGbneuTN1cK6sEwHE-F59ZrylmP~~9I-xx6UyWpPSg-h-mzGapSu9KWtbMdSiceTAcOi~lJMmiq7QimUXs0Q972Yi0zzkc19LH~yPGdDDWTtFgBi5NGEaQ5AM7Wl~3b3WN9RpT-OvqfsmMZD0G7oqQ~GbrnDTywTaR27j8HHf-C7QGhqUDErvzrohQsm1DLCJs1~jdqw0e~g44Gsv4YGBaCWe17Hz3Y9RAqTFQKvm690wKkgpRZT3Vh7Wv0Lf0Uy4yNk1ImahikZkFVQPBZ2jJfgDfbVJUSZpNA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Applications_of_Emerging_Technological_Models_in_Smart_City_Construction","translated_slug":"","page_count":11,"language":"en","content_type":"Work","owner":{"id":805201,"first_name":"Tgk","middle_initials":null,"last_name":"Vasista","page_name":"TgkVasista","domain_name":"king-saud","created_at":"2011-10-01T22:36:22.310-07:00","display_name":"Tgk Vasista","url":"https://king-saud.academia.edu/TgkVasista"},"attachments":[{"id":95684555,"title":"","file_type":"docx","scribd_thumbnail_url":"https://attachments.academia-assets.com/95684555/thumbnails/1.jpg","file_name":"Smart_Cities15.docx","download_url":"https://www.academia.edu/attachments/95684555/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Applications_of_Emerging_Technological_M.docx","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95684555/Smart_Cities15.docx?1670915134=\u0026response-content-disposition=attachment%3B+filename%3DApplications_of_Emerging_Technological_M.docx\u0026Expires=1733098142\u0026Signature=axZ38uUzjEochV~LhdUArC9KcoFJtSIvn9pmcpbFk44KciP6UFHWGbneuTN1cK6sEwHE-F59ZrylmP~~9I-xx6UyWpPSg-h-mzGapSu9KWtbMdSiceTAcOi~lJMmiq7QimUXs0Q972Yi0zzkc19LH~yPGdDDWTtFgBi5NGEaQ5AM7Wl~3b3WN9RpT-OvqfsmMZD0G7oqQ~GbrnDTywTaR27j8HHf-C7QGhqUDErvzrohQsm1DLCJs1~jdqw0e~g44Gsv4YGBaCWe17Hz3Y9RAqTFQKvm690wKkgpRZT3Vh7Wv0Lf0Uy4yNk1ImahikZkFVQPBZ2jJfgDfbVJUSZpNA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":489384,"name":"Urban Planning, Smart Cities","url":"https://www.academia.edu/Documents/in/Urban_Planning_Smart_Cities"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="92760709"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/92760709/PREDICTION_OF_SALES_BASED_ON_AN_EFFECTIVE_ADVERTISING_MEDIA_SALE_DATA_A_PYTHON_IMPLEMENTATION_APPROACH"><img alt="Research paper thumbnail of PREDICTION OF SALES BASED ON AN EFFECTIVE ADVERTISING MEDIA SALE DATA: A PYTHON IMPLEMENTATION APPROACH" class="work-thumbnail" src="https://attachments.academia-assets.com/95684381/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/92760709/PREDICTION_OF_SALES_BASED_ON_AN_EFFECTIVE_ADVERTISING_MEDIA_SALE_DATA_A_PYTHON_IMPLEMENTATION_APPROACH">PREDICTION OF SALES BASED ON AN EFFECTIVE ADVERTISING MEDIA SALE DATA: A PYTHON IMPLEMENTATION APPROACH</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://king-saud.academia.edu/TgkVasista">Tgk Vasista</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://psau.academia.edu/AhmadZamil">Ahmad Zamil</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://king-saud.academia.edu/MarwanShammot">Marwan Shammot</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Sales forecasting is an essential task in the retail management field. Intelligent forecasting us...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Sales forecasting is an essential task in the retail management field. Intelligent forecasting using machine learning techniques can help discover the selection of feature variables that influence prediction of sales growth. A Python program implementation is adopted to compute; develop and visualizing forecasting model of historical sales data based on advertising media opted to do the effective sales promotion. Python supports working on predictive algorithms through accessing from Python libraries. For this purpose, it relies on the past observations based transaction data set file as an input to produce output without worrying about the underlying mechanism. The results indicated that TV media is the feature variable that influences the prediction of sales of linear regression model. Regression results of OLS model type are displayed with coefficient values to substitute in the linear regression equation. Seaborne library of Python is used to generate the visualization of charts and graphs.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="511309fa6ff407b5310d0f05cabfba70" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":95684381,"asset_id":92760709,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/95684381/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="92760709"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="92760709"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 92760709; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=92760709]").text(description); $(".js-view-count[data-work-id=92760709]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 92760709; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='92760709']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 92760709, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "511309fa6ff407b5310d0f05cabfba70" } } $('.js-work-strip[data-work-id=92760709]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":92760709,"title":"PREDICTION OF SALES BASED ON AN EFFECTIVE ADVERTISING MEDIA SALE DATA: A PYTHON IMPLEMENTATION APPROACH","translated_title":"","metadata":{"abstract":"Sales forecasting is an essential task in the retail management field. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="92760708"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/92760708/Customer_Segmentation_Using_RFM_Analysis_Realizing_Through_Python_Implementation"><img alt="Research paper thumbnail of Customer Segmentation Using RFM Analysis: Realizing Through Python Implementation" class="work-thumbnail" src="https://attachments.academia-assets.com/95684369/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/92760708/Customer_Segmentation_Using_RFM_Analysis_Realizing_Through_Python_Implementation">Customer Segmentation Using RFM Analysis: Realizing Through Python Implementation</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://king-saud.academia.edu/TgkVasista">Tgk Vasista</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://psau.academia.edu/AhmadZamil">Ahmad Zamil</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Purpose: The purpose of this research paper is to segment the best contributed retail management ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Purpose: The purpose of this research paper is to segment the best contributed retail management customers of a super store and corresponding least contributed customers for a given transaction period based on the customer purchase behavior. Pacific Business Review International profitable offerings as a part of loyalty programs. Also, it is possible to focus on planning for customer retention programs. Originality/Value: The value of this research paper lies in realizing through mapping the theoretical value of RFM analysis to practically visualizing the customer segmentation in the form of grouping best customers and low-risk customers using Python implementation. Such value can be found in following the research methodology steps while developing the content of the paper as well as showing practically obtaining results through dealing with data-based case study as a part of serving itself as a testing the proposed hypotheses.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ec58d302edc28623d1213bab25b77adb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":95684369,"asset_id":92760708,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/95684369/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="92760708"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="92760708"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 92760708; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=92760708]").text(description); $(".js-view-count[data-work-id=92760708]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 92760708; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='92760708']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 92760708, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ec58d302edc28623d1213bab25b77adb" } } $('.js-work-strip[data-work-id=92760708]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":92760708,"title":"Customer Segmentation Using RFM Analysis: Realizing Through Python Implementation","translated_title":"","metadata":{"doi":"10.1108/ijrdm-01-2018-0025.","abstract":"Purpose: The purpose of this research paper is to segment the best contributed retail management customers of a super store and corresponding least contributed customers for a given transaction period based on the customer purchase behavior. Pacific Business Review International profitable offerings as a part of loyalty programs. Also, it is possible to focus on planning for customer retention programs. Originality/Value: The value of this research paper lies in realizing through mapping the theoretical value of RFM analysis to practically visualizing the customer segmentation in the form of grouping best customers and low-risk customers using Python implementation. Such value can be found in following the research methodology steps while developing the content of the paper as well as showing practically obtaining results through dealing with data-based case study as a part of serving itself as a testing the proposed hypotheses."},"translated_abstract":"Purpose: The purpose of this research paper is to segment the best contributed retail management customers of a super store and corresponding least contributed customers for a given transaction period based on the customer purchase behavior. Pacific Business Review International profitable offerings as a part of loyalty programs. Also, it is possible to focus on planning for customer retention programs. Originality/Value: The value of this research paper lies in realizing through mapping the theoretical value of RFM analysis to practically visualizing the customer segmentation in the form of grouping best customers and low-risk customers using Python implementation. Such value can be found in following the research methodology steps while developing the content of the paper as well as showing practically obtaining results through dealing with data-based case study as a part of serving itself as a testing the proposed hypotheses.","internal_url":"https://www.academia.edu/92760708/Customer_Segmentation_Using_RFM_Analysis_Realizing_Through_Python_Implementation","translated_internal_url":"","created_at":"2022-12-12T23:01:34.780-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":805201,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":39189120,"work_id":92760708,"tagging_user_id":805201,"tagged_user_id":470942,"co_author_invite_id":null,"email":"d***4@yahoo.com","affiliation":"Prince Sattam Bin Abdulaziz University","display_order":1,"name":"Ahmad Zamil","title":"Customer Segmentation Using RFM Analysis: Realizing Through Python Implementation"},{"id":39189121,"work_id":92760708,"tagging_user_id":805201,"tagged_user_id":185710531,"co_author_invite_id":null,"email":"t***a@gmail.com","display_order":2,"name":"Gopi Vasista","title":"Customer Segmentation Using RFM Analysis: Realizing Through Python Implementation"}],"downloadable_attachments":[{"id":95684369,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/95684369/thumbnails/1.jpg","file_name":"Python_Based_RFM_Analysis.pdf","download_url":"https://www.academia.edu/attachments/95684369/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Customer_Segmentation_Using_RFM_Analysis.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95684369/Python_Based_RFM_Analysis-libre.pdf?1670918031=\u0026response-content-disposition=attachment%3B+filename%3DCustomer_Segmentation_Using_RFM_Analysis.pdf\u0026Expires=1733098142\u0026Signature=GipvgUAxJJSZb2ePCTpnn-CGIL7pdZmKwGwLzgUy3eekznQSAD8~NKTIIdyTU-maL1aByw4TO-fd8-8BsqXj-8E6d5wVet4XA3nh1V37y9kvnmR0u8ftLg7NF4WXX-GIMu6oX21ULnwc8FT7rU38Zhx4-WU358kC5G67gZZVt3Lfda-AYxTKc3TVlXhj04Znq4DL5DkB7Eu0PQF82BjSgljUwN5-1sWN8DoEHrB3yWANCqrTJED2LIJGBLP5BACz~Og3MSDRSQ8nd3Y2XRxNDxr6sEfOGHYAKW3S9CFEF3rFpsCscu0Noem0Fhn93gB5VOdymGy1zqTI9GL0JBkMiw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Customer_Segmentation_Using_RFM_Analysis_Realizing_Through_Python_Implementation","translated_slug":"","page_count":13,"language":"en","content_type":"Work","owner":{"id":805201,"first_name":"Tgk","middle_initials":null,"last_name":"Vasista","page_name":"TgkVasista","domain_name":"king-saud","created_at":"2011-10-01T22:36:22.310-07:00","display_name":"Tgk Vasista","url":"https://king-saud.academia.edu/TgkVasista"},"attachments":[{"id":95684369,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/95684369/thumbnails/1.jpg","file_name":"Python_Based_RFM_Analysis.pdf","download_url":"https://www.academia.edu/attachments/95684369/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Customer_Segmentation_Using_RFM_Analysis.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95684369/Python_Based_RFM_Analysis-libre.pdf?1670918031=\u0026response-content-disposition=attachment%3B+filename%3DCustomer_Segmentation_Using_RFM_Analysis.pdf\u0026Expires=1733098142\u0026Signature=GipvgUAxJJSZb2ePCTpnn-CGIL7pdZmKwGwLzgUy3eekznQSAD8~NKTIIdyTU-maL1aByw4TO-fd8-8BsqXj-8E6d5wVet4XA3nh1V37y9kvnmR0u8ftLg7NF4WXX-GIMu6oX21ULnwc8FT7rU38Zhx4-WU358kC5G67gZZVt3Lfda-AYxTKc3TVlXhj04Znq4DL5DkB7Eu0PQF82BjSgljUwN5-1sWN8DoEHrB3yWANCqrTJED2LIJGBLP5BACz~Og3MSDRSQ8nd3Y2XRxNDxr6sEfOGHYAKW3S9CFEF3rFpsCscu0Noem0Fhn93gB5VOdymGy1zqTI9GL0JBkMiw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); 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The secondary file containing secondary data is obtained from the source web site called Github. Findings:(i) Segmenting customers based on their geographical residence is easy to work using Python (ii) segmenting customers based on their purchase behavior patterns of grocery items is easy to find using Python programming kind of innovative information technologies with visualization. Though there are prior researches conducted using Python libraries, however our approach is an effort of reducing the gap between the theoretically coding the RFM model and its close implementation in Python based on the same RFM coding. Research Limitations/Implications:The development of modern patterns of grocery stores dataanalysis is limited to the caseof Britain and has been described and analyzed through Python programming implementation. Though it is not possible to obtain patterns for all countries globally, however it is possible to obtain such patterns of some of the leading countries like UAE, Saudi Arabia and some countries from Europe etc. Practical Implications:Customer Segmentation through RFM analysis technique implementation in Python provides an opportunity to analyze not only the Britain region but also for different countries is possible. RFM analysis technique that is mentioned theoretically can be well programmed with possible visualization using Python coding and programming libraries. But one must be familiar with having awareness of how to code in Python programming in order to work towards obtaining geographical grouping and customer groupingusing visualization techniques through graphs and charts. Social Implications:It helps grocery stores management in identifying profitable customers and thus can campaign various customer profitable offerings as a part of loyalty programs. Also, it is possible to focus on planning for customer retention programs. Originality/Value:The value of this research paper lies in realizing through mapping the theoretical value of RFM analysis to practically visualizing the customer segmentation in the form of grouping best customers and low-risk customers using Python implementation. Such value can be found in following the research methodology steps while developing the content of the paper as well as showing practically obtaining results through dealing with data-based case study as a part of serving itself as a testing the proposed hypotheses.","grobid_abstract_attachment_id":95684394},"translated_abstract":null,"internal_url":"https://www.academia.edu/92760707/CUSTOMER_SEGMENTATION_USING_RFM_ANALYSIS_REALIZING_THROUGH_PYTHON_IMPLEMENTATION","translated_internal_url":"","created_at":"2022-12-12T23:01:34.760-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":805201,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":39189111,"work_id":92760707,"tagging_user_id":805201,"tagged_user_id":null,"co_author_invite_id":7562671,"email":"a***l@psau.edu.sa","display_order":1,"name":"Ahmad Zamil","title":"CUSTOMER SEGMENTATION USING RFM ANALYSIS: REALIZING THROUGH PYTHON IMPLEMENTATION"},{"id":39189112,"work_id":92760707,"tagging_user_id":805201,"tagged_user_id":185710531,"co_author_invite_id":null,"email":"t***a@gmail.com","display_order":2,"name":"Gopi Vasista","title":"CUSTOMER SEGMENTATION USING RFM ANALYSIS: REALIZING THROUGH PYTHON IMPLEMENTATION"}],"downloadable_attachments":[{"id":95684394,"title":"","file_type":"docx","scribd_thumbnail_url":"https://attachments.academia-assets.com/95684394/thumbnails/1.jpg","file_name":"PBR_CUSTOMER_SEGMENTATION_USING_RFM_ANALYSIS_PYTHON.docx","download_url":"https://www.academia.edu/attachments/95684394/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"CUSTOMER_SEGMENTATION_USING_RFM_ANALYSIS.docx","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95684394/PBR_CUSTOMER_SEGMENTATION_USING_RFM_ANALYSIS_PYTHON.docx?1670914880=\u0026response-content-disposition=attachment%3B+filename%3DCUSTOMER_SEGMENTATION_USING_RFM_ANALYSIS.docx\u0026Expires=1733098142\u0026Signature=Ezt727TTnQIcBI-eU4vG4IOBIg4DY0ERpn6LDt2xv7DKn5~hXuZS-8HNyjaFn9WnXHBmMM6doMv0U641vn~3ZMYMutQChaKe3EYe1ZeBzZMoVhMdsGwwUA9fVNllnZeV9NIIlJ9StT8WrYk1JRVk8HkBnnTlM3ZBySgMmEaBMNHaKeERAE31RQYjlwBvlFKjlSDLePa0C3m3HW-WlU8~B3Ro0TpJDGSd73iTIpYf6M~sLnH7ZMlnQSrqdbQEHhIu-FIVmAb-wloMC0I7vpP2Bswg1tXiUqWa-sFkLUZU~cjReS0zO0c-WNphWJtY4uQAwEHB-LcZgxI8scL8CQ-DEQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"CUSTOMER_SEGMENTATION_USING_RFM_ANALYSIS_REALIZING_THROUGH_PYTHON_IMPLEMENTATION","translated_slug":"","page_count":22,"language":"en","content_type":"Work","owner":{"id":805201,"first_name":"Tgk","middle_initials":null,"last_name":"Vasista","page_name":"TgkVasista","domain_name":"king-saud","created_at":"2011-10-01T22:36:22.310-07:00","display_name":"Tgk Vasista","url":"https://king-saud.academia.edu/TgkVasista"},"attachments":[{"id":95684394,"title":"","file_type":"docx","scribd_thumbnail_url":"https://attachments.academia-assets.com/95684394/thumbnails/1.jpg","file_name":"PBR_CUSTOMER_SEGMENTATION_USING_RFM_ANALYSIS_PYTHON.docx","download_url":"https://www.academia.edu/attachments/95684394/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"CUSTOMER_SEGMENTATION_USING_RFM_ANALYSIS.docx","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95684394/PBR_CUSTOMER_SEGMENTATION_USING_RFM_ANALYSIS_PYTHON.docx?1670914880=\u0026response-content-disposition=attachment%3B+filename%3DCUSTOMER_SEGMENTATION_USING_RFM_ANALYSIS.docx\u0026Expires=1733098142\u0026Signature=Ezt727TTnQIcBI-eU4vG4IOBIg4DY0ERpn6LDt2xv7DKn5~hXuZS-8HNyjaFn9WnXHBmMM6doMv0U641vn~3ZMYMutQChaKe3EYe1ZeBzZMoVhMdsGwwUA9fVNllnZeV9NIIlJ9StT8WrYk1JRVk8HkBnnTlM3ZBySgMmEaBMNHaKeERAE31RQYjlwBvlFKjlSDLePa0C3m3HW-WlU8~B3Ro0TpJDGSd73iTIpYf6M~sLnH7ZMlnQSrqdbQEHhIu-FIVmAb-wloMC0I7vpP2Bswg1tXiUqWa-sFkLUZU~cjReS0zO0c-WNphWJtY4uQAwEHB-LcZgxI8scL8CQ-DEQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="92760706"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/92760706/PREDICTION_OF_SALES_BASED_ON_AN_EFFECTIVE_ADVERTISING_MEDIA_SALE_DATA_A_PYTHON_IMPLEMENTATION_APPROACH"><img alt="Research paper thumbnail of PREDICTION OF SALES BASED ON AN EFFECTIVE ADVERTISING MEDIA SALE DATA: A PYTHON IMPLEMENTATION APPROACH" class="work-thumbnail" src="https://attachments.academia-assets.com/95684382/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/92760706/PREDICTION_OF_SALES_BASED_ON_AN_EFFECTIVE_ADVERTISING_MEDIA_SALE_DATA_A_PYTHON_IMPLEMENTATION_APPROACH">PREDICTION OF SALES BASED ON AN EFFECTIVE ADVERTISING MEDIA SALE DATA: A PYTHON IMPLEMENTATION APPROACH</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://king-saud.academia.edu/TgkVasista">Tgk Vasista</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://psau.academia.edu/AhmadZamil">Ahmad Zamil</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Sales forecasting is an essential task in the retail management field. Intelligent forecasting us...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Sales forecasting is an essential task in the retail management field. Intelligent forecasting using machine learning techniques can help discover the selection of feature variables that influence prediction of sales growth. A Python program implementation is adopted to compute; develop and visualizing forecasting model of historical sales data based on advertising media opted to do the effective sales promotion. Python supports working on predictive algorithms through accessing from Python libraries. For this purpose, it relies on the past observations based transaction data set file as an input to produce output without worrying about the underlying mechanism. The results indicated that TV media is the feature variable that influences the prediction of sales of linear regression model. Regression results of OLS model type are displayed with coefficient values to substitute in the linear regression equation. Seaborne library of Python is used to generate the visualization of charts and graphs.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="312970928968a34c33785d93166be4f2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":95684382,"asset_id":92760706,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/95684382/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="92760706"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="92760706"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 92760706; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=92760706]").text(description); $(".js-view-count[data-work-id=92760706]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 92760706; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='92760706']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 92760706, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "312970928968a34c33785d93166be4f2" } } $('.js-work-strip[data-work-id=92760706]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":92760706,"title":"PREDICTION OF SALES BASED ON AN EFFECTIVE ADVERTISING MEDIA SALE DATA: A PYTHON IMPLEMENTATION APPROACH","translated_title":"","metadata":{"abstract":"Sales forecasting is an essential task in the retail management field. Intelligent forecasting using machine learning techniques can help discover the selection of feature variables that influence prediction of sales growth. A Python program implementation is adopted to compute; develop and visualizing forecasting model of historical sales data based on advertising media opted to do the effective sales promotion. Python supports working on predictive algorithms through accessing from Python libraries. For this purpose, it relies on the past observations based transaction data set file as an input to produce output without worrying about the underlying mechanism. The results indicated that TV media is the feature variable that influences the prediction of sales of linear regression model. Regression results of OLS model type are displayed with coefficient values to substitute in the linear regression equation. Seaborne library of Python is used to generate the visualization of charts and graphs."},"translated_abstract":"Sales forecasting is an essential task in the retail management field. Intelligent forecasting using machine learning techniques can help discover the selection of feature variables that influence prediction of sales growth. A Python program implementation is adopted to compute; develop and visualizing forecasting model of historical sales data based on advertising media opted to do the effective sales promotion. Python supports working on predictive algorithms through accessing from Python libraries. For this purpose, it relies on the past observations based transaction data set file as an input to produce output without worrying about the underlying mechanism. The results indicated that TV media is the feature variable that influences the prediction of sales of linear regression model. Regression results of OLS model type are displayed with coefficient values to substitute in the linear regression equation. Seaborne library of Python is used to generate the visualization of charts and graphs.","internal_url":"https://www.academia.edu/92760706/PREDICTION_OF_SALES_BASED_ON_AN_EFFECTIVE_ADVERTISING_MEDIA_SALE_DATA_A_PYTHON_IMPLEMENTATION_APPROACH","translated_internal_url":"","created_at":"2022-12-12T23:01:34.748-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":805201,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":39189102,"work_id":92760706,"tagging_user_id":805201,"tagged_user_id":470942,"co_author_invite_id":null,"email":"d***4@yahoo.com","affiliation":"Prince Sattam Bin Abdulaziz University","display_order":1,"name":"Ahmad Zamil","title":"PREDICTION OF SALES BASED ON AN EFFECTIVE ADVERTISING MEDIA SALE DATA: A PYTHON IMPLEMENTATION APPROACH"},{"id":39189103,"work_id":92760706,"tagging_user_id":805201,"tagged_user_id":185710531,"co_author_invite_id":null,"email":"t***a@gmail.com","display_order":2,"name":"Gopi Vasista","title":"PREDICTION OF SALES BASED ON AN EFFECTIVE ADVERTISING MEDIA SALE DATA: A PYTHON IMPLEMENTATION APPROACH"},{"id":39189104,"work_id":92760706,"tagging_user_id":805201,"tagged_user_id":24494059,"co_author_invite_id":null,"email":"d***5@gmail.com","affiliation":"King Saud University","display_order":3,"name":"Marwan Shammot","title":"PREDICTION OF SALES BASED ON AN EFFECTIVE ADVERTISING MEDIA SALE DATA: A PYTHON IMPLEMENTATION APPROACH"}],"downloadable_attachments":[{"id":95684382,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/95684382/thumbnails/1.jpg","file_name":"ASM_prediction_of_sales_based_on_an_effective_advertising_media_sale_data_a_python_implementation_approach.pdf","download_url":"https://www.academia.edu/attachments/95684382/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"PREDICTION_OF_SALES_BASED_ON_AN_EFFECTIV.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95684382/ASM_prediction_of_sales_based_on_an_effective_advertising_media_sale_data_a_python_implementation_approach-libre.pdf?1670918033=\u0026response-content-disposition=attachment%3B+filename%3DPREDICTION_OF_SALES_BASED_ON_AN_EFFECTIV.pdf\u0026Expires=1733098142\u0026Signature=NIF4MyyBoKvY2ZdUQCFsskqcnh8VQjv5k9WWZg4hFSedwqEYoVM~Tl57kIpvkodhv7dI8EhhmkAnKemZ-rJrFLXUGp9eWjtqNpSisssTyzYsEIIK2oZQVLa-l3xCibYY2nzIWAH~RoB9XG0a1UYX~rUWe6piVg8WHpFUefjXvFO3G38Hxg4SdSHrCxSnIhfr0XCG5TwiNVuvmvsRiluzOLtz40hhiaDjRSiRGL0aPc7INGJ7P8GMqiZBCSfEAblXh1OCXaWe5SA4Irmtzlu0f9z0aM2-ako~SV3GBxW5IL5kUW1S4f5pyS~bU6MaAm0RpOa3Hi4NCqoPJSF6iuB27A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"PREDICTION_OF_SALES_BASED_ON_AN_EFFECTIVE_ADVERTISING_MEDIA_SALE_DATA_A_PYTHON_IMPLEMENTATION_APPROACH","translated_slug":"","page_count":9,"language":"en","content_type":"Work","owner":{"id":805201,"first_name":"Tgk","middle_initials":null,"last_name":"Vasista","page_name":"TgkVasista","domain_name":"king-saud","created_at":"2011-10-01T22:36:22.310-07:00","display_name":"Tgk Vasista","url":"https://king-saud.academia.edu/TgkVasista"},"attachments":[{"id":95684382,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/95684382/thumbnails/1.jpg","file_name":"ASM_prediction_of_sales_based_on_an_effective_advertising_media_sale_data_a_python_implementation_approach.pdf","download_url":"https://www.academia.edu/attachments/95684382/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"PREDICTION_OF_SALES_BASED_ON_AN_EFFECTIV.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95684382/ASM_prediction_of_sales_based_on_an_effective_advertising_media_sale_data_a_python_implementation_approach-libre.pdf?1670918033=\u0026response-content-disposition=attachment%3B+filename%3DPREDICTION_OF_SALES_BASED_ON_AN_EFFECTIV.pdf\u0026Expires=1733098142\u0026Signature=NIF4MyyBoKvY2ZdUQCFsskqcnh8VQjv5k9WWZg4hFSedwqEYoVM~Tl57kIpvkodhv7dI8EhhmkAnKemZ-rJrFLXUGp9eWjtqNpSisssTyzYsEIIK2oZQVLa-l3xCibYY2nzIWAH~RoB9XG0a1UYX~rUWe6piVg8WHpFUefjXvFO3G38Hxg4SdSHrCxSnIhfr0XCG5TwiNVuvmvsRiluzOLtz40hhiaDjRSiRGL0aPc7INGJ7P8GMqiZBCSfEAblXh1OCXaWe5SA4Irmtzlu0f9z0aM2-ako~SV3GBxW5IL5kUW1S4f5pyS~bU6MaAm0RpOa3Hi4NCqoPJSF6iuB27A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="92760705"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/92760705/PREDICTING_TELECOM_CUSTOMER_CHURN_AN_EXAMPLE_OF_IMPLEMENTING_IN_PYTHON"><img alt="Research paper thumbnail of PREDICTING TELECOM CUSTOMER CHURN: AN EXAMPLE OF IMPLEMENTING IN PYTHON" class="work-thumbnail" src="https://attachments.academia-assets.com/95684377/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/92760705/PREDICTING_TELECOM_CUSTOMER_CHURN_AN_EXAMPLE_OF_IMPLEMENTING_IN_PYTHON">PREDICTING TELECOM CUSTOMER CHURN: AN EXAMPLE OF IMPLEMENTING IN PYTHON</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://king-saud.academia.edu/TgkVasista">Tgk Vasista</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://psau.academia.edu/AhmadZamil">Ahmad Zamil</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The telecom industry is facing fierce competition. Customer retention is becoming a real challeng...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The telecom industry is facing fierce competition. Customer retention is becoming a real challenge. Telecom companies do not want their customers to leave them and look for other service providers. Thus addressing customer churn is becoming a problem. This paper determines the customer churn percentage for a given case of transaction data set as a secondary data. The objective of this research is to identify the probability of customer churn using predictive analytics technique using logistic regression model in order to assess the tendency of probability of customer churn. The result of model accuracy got is 0.8. Based on the existing telecom case study data, customer churn percentage is determined as shown in the graph in the main body of the paper and weighting factors of model function are computed using Python programming language and its libraries.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d6a5f5c1eb76383e2e06014429f2c242" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":95684377,"asset_id":92760705,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/95684377/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="92760705"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="92760705"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 92760705; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=92760705]").text(description); $(".js-view-count[data-work-id=92760705]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 92760705; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='92760705']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 92760705, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d6a5f5c1eb76383e2e06014429f2c242" } } $('.js-work-strip[data-work-id=92760705]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":92760705,"title":"PREDICTING TELECOM CUSTOMER CHURN: AN EXAMPLE OF IMPLEMENTING IN PYTHON","translated_title":"","metadata":{"abstract":"The telecom industry is facing fierce competition. Customer retention is becoming a real challenge. Telecom companies do not want their customers to leave them and look for other service providers. Thus addressing customer churn is becoming a problem. This paper determines the customer churn percentage for a given case of transaction data set as a secondary data. The objective of this research is to identify the probability of customer churn using predictive analytics technique using logistic regression model in order to assess the tendency of probability of customer churn. The result of model accuracy got is 0.8. Based on the existing telecom case study data, customer churn percentage is determined as shown in the graph in the main body of the paper and weighting factors of model function are computed using Python programming language and its libraries."},"translated_abstract":"The telecom industry is facing fierce competition. Customer retention is becoming a real challenge. Telecom companies do not want their customers to leave them and look for other service providers. Thus addressing customer churn is becoming a problem. This paper determines the customer churn percentage for a given case of transaction data set as a secondary data. The objective of this research is to identify the probability of customer churn using predictive analytics technique using logistic regression model in order to assess the tendency of probability of customer churn. The result of model accuracy got is 0.8. Based on the existing telecom case study data, customer churn percentage is determined as shown in the graph in the main body of the paper and weighting factors of model function are computed using Python programming language and its libraries.","internal_url":"https://www.academia.edu/92760705/PREDICTING_TELECOM_CUSTOMER_CHURN_AN_EXAMPLE_OF_IMPLEMENTING_IN_PYTHON","translated_internal_url":"","created_at":"2022-12-12T23:01:34.754-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":805201,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":39189109,"work_id":92760705,"tagging_user_id":805201,"tagged_user_id":470942,"co_author_invite_id":null,"email":"d***4@yahoo.com","affiliation":"Prince Sattam Bin Abdulaziz University","display_order":1,"name":"Ahmad Zamil","title":"PREDICTING TELECOM CUSTOMER CHURN: AN EXAMPLE OF IMPLEMENTING IN PYTHON"},{"id":39189110,"work_id":92760705,"tagging_user_id":805201,"tagged_user_id":185710531,"co_author_invite_id":null,"email":"t***a@gmail.com","display_order":2,"name":"Gopi Vasista","title":"PREDICTING TELECOM CUSTOMER CHURN: AN EXAMPLE OF IMPLEMENTING IN PYTHON"}],"downloadable_attachments":[{"id":95684377,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/95684377/thumbnails/1.jpg","file_name":"JLERI_predicting_telecom_customer_churn_an_example_of_implementing_in_python.pdf","download_url":"https://www.academia.edu/attachments/95684377/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"PREDICTING_TELECOM_CUSTOMER_CHURN_AN_EXA.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95684377/JLERI_predicting_telecom_customer_churn_an_example_of_implementing_in_python-libre.pdf?1670918024=\u0026response-content-disposition=attachment%3B+filename%3DPREDICTING_TELECOM_CUSTOMER_CHURN_AN_EXA.pdf\u0026Expires=1733098142\u0026Signature=bUlksXvSrNEC~-4QZSzRjtc7hnfJsCgvh4Lr4~jD8e2SHBh15Zoxbdg4mr9GokvuthoCosd6I2t64hyyclhF~lCK19ZESjDRoLmdcUoYIpG29FC754ZVoW-gDniOf7-KKXYxcV0XGBGTYOvm4LxMw0xBqegZviai8eJvMvbNyx3TUA6CjVj8wicxBeXe1iOIHLzef7K4wzqbKxwLRy9MJKSs1UFdQAmKLvRmV02Bg4i2KGMWzv3fbV3SysLoye5zEqItsOMmDi3IhNoKm1wwdk690i8C7LygRQE~p3yMvxe6q6wpluT1sOFkzjG9gu~Ul9qyjRy~7k~Xh1-klM6qXQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"PREDICTING_TELECOM_CUSTOMER_CHURN_AN_EXAMPLE_OF_IMPLEMENTING_IN_PYTHON","translated_slug":"","page_count":13,"language":"en","content_type":"Work","owner":{"id":805201,"first_name":"Tgk","middle_initials":null,"last_name":"Vasista","page_name":"TgkVasista","domain_name":"king-saud","created_at":"2011-10-01T22:36:22.310-07:00","display_name":"Tgk Vasista","url":"https://king-saud.academia.edu/TgkVasista"},"attachments":[{"id":95684377,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/95684377/thumbnails/1.jpg","file_name":"JLERI_predicting_telecom_customer_churn_an_example_of_implementing_in_python.pdf","download_url":"https://www.academia.edu/attachments/95684377/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"PREDICTING_TELECOM_CUSTOMER_CHURN_AN_EXA.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95684377/JLERI_predicting_telecom_customer_churn_an_example_of_implementing_in_python-libre.pdf?1670918024=\u0026response-content-disposition=attachment%3B+filename%3DPREDICTING_TELECOM_CUSTOMER_CHURN_AN_EXA.pdf\u0026Expires=1733098142\u0026Signature=bUlksXvSrNEC~-4QZSzRjtc7hnfJsCgvh4Lr4~jD8e2SHBh15Zoxbdg4mr9GokvuthoCosd6I2t64hyyclhF~lCK19ZESjDRoLmdcUoYIpG29FC754ZVoW-gDniOf7-KKXYxcV0XGBGTYOvm4LxMw0xBqegZviai8eJvMvbNyx3TUA6CjVj8wicxBeXe1iOIHLzef7K4wzqbKxwLRy9MJKSs1UFdQAmKLvRmV02Bg4i2KGMWzv3fbV3SysLoye5zEqItsOMmDi3IhNoKm1wwdk690i8C7LygRQE~p3yMvxe6q6wpluT1sOFkzjG9gu~Ul9qyjRy~7k~Xh1-klM6qXQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="92760704"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/92760704/Enhancing_Customer_Loyalty_with_Market_Basket_Analysis_Using_Innovative_Methods_A_Python_Implementation_Approach"><img alt="Research paper thumbnail of Enhancing Customer Loyalty with Market Basket Analysis Using Innovative Methods: A Python Implementation Approach" class="work-thumbnail" src="https://attachments.academia-assets.com/95684375/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/92760704/Enhancing_Customer_Loyalty_with_Market_Basket_Analysis_Using_Innovative_Methods_A_Python_Implementation_Approach">Enhancing Customer Loyalty with Market Basket Analysis Using Innovative Methods: A Python Implementation Approach</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Currently, customer options have increased to purchasing grocery items either in brick and mortar...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Currently, customer options have increased to purchasing grocery items either in brick and mortar based or online stores. The challenge in retail management is to encourage customers to revisit stores and purchase items. Today, companies are using loyalty programs as a technique to enlarge their customer base so that both consumers and companies can reap benefits. This paper focuses on how to enhance customer loyalty at retail stores using state of the art information technology and programming applications such as Data analytics and Python. By applying the Apriori algorithm libraries of Python programming language in Anaconda Navigator, it is possible to data mine the association rules. Eighteen such association rules have been extracted from a secondary data set file available through various Internet sources such as Kaggle.com and Github, to learn customers' purchasing behaviour at retail stores in order to work on customer relationship management strategy.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4637849836ecec2ab75c8ad295ffacdf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":95684375,"asset_id":92760704,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/95684375/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="92760704"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="92760704"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 92760704; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=92760704]").text(description); $(".js-view-count[data-work-id=92760704]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 92760704; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='92760704']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 92760704, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "4637849836ecec2ab75c8ad295ffacdf" } } $('.js-work-strip[data-work-id=92760704]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":92760704,"title":"Enhancing Customer Loyalty with Market Basket Analysis Using Innovative Methods: A Python Implementation Approach","translated_title":"","metadata":{"abstract":"Currently, customer options have increased to purchasing grocery items either in brick and mortar based or online stores. The challenge in retail management is to encourage customers to revisit stores and purchase items. Today, companies are using loyalty programs as a technique to enlarge their customer base so that both consumers and companies can reap benefits. This paper focuses on how to enhance customer loyalty at retail stores using state of the art information technology and programming applications such as Data analytics and Python. By applying the Apriori algorithm libraries of Python programming language in Anaconda Navigator, it is possible to data mine the association rules. Eighteen such association rules have been extracted from a secondary data set file available through various Internet sources such as Kaggle.com and Github, to learn customers' purchasing behaviour at retail stores in order to work on customer relationship management strategy."},"translated_abstract":"Currently, customer options have increased to purchasing grocery items either in brick and mortar based or online stores. The challenge in retail management is to encourage customers to revisit stores and purchase items. Today, companies are using loyalty programs as a technique to enlarge their customer base so that both consumers and companies can reap benefits. This paper focuses on how to enhance customer loyalty at retail stores using state of the art information technology and programming applications such as Data analytics and Python. By applying the Apriori algorithm libraries of Python programming language in Anaconda Navigator, it is possible to data mine the association rules. 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For this purpose a unified model that allows a better understanding on the nature and significance of strategic alignment in theory and practice called 'Abstraction Standardization Customization Personalization model' (ASCP Model) is conceived based on Abstraction-Standardization-Concretization Methodology. Classification is an important tool for perception and can be found in numerous scientific disciplines. Several application areas of classification are described in the context of information modeling. The usefulness of classification both as a reference model and a methodology is required for the purpose of systematically positioning the macro level problems in various layers defined in the classification scheme and is adopted in ASCP Model. ASCP Model is a classification scheme to drive and derive Process Standardization. Consistency in operations is necessary for claiming the process as a reference to set of standard patterns. Business Process Modeling has undoubtedly emerged as a popular and relevant practice in this regard. Our findings suggest that the critical areas of concern are standardization of processes, process modeling approaches, identification of the value and its proposition in business process modeling and in model-driven process execution. 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Corrupt officials and greedy lawyers may instead of speeding up, actually can cause administrative delays in which they have no effect on outcomes but transfer apparent responsibility for decision from elected political officials to agencies or courts. So reformation in administrative procedure in the form of promoting e-government can provide a net benefit to citizens. Developing countries usually face problem of having insufficient infrastructure in fulfilling the needs of providing citizen centric or user centric e-services in various domains such as Judiciary Administration. SaaS based Cloud computing model has a potential to offer solutions in the form of automated useroriented legal case management system while addressing the capital expenditure and it's Return on Investment. It provides service oriented access to end users without compromising on security. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84411727"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/84411727/Strategic_Cost_Management_for_Construction_Project_Success_A_Systematic_Study"><img alt="Research paper thumbnail of Strategic Cost Management for Construction Project Success : A Systematic Study" class="work-thumbnail" src="https://attachments.academia-assets.com/89445755/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84411727/Strategic_Cost_Management_for_Construction_Project_Success_A_Systematic_Study">Strategic Cost Management for Construction Project Success : A Systematic Study</a></div><div class="wp-workCard_item"><span>Civil Engineering and Urban Planning: An International Journal (CiVEJ)</span><span>, 2017</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c4030d167548e3a42b001d3df3e800b4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89445755,"asset_id":84411727,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89445755/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="84411727"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84411727"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84411727; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84411725"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/84411725/Thoughtful_Approaches_to_Implementation_of_Electronic_Rulemaking"><img alt="Research paper thumbnail of Thoughtful Approaches to Implementation of Electronic Rulemaking" class="work-thumbnail" src="https://attachments.academia-assets.com/89445752/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84411725/Thoughtful_Approaches_to_Implementation_of_Electronic_Rulemaking">Thoughtful Approaches to Implementation of Electronic Rulemaking</a></div><div class="wp-workCard_item"><span>International Journal of Managing Public Sector Information and Communication Technologies</span><span>, 2016</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="84cee269772c64643bb86db9f7ba95b7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89445752,"asset_id":84411725,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89445752/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="84411725"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84411725"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84411725; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84411724"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/84411724/Web_Portal_Integration_Architecture_Approaches"><img alt="Research paper thumbnail of Web Portal Integration Architecture Approaches" class="work-thumbnail" src="https://attachments.academia-assets.com/89445756/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84411724/Web_Portal_Integration_Architecture_Approaches">Web Portal Integration Architecture Approaches</a></div><div class="wp-workCard_item"><span>International Journal on Web Service Computing</span><span>, 2012</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3cf18ba3de3b025ba1082ac186d8a523" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89445756,"asset_id":84411724,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89445756/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="84411724"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84411724"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84411724; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="123786940"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/123786940/Prediction_of_Sales_Based_on_an_Effective_Advertising_Media_Sale_Data_A_Python_Implementation_Approach"><img alt="Research paper thumbnail of Prediction of Sales Based on an Effective Advertising Media Sale Data: A Python Implementation Approach" class="work-thumbnail" src="https://attachments.academia-assets.com/118138106/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/123786940/Prediction_of_Sales_Based_on_an_Effective_Advertising_Media_Sale_Data_A_Python_Implementation_Approach">Prediction of Sales Based on an Effective Advertising Media Sale Data: A Python Implementation Approach</a></div><div class="wp-workCard_item"><span>Academy of Strategic Management Journal</span><span>, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="de4bd3681b0679f5f553df46a1779bb8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118138106,"asset_id":123786940,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118138106/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="123786940"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="123786940"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123786940; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=123786940]").text(description); $(".js-view-count[data-work-id=123786940]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 123786940; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='123786940']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 123786940, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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Predicting Telecom Customer Churn: An Example of Implementing in Python" class="work-thumbnail" src="https://attachments.academia-assets.com/118138104/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/123786939/Predicting_Telecom_Customer_Churn_An_Example_of_Implementing_in_Python">Predicting Telecom Customer Churn: An Example of Implementing in Python</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The telecom industry is facing fierce competition. Customer retention is becoming a real challeng...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The telecom industry is facing fierce competition. Customer retention is becoming a real challenge. Telecom companies do not want their customers to leave them and look for other service providers. Thus addressing customer churn is becoming a problem. This paper determines the customer churn percentage for a given case of transaction data set as a secondary data. The objective of this research is to identify the probability of customer churn using predictive analytics technique using logistic regression model in order to assess the tendency of probability of customer churn. The result of model accuracy got is 0.8. Based on the existing telecom case study data, customer churn percentage is determined as shown in the graph in the main body of the paper and weighting factors of model function are computed using Python programming language and its libraries.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7da15df1b94c888f634633de24471e19" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118138104,"asset_id":123786939,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118138104/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="123786939"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="123786939"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123786939; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=123786939]").text(description); $(".js-view-count[data-work-id=123786939]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 123786939; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='123786939']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 123786939, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "7da15df1b94c888f634633de24471e19" } } $('.js-work-strip[data-work-id=123786939]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":123786939,"title":"Predicting Telecom Customer Churn: An Example of Implementing in Python","translated_title":"","metadata":{"abstract":"The telecom industry is facing fierce competition. 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Customer retention is becoming a real challenge. Telecom companies do not want their customers to leave them and look for other service providers. Thus addressing customer churn is becoming a problem. This paper determines the customer churn percentage for a given case of transaction data set as a secondary data. The objective of this research is to identify the probability of customer churn using predictive analytics technique using logistic regression model in order to assess the tendency of probability of customer churn. The result of model accuracy got is 0.8. Based on the existing telecom case study data, customer churn percentage is determined as shown in the graph in the main body of the paper and weighting factors of model function are computed using Python programming language and its 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data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/123786933/Benefits_Barriers_and_Applications_of_Information_Communication_Technology_in_Construction_Industry_a_Contemporary_Study"><img alt="Research paper thumbnail of Benefits, Barriers and Applications of Information Communication Technology in Construction Industry: a Contemporary Study" class="work-thumbnail" src="https://attachments.academia-assets.com/118138095/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/123786933/Benefits_Barriers_and_Applications_of_Information_Communication_Technology_in_Construction_Industry_a_Contemporary_Study">Benefits, Barriers and Applications of Information Communication Technology in Construction Industry: a Contemporary Study</a></div><div class="wp-workCard_item"><span>International Journal of Engineering &amp; Technology</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the construction industry there is a necessity for improving the construction efficiency throu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In the construction industry there is a necessity for improving the construction efficiency through adopting innovative methods. Information and Communication Technologies (ICT) help in increasing the client satisfaction reduce coordination errors and provide a greater understanding among project participants in terms of better handling issues and requirements. Better efficiency can be achieved by making a building smarter. Construction projects are faced with many challenges that must not be ignored or underestimated. Some of the wicked problems and challenges such as understanding how one decision will impact decisions in other areas; problem of addressing the risk due to dynamic and uncertainty existing in the environment; resolving conflict where there are competing claims resulting both good and bad outcomes from the same value system are required to be well managed, if required using innovative methods such as using ICT and software packages. Therefore Objective of this resear...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2f157d170063c5d0fd1d80ec8c254a71" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118138095,"asset_id":123786933,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118138095/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="123786933"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="123786933"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123786933; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=123786933]").text(description); $(".js-view-count[data-work-id=123786933]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 123786933; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='123786933']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 123786933, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2f157d170063c5d0fd1d80ec8c254a71" } } $('.js-work-strip[data-work-id=123786933]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":123786933,"title":"Benefits, Barriers and Applications of Information Communication Technology in Construction Industry: a Contemporary Study","translated_title":"","metadata":{"abstract":"In the construction industry there is a necessity for improving the construction efficiency through adopting innovative methods. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="92761631"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/92761631/Applications_of_Emerging_Technological_Models_in_Smart_City_Construction"><img alt="Research paper thumbnail of Applications of Emerging Technological Models in Smart City Construction" class="work-thumbnail" src="https://attachments.academia-assets.com/95685107/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/92761631/Applications_of_Emerging_Technological_Models_in_Smart_City_Construction">Applications of Emerging Technological Models in Smart City Construction</a></div><div class="wp-workCard_item"><span>High Technology Letters</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The construction industry is considered as one of the key sectors and a tool to manage the nation...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The construction industry is considered as one of the key sectors and a tool to manage the national economy by the government as it has a strong linkage with other industries. The construction investment indicates the growth and creation of employment and is recognized as an important public policy tool. Currently Government of India is on its way to invest on many large construction projects. Large construction projects are powerful strategic weapons that are creating economic value, but are inherently complex and dynamic. Delay in large project construction has been becoming very usual across many developing countries and reflecting a national waste. Completing huge and complex construction projects by considering more important features of project management has been becoming a challenge. When the construction industry is facing with numerous challenges including low productivity, lack of research and development, poor technology advancements, the concept of digital twin as a model for data driven management is believed to provide a suitable solution, especially when combined with emerging strategies such as lean construction and emerging technologies such as Building Information Modeling, IoT, Big data analytics, cloud & edge computing and automated & intelligent management systems etc. can provide many useful solutions especially in case of huge construction kind of projects like smart city construction planning and management. It is also concluded that a Digital Triplet Architectural Model can be proposed to be made adoptable with the idea of using the above mentioned emerging technologies in the construction of Smart Cities.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9057755ac43badc2f0e33ca2eed78fac" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":95685107,"asset_id":92761631,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/95685107/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="92761631"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="92761631"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 92761631; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=92761631]").text(description); $(".js-view-count[data-work-id=92761631]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 92761631; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='92761631']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 92761631, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "9057755ac43badc2f0e33ca2eed78fac" } } $('.js-work-strip[data-work-id=92761631]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":92761631,"title":"Applications of Emerging Technological Models in Smart City Construction","translated_title":"","metadata":{"abstract":"The construction industry is considered as one of the key sectors and a tool to manage the national economy by the government as it has a strong linkage with other industries. 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Ramana</a></span></div><div class="wp-workCard_item"><span>High Technology Letters</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The construction industry is considered as one of the key sectors and a tool to manage the nation...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The construction industry is considered as one of the key sectors and a tool to manage the national economy by the government as it has a strong linkage with other industries. The construction investment indicates the growth and creation of employment and is recognized as an important public policy tool. Currently Government of India is on its way to invest on many large construction projects. Large construction projects are powerful strategic weapons that are creating economic value, but are inherently complex and dynamic. Delay in large project construction has been becoming very usual across many developing countries and reflecting a national waste. Completing huge and complex construction projects by considering more important features of project management has been becoming a challenge. When the construction industry is facing with numerous challenges including low productivity, lack of research and development, poor technology advancements, the concept of digital twin as a model for data driven management is believed to provide a suitable solution, especially when combined with emerging strategies such as lean construction and emerging technologies such as Building Information Modeling, IoT, Big data analytics, cloud & edge computing and automated & intelligent management systems etc. can provide many useful solutions especially in case of huge construction kind of projects like smart city construction planning and management. It is also concluded that a Digital Triplet Architectural Model can be proposed to be made adoptable with the idea of using the above mentioned emerging technologies in the construction of Smart Cities.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2c8a060c021f12c1edaef1186d0b05aa" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":95684555,"asset_id":92760933,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/95684555/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="92760933"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="92760933"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 92760933; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=92760933]").text(description); $(".js-view-count[data-work-id=92760933]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 92760933; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='92760933']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 92760933, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2c8a060c021f12c1edaef1186d0b05aa" } } $('.js-work-strip[data-work-id=92760933]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":92760933,"title":"Applications of Emerging Technological Models in Smart City Construction","translated_title":"","metadata":{"abstract":"The construction industry is considered as one of the key sectors and a tool to manage the national economy by the government as it has a strong linkage with other industries. 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It is also concluded that a Digital Triplet Architectural Model can be proposed to be made adoptable with the idea of using the above mentioned emerging technologies in the construction of Smart Cities.","internal_url":"https://www.academia.edu/92760933/Applications_of_Emerging_Technological_Models_in_Smart_City_Construction","translated_internal_url":"","created_at":"2022-12-12T23:05:39.321-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":805201,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":39189135,"work_id":92760933,"tagging_user_id":805201,"tagged_user_id":185710531,"co_author_invite_id":null,"email":"t***a@gmail.com","display_order":1,"name":"Gopi Vasista","title":"Applications of Emerging Technological Models in Smart City Construction"},{"id":39189136,"work_id":92760933,"tagging_user_id":805201,"tagged_user_id":254769646,"co_author_invite_id":7721874,"email":"d***a@pallaviengineeringcollege.ac.in","display_order":2,"name":"D. 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Intelligent forecasting us...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Sales forecasting is an essential task in the retail management field. Intelligent forecasting using machine learning techniques can help discover the selection of feature variables that influence prediction of sales growth. A Python program implementation is adopted to compute; develop and visualizing forecasting model of historical sales data based on advertising media opted to do the effective sales promotion. Python supports working on predictive algorithms through accessing from Python libraries. For this purpose, it relies on the past observations based transaction data set file as an input to produce output without worrying about the underlying mechanism. The results indicated that TV media is the feature variable that influences the prediction of sales of linear regression model. Regression results of OLS model type are displayed with coefficient values to substitute in the linear regression equation. Seaborne library of Python is used to generate the visualization of charts and graphs.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="511309fa6ff407b5310d0f05cabfba70" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":95684381,"asset_id":92760709,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/95684381/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="92760709"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="92760709"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 92760709; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=92760709]").text(description); $(".js-view-count[data-work-id=92760709]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 92760709; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='92760709']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 92760709, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "511309fa6ff407b5310d0f05cabfba70" } } $('.js-work-strip[data-work-id=92760709]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":92760709,"title":"PREDICTION OF SALES BASED ON AN EFFECTIVE ADVERTISING MEDIA SALE DATA: A PYTHON IMPLEMENTATION APPROACH","translated_title":"","metadata":{"abstract":"Sales forecasting is an essential task in the retail management field. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="92760708"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/92760708/Customer_Segmentation_Using_RFM_Analysis_Realizing_Through_Python_Implementation"><img alt="Research paper thumbnail of Customer Segmentation Using RFM Analysis: Realizing Through Python Implementation" class="work-thumbnail" src="https://attachments.academia-assets.com/95684369/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/92760708/Customer_Segmentation_Using_RFM_Analysis_Realizing_Through_Python_Implementation">Customer Segmentation Using RFM Analysis: Realizing Through Python Implementation</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://king-saud.academia.edu/TgkVasista">Tgk Vasista</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://psau.academia.edu/AhmadZamil">Ahmad Zamil</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Purpose: The purpose of this research paper is to segment the best contributed retail management ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Purpose: The purpose of this research paper is to segment the best contributed retail management customers of a super store and corresponding least contributed customers for a given transaction period based on the customer purchase behavior. Pacific Business Review International profitable offerings as a part of loyalty programs. Also, it is possible to focus on planning for customer retention programs. Originality/Value: The value of this research paper lies in realizing through mapping the theoretical value of RFM analysis to practically visualizing the customer segmentation in the form of grouping best customers and low-risk customers using Python implementation. Such value can be found in following the research methodology steps while developing the content of the paper as well as showing practically obtaining results through dealing with data-based case study as a part of serving itself as a testing the proposed hypotheses.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ec58d302edc28623d1213bab25b77adb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":95684369,"asset_id":92760708,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/95684369/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="92760708"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="92760708"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 92760708; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=92760708]").text(description); $(".js-view-count[data-work-id=92760708]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 92760708; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='92760708']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 92760708, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ec58d302edc28623d1213bab25b77adb" } } $('.js-work-strip[data-work-id=92760708]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":92760708,"title":"Customer Segmentation Using RFM Analysis: Realizing Through Python Implementation","translated_title":"","metadata":{"doi":"10.1108/ijrdm-01-2018-0025.","abstract":"Purpose: The purpose of this research paper is to segment the best contributed retail management customers of a super store and corresponding least contributed customers for a given transaction period based on the customer purchase behavior. Pacific Business Review International profitable offerings as a part of loyalty programs. Also, it is possible to focus on planning for customer retention programs. Originality/Value: The value of this research paper lies in realizing through mapping the theoretical value of RFM analysis to practically visualizing the customer segmentation in the form of grouping best customers and low-risk customers using Python implementation. Such value can be found in following the research methodology steps while developing the content of the paper as well as showing practically obtaining results through dealing with data-based case study as a part of serving itself as a testing the proposed hypotheses."},"translated_abstract":"Purpose: The purpose of this research paper is to segment the best contributed retail management customers of a super store and corresponding least contributed customers for a given transaction period based on the customer purchase behavior. Pacific Business Review International profitable offerings as a part of loyalty programs. Also, it is possible to focus on planning for customer retention programs. Originality/Value: The value of this research paper lies in realizing through mapping the theoretical value of RFM analysis to practically visualizing the customer segmentation in the form of grouping best customers and low-risk customers using Python implementation. Such value can be found in following the research methodology steps while developing the content of the paper as well as showing practically obtaining results through dealing with data-based case study as a part of serving itself as a testing the proposed hypotheses.","internal_url":"https://www.academia.edu/92760708/Customer_Segmentation_Using_RFM_Analysis_Realizing_Through_Python_Implementation","translated_internal_url":"","created_at":"2022-12-12T23:01:34.780-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":805201,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":39189120,"work_id":92760708,"tagging_user_id":805201,"tagged_user_id":470942,"co_author_invite_id":null,"email":"d***4@yahoo.com","affiliation":"Prince Sattam Bin Abdulaziz University","display_order":1,"name":"Ahmad Zamil","title":"Customer Segmentation Using RFM Analysis: Realizing Through Python Implementation"},{"id":39189121,"work_id":92760708,"tagging_user_id":805201,"tagged_user_id":185710531,"co_author_invite_id":null,"email":"t***a@gmail.com","display_order":2,"name":"Gopi Vasista","title":"Customer Segmentation Using RFM Analysis: Realizing Through Python Implementation"}],"downloadable_attachments":[{"id":95684369,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/95684369/thumbnails/1.jpg","file_name":"Python_Based_RFM_Analysis.pdf","download_url":"https://www.academia.edu/attachments/95684369/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Customer_Segmentation_Using_RFM_Analysis.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95684369/Python_Based_RFM_Analysis-libre.pdf?1670918031=\u0026response-content-disposition=attachment%3B+filename%3DCustomer_Segmentation_Using_RFM_Analysis.pdf\u0026Expires=1733098142\u0026Signature=GipvgUAxJJSZb2ePCTpnn-CGIL7pdZmKwGwLzgUy3eekznQSAD8~NKTIIdyTU-maL1aByw4TO-fd8-8BsqXj-8E6d5wVet4XA3nh1V37y9kvnmR0u8ftLg7NF4WXX-GIMu6oX21ULnwc8FT7rU38Zhx4-WU358kC5G67gZZVt3Lfda-AYxTKc3TVlXhj04Znq4DL5DkB7Eu0PQF82BjSgljUwN5-1sWN8DoEHrB3yWANCqrTJED2LIJGBLP5BACz~Og3MSDRSQ8nd3Y2XRxNDxr6sEfOGHYAKW3S9CFEF3rFpsCscu0Noem0Fhn93gB5VOdymGy1zqTI9GL0JBkMiw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Customer_Segmentation_Using_RFM_Analysis_Realizing_Through_Python_Implementation","translated_slug":"","page_count":13,"language":"en","content_type":"Work","owner":{"id":805201,"first_name":"Tgk","middle_initials":null,"last_name":"Vasista","page_name":"TgkVasista","domain_name":"king-saud","created_at":"2011-10-01T22:36:22.310-07:00","display_name":"Tgk Vasista","url":"https://king-saud.academia.edu/TgkVasista"},"attachments":[{"id":95684369,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/95684369/thumbnails/1.jpg","file_name":"Python_Based_RFM_Analysis.pdf","download_url":"https://www.academia.edu/attachments/95684369/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Customer_Segmentation_Using_RFM_Analysis.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95684369/Python_Based_RFM_Analysis-libre.pdf?1670918031=\u0026response-content-disposition=attachment%3B+filename%3DCustomer_Segmentation_Using_RFM_Analysis.pdf\u0026Expires=1733098142\u0026Signature=GipvgUAxJJSZb2ePCTpnn-CGIL7pdZmKwGwLzgUy3eekznQSAD8~NKTIIdyTU-maL1aByw4TO-fd8-8BsqXj-8E6d5wVet4XA3nh1V37y9kvnmR0u8ftLg7NF4WXX-GIMu6oX21ULnwc8FT7rU38Zhx4-WU358kC5G67gZZVt3Lfda-AYxTKc3TVlXhj04Znq4DL5DkB7Eu0PQF82BjSgljUwN5-1sWN8DoEHrB3yWANCqrTJED2LIJGBLP5BACz~Og3MSDRSQ8nd3Y2XRxNDxr6sEfOGHYAKW3S9CFEF3rFpsCscu0Noem0Fhn93gB5VOdymGy1zqTI9GL0JBkMiw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); 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The secondary file containing secondary data is obtained from the source web site called Github. Findings:(i) Segmenting customers based on their geographical residence is easy to work using Python (ii) segmenting customers based on their purchase behavior patterns of grocery items is easy to find using Python programming kind of innovative information technologies with visualization. Though there are prior researches conducted using Python libraries, however our approach is an effort of reducing the gap between the theoretically coding the RFM model and its close implementation in Python based on the same RFM coding. Research Limitations/Implications:The development of modern patterns of grocery stores dataanalysis is limited to the caseof Britain and has been described and analyzed through Python programming implementation. Though it is not possible to obtain patterns for all countries globally, however it is possible to obtain such patterns of some of the leading countries like UAE, Saudi Arabia and some countries from Europe etc. Practical Implications:Customer Segmentation through RFM analysis technique implementation in Python provides an opportunity to analyze not only the Britain region but also for different countries is possible. RFM analysis technique that is mentioned theoretically can be well programmed with possible visualization using Python coding and programming libraries. But one must be familiar with having awareness of how to code in Python programming in order to work towards obtaining geographical grouping and customer groupingusing visualization techniques through graphs and charts. Social Implications:It helps grocery stores management in identifying profitable customers and thus can campaign various customer profitable offerings as a part of loyalty programs. Also, it is possible to focus on planning for customer retention programs. Originality/Value:The value of this research paper lies in realizing through mapping the theoretical value of RFM analysis to practically visualizing the customer segmentation in the form of grouping best customers and low-risk customers using Python implementation. Such value can be found in following the research methodology steps while developing the content of the paper as well as showing practically obtaining results through dealing with data-based case study as a part of serving itself as a testing the proposed hypotheses.","grobid_abstract_attachment_id":95684394},"translated_abstract":null,"internal_url":"https://www.academia.edu/92760707/CUSTOMER_SEGMENTATION_USING_RFM_ANALYSIS_REALIZING_THROUGH_PYTHON_IMPLEMENTATION","translated_internal_url":"","created_at":"2022-12-12T23:01:34.760-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":805201,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":39189111,"work_id":92760707,"tagging_user_id":805201,"tagged_user_id":null,"co_author_invite_id":7562671,"email":"a***l@psau.edu.sa","display_order":1,"name":"Ahmad Zamil","title":"CUSTOMER SEGMENTATION USING RFM ANALYSIS: REALIZING THROUGH PYTHON IMPLEMENTATION"},{"id":39189112,"work_id":92760707,"tagging_user_id":805201,"tagged_user_id":185710531,"co_author_invite_id":null,"email":"t***a@gmail.com","display_order":2,"name":"Gopi Vasista","title":"CUSTOMER SEGMENTATION USING RFM ANALYSIS: REALIZING THROUGH PYTHON IMPLEMENTATION"}],"downloadable_attachments":[{"id":95684394,"title":"","file_type":"docx","scribd_thumbnail_url":"https://attachments.academia-assets.com/95684394/thumbnails/1.jpg","file_name":"PBR_CUSTOMER_SEGMENTATION_USING_RFM_ANALYSIS_PYTHON.docx","download_url":"https://www.academia.edu/attachments/95684394/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"CUSTOMER_SEGMENTATION_USING_RFM_ANALYSIS.docx","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95684394/PBR_CUSTOMER_SEGMENTATION_USING_RFM_ANALYSIS_PYTHON.docx?1670914880=\u0026response-content-disposition=attachment%3B+filename%3DCUSTOMER_SEGMENTATION_USING_RFM_ANALYSIS.docx\u0026Expires=1733098142\u0026Signature=Ezt727TTnQIcBI-eU4vG4IOBIg4DY0ERpn6LDt2xv7DKn5~hXuZS-8HNyjaFn9WnXHBmMM6doMv0U641vn~3ZMYMutQChaKe3EYe1ZeBzZMoVhMdsGwwUA9fVNllnZeV9NIIlJ9StT8WrYk1JRVk8HkBnnTlM3ZBySgMmEaBMNHaKeERAE31RQYjlwBvlFKjlSDLePa0C3m3HW-WlU8~B3Ro0TpJDGSd73iTIpYf6M~sLnH7ZMlnQSrqdbQEHhIu-FIVmAb-wloMC0I7vpP2Bswg1tXiUqWa-sFkLUZU~cjReS0zO0c-WNphWJtY4uQAwEHB-LcZgxI8scL8CQ-DEQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"CUSTOMER_SEGMENTATION_USING_RFM_ANALYSIS_REALIZING_THROUGH_PYTHON_IMPLEMENTATION","translated_slug":"","page_count":22,"language":"en","content_type":"Work","owner":{"id":805201,"first_name":"Tgk","middle_initials":null,"last_name":"Vasista","page_name":"TgkVasista","domain_name":"king-saud","created_at":"2011-10-01T22:36:22.310-07:00","display_name":"Tgk Vasista","url":"https://king-saud.academia.edu/TgkVasista"},"attachments":[{"id":95684394,"title":"","file_type":"docx","scribd_thumbnail_url":"https://attachments.academia-assets.com/95684394/thumbnails/1.jpg","file_name":"PBR_CUSTOMER_SEGMENTATION_USING_RFM_ANALYSIS_PYTHON.docx","download_url":"https://www.academia.edu/attachments/95684394/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"CUSTOMER_SEGMENTATION_USING_RFM_ANALYSIS.docx","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95684394/PBR_CUSTOMER_SEGMENTATION_USING_RFM_ANALYSIS_PYTHON.docx?1670914880=\u0026response-content-disposition=attachment%3B+filename%3DCUSTOMER_SEGMENTATION_USING_RFM_ANALYSIS.docx\u0026Expires=1733098142\u0026Signature=Ezt727TTnQIcBI-eU4vG4IOBIg4DY0ERpn6LDt2xv7DKn5~hXuZS-8HNyjaFn9WnXHBmMM6doMv0U641vn~3ZMYMutQChaKe3EYe1ZeBzZMoVhMdsGwwUA9fVNllnZeV9NIIlJ9StT8WrYk1JRVk8HkBnnTlM3ZBySgMmEaBMNHaKeERAE31RQYjlwBvlFKjlSDLePa0C3m3HW-WlU8~B3Ro0TpJDGSd73iTIpYf6M~sLnH7ZMlnQSrqdbQEHhIu-FIVmAb-wloMC0I7vpP2Bswg1tXiUqWa-sFkLUZU~cjReS0zO0c-WNphWJtY4uQAwEHB-LcZgxI8scL8CQ-DEQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="92760706"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/92760706/PREDICTION_OF_SALES_BASED_ON_AN_EFFECTIVE_ADVERTISING_MEDIA_SALE_DATA_A_PYTHON_IMPLEMENTATION_APPROACH"><img alt="Research paper thumbnail of PREDICTION OF SALES BASED ON AN EFFECTIVE ADVERTISING MEDIA SALE DATA: A PYTHON IMPLEMENTATION APPROACH" class="work-thumbnail" src="https://attachments.academia-assets.com/95684382/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/92760706/PREDICTION_OF_SALES_BASED_ON_AN_EFFECTIVE_ADVERTISING_MEDIA_SALE_DATA_A_PYTHON_IMPLEMENTATION_APPROACH">PREDICTION OF SALES BASED ON AN EFFECTIVE ADVERTISING MEDIA SALE DATA: A PYTHON IMPLEMENTATION APPROACH</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://king-saud.academia.edu/TgkVasista">Tgk Vasista</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://psau.academia.edu/AhmadZamil">Ahmad Zamil</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Sales forecasting is an essential task in the retail management field. Intelligent forecasting us...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Sales forecasting is an essential task in the retail management field. Intelligent forecasting using machine learning techniques can help discover the selection of feature variables that influence prediction of sales growth. A Python program implementation is adopted to compute; develop and visualizing forecasting model of historical sales data based on advertising media opted to do the effective sales promotion. Python supports working on predictive algorithms through accessing from Python libraries. For this purpose, it relies on the past observations based transaction data set file as an input to produce output without worrying about the underlying mechanism. The results indicated that TV media is the feature variable that influences the prediction of sales of linear regression model. Regression results of OLS model type are displayed with coefficient values to substitute in the linear regression equation. Seaborne library of Python is used to generate the visualization of charts and graphs.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="312970928968a34c33785d93166be4f2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":95684382,"asset_id":92760706,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/95684382/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="92760706"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="92760706"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 92760706; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=92760706]").text(description); $(".js-view-count[data-work-id=92760706]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 92760706; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='92760706']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 92760706, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "312970928968a34c33785d93166be4f2" } } $('.js-work-strip[data-work-id=92760706]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":92760706,"title":"PREDICTION OF SALES BASED ON AN EFFECTIVE ADVERTISING MEDIA SALE DATA: A PYTHON IMPLEMENTATION APPROACH","translated_title":"","metadata":{"abstract":"Sales forecasting is an essential task in the retail management field. Intelligent forecasting using machine learning techniques can help discover the selection of feature variables that influence prediction of sales growth. A Python program implementation is adopted to compute; develop and visualizing forecasting model of historical sales data based on advertising media opted to do the effective sales promotion. Python supports working on predictive algorithms through accessing from Python libraries. For this purpose, it relies on the past observations based transaction data set file as an input to produce output without worrying about the underlying mechanism. The results indicated that TV media is the feature variable that influences the prediction of sales of linear regression model. Regression results of OLS model type are displayed with coefficient values to substitute in the linear regression equation. Seaborne library of Python is used to generate the visualization of charts and graphs."},"translated_abstract":"Sales forecasting is an essential task in the retail management field. Intelligent forecasting using machine learning techniques can help discover the selection of feature variables that influence prediction of sales growth. A Python program implementation is adopted to compute; develop and visualizing forecasting model of historical sales data based on advertising media opted to do the effective sales promotion. Python supports working on predictive algorithms through accessing from Python libraries. For this purpose, it relies on the past observations based transaction data set file as an input to produce output without worrying about the underlying mechanism. The results indicated that TV media is the feature variable that influences the prediction of sales of linear regression model. Regression results of OLS model type are displayed with coefficient values to substitute in the linear regression equation. Seaborne library of Python is used to generate the visualization of charts and graphs.","internal_url":"https://www.academia.edu/92760706/PREDICTION_OF_SALES_BASED_ON_AN_EFFECTIVE_ADVERTISING_MEDIA_SALE_DATA_A_PYTHON_IMPLEMENTATION_APPROACH","translated_internal_url":"","created_at":"2022-12-12T23:01:34.748-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":805201,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":39189102,"work_id":92760706,"tagging_user_id":805201,"tagged_user_id":470942,"co_author_invite_id":null,"email":"d***4@yahoo.com","affiliation":"Prince Sattam Bin Abdulaziz University","display_order":1,"name":"Ahmad Zamil","title":"PREDICTION OF SALES BASED ON AN EFFECTIVE ADVERTISING MEDIA SALE DATA: A PYTHON IMPLEMENTATION APPROACH"},{"id":39189103,"work_id":92760706,"tagging_user_id":805201,"tagged_user_id":185710531,"co_author_invite_id":null,"email":"t***a@gmail.com","display_order":2,"name":"Gopi Vasista","title":"PREDICTION OF SALES BASED ON AN EFFECTIVE ADVERTISING MEDIA SALE DATA: A PYTHON IMPLEMENTATION APPROACH"},{"id":39189104,"work_id":92760706,"tagging_user_id":805201,"tagged_user_id":24494059,"co_author_invite_id":null,"email":"d***5@gmail.com","affiliation":"King Saud University","display_order":3,"name":"Marwan Shammot","title":"PREDICTION OF SALES BASED ON AN EFFECTIVE ADVERTISING MEDIA SALE DATA: A PYTHON IMPLEMENTATION APPROACH"}],"downloadable_attachments":[{"id":95684382,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/95684382/thumbnails/1.jpg","file_name":"ASM_prediction_of_sales_based_on_an_effective_advertising_media_sale_data_a_python_implementation_approach.pdf","download_url":"https://www.academia.edu/attachments/95684382/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"PREDICTION_OF_SALES_BASED_ON_AN_EFFECTIV.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95684382/ASM_prediction_of_sales_based_on_an_effective_advertising_media_sale_data_a_python_implementation_approach-libre.pdf?1670918033=\u0026response-content-disposition=attachment%3B+filename%3DPREDICTION_OF_SALES_BASED_ON_AN_EFFECTIV.pdf\u0026Expires=1733098142\u0026Signature=NIF4MyyBoKvY2ZdUQCFsskqcnh8VQjv5k9WWZg4hFSedwqEYoVM~Tl57kIpvkodhv7dI8EhhmkAnKemZ-rJrFLXUGp9eWjtqNpSisssTyzYsEIIK2oZQVLa-l3xCibYY2nzIWAH~RoB9XG0a1UYX~rUWe6piVg8WHpFUefjXvFO3G38Hxg4SdSHrCxSnIhfr0XCG5TwiNVuvmvsRiluzOLtz40hhiaDjRSiRGL0aPc7INGJ7P8GMqiZBCSfEAblXh1OCXaWe5SA4Irmtzlu0f9z0aM2-ako~SV3GBxW5IL5kUW1S4f5pyS~bU6MaAm0RpOa3Hi4NCqoPJSF6iuB27A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"PREDICTION_OF_SALES_BASED_ON_AN_EFFECTIVE_ADVERTISING_MEDIA_SALE_DATA_A_PYTHON_IMPLEMENTATION_APPROACH","translated_slug":"","page_count":9,"language":"en","content_type":"Work","owner":{"id":805201,"first_name":"Tgk","middle_initials":null,"last_name":"Vasista","page_name":"TgkVasista","domain_name":"king-saud","created_at":"2011-10-01T22:36:22.310-07:00","display_name":"Tgk Vasista","url":"https://king-saud.academia.edu/TgkVasista"},"attachments":[{"id":95684382,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/95684382/thumbnails/1.jpg","file_name":"ASM_prediction_of_sales_based_on_an_effective_advertising_media_sale_data_a_python_implementation_approach.pdf","download_url":"https://www.academia.edu/attachments/95684382/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"PREDICTION_OF_SALES_BASED_ON_AN_EFFECTIV.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95684382/ASM_prediction_of_sales_based_on_an_effective_advertising_media_sale_data_a_python_implementation_approach-libre.pdf?1670918033=\u0026response-content-disposition=attachment%3B+filename%3DPREDICTION_OF_SALES_BASED_ON_AN_EFFECTIV.pdf\u0026Expires=1733098142\u0026Signature=NIF4MyyBoKvY2ZdUQCFsskqcnh8VQjv5k9WWZg4hFSedwqEYoVM~Tl57kIpvkodhv7dI8EhhmkAnKemZ-rJrFLXUGp9eWjtqNpSisssTyzYsEIIK2oZQVLa-l3xCibYY2nzIWAH~RoB9XG0a1UYX~rUWe6piVg8WHpFUefjXvFO3G38Hxg4SdSHrCxSnIhfr0XCG5TwiNVuvmvsRiluzOLtz40hhiaDjRSiRGL0aPc7INGJ7P8GMqiZBCSfEAblXh1OCXaWe5SA4Irmtzlu0f9z0aM2-ako~SV3GBxW5IL5kUW1S4f5pyS~bU6MaAm0RpOa3Hi4NCqoPJSF6iuB27A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="92760705"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/92760705/PREDICTING_TELECOM_CUSTOMER_CHURN_AN_EXAMPLE_OF_IMPLEMENTING_IN_PYTHON"><img alt="Research paper thumbnail of PREDICTING TELECOM CUSTOMER CHURN: AN EXAMPLE OF IMPLEMENTING IN PYTHON" class="work-thumbnail" src="https://attachments.academia-assets.com/95684377/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/92760705/PREDICTING_TELECOM_CUSTOMER_CHURN_AN_EXAMPLE_OF_IMPLEMENTING_IN_PYTHON">PREDICTING TELECOM CUSTOMER CHURN: AN EXAMPLE OF IMPLEMENTING IN PYTHON</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://king-saud.academia.edu/TgkVasista">Tgk Vasista</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://psau.academia.edu/AhmadZamil">Ahmad Zamil</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The telecom industry is facing fierce competition. Customer retention is becoming a real challeng...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The telecom industry is facing fierce competition. Customer retention is becoming a real challenge. Telecom companies do not want their customers to leave them and look for other service providers. Thus addressing customer churn is becoming a problem. This paper determines the customer churn percentage for a given case of transaction data set as a secondary data. The objective of this research is to identify the probability of customer churn using predictive analytics technique using logistic regression model in order to assess the tendency of probability of customer churn. The result of model accuracy got is 0.8. Based on the existing telecom case study data, customer churn percentage is determined as shown in the graph in the main body of the paper and weighting factors of model function are computed using Python programming language and its libraries.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d6a5f5c1eb76383e2e06014429f2c242" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":95684377,"asset_id":92760705,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/95684377/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="92760705"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="92760705"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 92760705; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=92760705]").text(description); $(".js-view-count[data-work-id=92760705]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 92760705; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='92760705']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 92760705, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d6a5f5c1eb76383e2e06014429f2c242" } } $('.js-work-strip[data-work-id=92760705]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":92760705,"title":"PREDICTING TELECOM CUSTOMER CHURN: AN EXAMPLE OF IMPLEMENTING IN PYTHON","translated_title":"","metadata":{"abstract":"The telecom industry is facing fierce competition. Customer retention is becoming a real challenge. Telecom companies do not want their customers to leave them and look for other service providers. Thus addressing customer churn is becoming a problem. This paper determines the customer churn percentage for a given case of transaction data set as a secondary data. The objective of this research is to identify the probability of customer churn using predictive analytics technique using logistic regression model in order to assess the tendency of probability of customer churn. The result of model accuracy got is 0.8. Based on the existing telecom case study data, customer churn percentage is determined as shown in the graph in the main body of the paper and weighting factors of model function are computed using Python programming language and its libraries."},"translated_abstract":"The telecom industry is facing fierce competition. Customer retention is becoming a real challenge. Telecom companies do not want their customers to leave them and look for other service providers. Thus addressing customer churn is becoming a problem. This paper determines the customer churn percentage for a given case of transaction data set as a secondary data. The objective of this research is to identify the probability of customer churn using predictive analytics technique using logistic regression model in order to assess the tendency of probability of customer churn. The result of model accuracy got is 0.8. Based on the existing telecom case study data, customer churn percentage is determined as shown in the graph in the main body of the paper and weighting factors of model function are computed using Python programming language and its libraries.","internal_url":"https://www.academia.edu/92760705/PREDICTING_TELECOM_CUSTOMER_CHURN_AN_EXAMPLE_OF_IMPLEMENTING_IN_PYTHON","translated_internal_url":"","created_at":"2022-12-12T23:01:34.754-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":805201,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":39189109,"work_id":92760705,"tagging_user_id":805201,"tagged_user_id":470942,"co_author_invite_id":null,"email":"d***4@yahoo.com","affiliation":"Prince Sattam Bin Abdulaziz University","display_order":1,"name":"Ahmad Zamil","title":"PREDICTING TELECOM CUSTOMER CHURN: AN EXAMPLE OF IMPLEMENTING IN PYTHON"},{"id":39189110,"work_id":92760705,"tagging_user_id":805201,"tagged_user_id":185710531,"co_author_invite_id":null,"email":"t***a@gmail.com","display_order":2,"name":"Gopi Vasista","title":"PREDICTING TELECOM CUSTOMER CHURN: AN EXAMPLE OF IMPLEMENTING IN PYTHON"}],"downloadable_attachments":[{"id":95684377,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/95684377/thumbnails/1.jpg","file_name":"JLERI_predicting_telecom_customer_churn_an_example_of_implementing_in_python.pdf","download_url":"https://www.academia.edu/attachments/95684377/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"PREDICTING_TELECOM_CUSTOMER_CHURN_AN_EXA.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95684377/JLERI_predicting_telecom_customer_churn_an_example_of_implementing_in_python-libre.pdf?1670918024=\u0026response-content-disposition=attachment%3B+filename%3DPREDICTING_TELECOM_CUSTOMER_CHURN_AN_EXA.pdf\u0026Expires=1733098142\u0026Signature=bUlksXvSrNEC~-4QZSzRjtc7hnfJsCgvh4Lr4~jD8e2SHBh15Zoxbdg4mr9GokvuthoCosd6I2t64hyyclhF~lCK19ZESjDRoLmdcUoYIpG29FC754ZVoW-gDniOf7-KKXYxcV0XGBGTYOvm4LxMw0xBqegZviai8eJvMvbNyx3TUA6CjVj8wicxBeXe1iOIHLzef7K4wzqbKxwLRy9MJKSs1UFdQAmKLvRmV02Bg4i2KGMWzv3fbV3SysLoye5zEqItsOMmDi3IhNoKm1wwdk690i8C7LygRQE~p3yMvxe6q6wpluT1sOFkzjG9gu~Ul9qyjRy~7k~Xh1-klM6qXQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"PREDICTING_TELECOM_CUSTOMER_CHURN_AN_EXAMPLE_OF_IMPLEMENTING_IN_PYTHON","translated_slug":"","page_count":13,"language":"en","content_type":"Work","owner":{"id":805201,"first_name":"Tgk","middle_initials":null,"last_name":"Vasista","page_name":"TgkVasista","domain_name":"king-saud","created_at":"2011-10-01T22:36:22.310-07:00","display_name":"Tgk Vasista","url":"https://king-saud.academia.edu/TgkVasista"},"attachments":[{"id":95684377,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/95684377/thumbnails/1.jpg","file_name":"JLERI_predicting_telecom_customer_churn_an_example_of_implementing_in_python.pdf","download_url":"https://www.academia.edu/attachments/95684377/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"PREDICTING_TELECOM_CUSTOMER_CHURN_AN_EXA.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95684377/JLERI_predicting_telecom_customer_churn_an_example_of_implementing_in_python-libre.pdf?1670918024=\u0026response-content-disposition=attachment%3B+filename%3DPREDICTING_TELECOM_CUSTOMER_CHURN_AN_EXA.pdf\u0026Expires=1733098142\u0026Signature=bUlksXvSrNEC~-4QZSzRjtc7hnfJsCgvh4Lr4~jD8e2SHBh15Zoxbdg4mr9GokvuthoCosd6I2t64hyyclhF~lCK19ZESjDRoLmdcUoYIpG29FC754ZVoW-gDniOf7-KKXYxcV0XGBGTYOvm4LxMw0xBqegZviai8eJvMvbNyx3TUA6CjVj8wicxBeXe1iOIHLzef7K4wzqbKxwLRy9MJKSs1UFdQAmKLvRmV02Bg4i2KGMWzv3fbV3SysLoye5zEqItsOMmDi3IhNoKm1wwdk690i8C7LygRQE~p3yMvxe6q6wpluT1sOFkzjG9gu~Ul9qyjRy~7k~Xh1-klM6qXQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="92760704"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/92760704/Enhancing_Customer_Loyalty_with_Market_Basket_Analysis_Using_Innovative_Methods_A_Python_Implementation_Approach"><img alt="Research paper thumbnail of Enhancing Customer Loyalty with Market Basket Analysis Using Innovative Methods: A Python Implementation Approach" class="work-thumbnail" src="https://attachments.academia-assets.com/95684375/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/92760704/Enhancing_Customer_Loyalty_with_Market_Basket_Analysis_Using_Innovative_Methods_A_Python_Implementation_Approach">Enhancing Customer Loyalty with Market Basket Analysis Using Innovative Methods: A Python Implementation Approach</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Currently, customer options have increased to purchasing grocery items either in brick and mortar...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Currently, customer options have increased to purchasing grocery items either in brick and mortar based or online stores. The challenge in retail management is to encourage customers to revisit stores and purchase items. Today, companies are using loyalty programs as a technique to enlarge their customer base so that both consumers and companies can reap benefits. This paper focuses on how to enhance customer loyalty at retail stores using state of the art information technology and programming applications such as Data analytics and Python. By applying the Apriori algorithm libraries of Python programming language in Anaconda Navigator, it is possible to data mine the association rules. Eighteen such association rules have been extracted from a secondary data set file available through various Internet sources such as Kaggle.com and Github, to learn customers' purchasing behaviour at retail stores in order to work on customer relationship management strategy.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4637849836ecec2ab75c8ad295ffacdf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":95684375,"asset_id":92760704,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/95684375/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="92760704"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="92760704"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 92760704; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=92760704]").text(description); $(".js-view-count[data-work-id=92760704]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 92760704; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='92760704']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 92760704, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "4637849836ecec2ab75c8ad295ffacdf" } } $('.js-work-strip[data-work-id=92760704]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":92760704,"title":"Enhancing Customer Loyalty with Market Basket Analysis Using Innovative Methods: A Python Implementation Approach","translated_title":"","metadata":{"abstract":"Currently, customer options have increased to purchasing grocery items either in brick and mortar based or online stores. The challenge in retail management is to encourage customers to revisit stores and purchase items. Today, companies are using loyalty programs as a technique to enlarge their customer base so that both consumers and companies can reap benefits. This paper focuses on how to enhance customer loyalty at retail stores using state of the art information technology and programming applications such as Data analytics and Python. By applying the Apriori algorithm libraries of Python programming language in Anaconda Navigator, it is possible to data mine the association rules. Eighteen such association rules have been extracted from a secondary data set file available through various Internet sources such as Kaggle.com and Github, to learn customers' purchasing behaviour at retail stores in order to work on customer relationship management strategy."},"translated_abstract":"Currently, customer options have increased to purchasing grocery items either in brick and mortar based or online stores. The challenge in retail management is to encourage customers to revisit stores and purchase items. Today, companies are using loyalty programs as a technique to enlarge their customer base so that both consumers and companies can reap benefits. This paper focuses on how to enhance customer loyalty at retail stores using state of the art information technology and programming applications such as Data analytics and Python. By applying the Apriori algorithm libraries of Python programming language in Anaconda Navigator, it is possible to data mine the association rules. Eighteen such association rules have been extracted from a secondary data set file available through various Internet sources such as Kaggle.com and Github, to learn customers' purchasing behaviour at retail stores in order to work on customer relationship management strategy.","internal_url":"https://www.academia.edu/92760704/Enhancing_Customer_Loyalty_with_Market_Basket_Analysis_Using_Innovative_Methods_A_Python_Implementation_Approach","translated_internal_url":"","created_at":"2022-12-12T23:01:34.729-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":805201,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":39189105,"work_id":92760704,"tagging_user_id":805201,"tagged_user_id":null,"co_author_invite_id":7296562,"email":"a***l@psau.edu.sa","display_order":1,"name":"Ahmad Zamil","title":"Enhancing Customer Loyalty with Market Basket Analysis Using Innovative Methods: A Python Implementation Approach"},{"id":39189106,"work_id":92760704,"tagging_user_id":805201,"tagged_user_id":null,"co_author_invite_id":7296563,"email":"b***n@ammanu.edu.jo","display_order":2,"name":"Ahmad Al Adwan","title":"Enhancing Customer Loyalty with Market Basket Analysis Using Innovative Methods: A Python Implementation Approach"},{"id":39189107,"work_id":92760704,"tagging_user_id":805201,"tagged_user_id":null,"co_author_invite_id":7296564,"email":"c***a@gmail.com","display_order":3,"name":"T. Vasista","title":"Enhancing Customer Loyalty with Market Basket Analysis Using Innovative Methods: A Python Implementation Approach"},{"id":39189108,"work_id":92760704,"tagging_user_id":805201,"tagged_user_id":185710531,"co_author_invite_id":null,"email":"t***a@gmail.com","display_order":4,"name":"Gopi Vasista","title":"Enhancing Customer Loyalty with Market Basket Analysis Using Innovative Methods: A Python Implementation Approach"}],"downloadable_attachments":[{"id":95684375,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/95684375/thumbnails/1.jpg","file_name":"IJICC_MBA_Python.pdf","download_url":"https://www.academia.edu/attachments/95684375/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Enhancing_Customer_Loyalty_with_Market_B.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95684375/IJICC_MBA_Python-libre.pdf?1670918032=\u0026response-content-disposition=attachment%3B+filename%3DEnhancing_Customer_Loyalty_with_Market_B.pdf\u0026Expires=1733098142\u0026Signature=N4aPFDRzjIBuWwUqCCy4snby~5XpJfXVGY0y7-yeoEzGf9gGe6sTwAio-bPG4ihnRs8L3-PEJpC63uQLZIIh4J~T5E3pCUpNyaiRacUChwexWd1JpDSUcdRDy5tyUTNPOdJid7D6d6xitAXmKr8UMYzWOOGz1EzisIRFwTfwc8tnNqz0mBnv5c21Nc19q8KpVapyydztpdypyteKFOJdhCShM2BxuZ0-RQixQzfT6UQqHrm62ipeiJcwbXq7GZbwIQWPsxjy8brjOeE~RfkvGNSURiSWdJ9vZykoWRbKMgUErGYw0Z0UQ47Uhs76HNq8R2ihSuyVE~JyaYlnorjcRg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Enhancing_Customer_Loyalty_with_Market_Basket_Analysis_Using_Innovative_Methods_A_Python_Implementation_Approach","translated_slug":"","page_count":18,"language":"en","content_type":"Work","owner":{"id":805201,"first_name":"Tgk","middle_initials":null,"last_name":"Vasista","page_name":"TgkVasista","domain_name":"king-saud","created_at":"2011-10-01T22:36:22.310-07:00","display_name":"Tgk Vasista","url":"https://king-saud.academia.edu/TgkVasista"},"attachments":[{"id":95684375,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/95684375/thumbnails/1.jpg","file_name":"IJICC_MBA_Python.pdf","download_url":"https://www.academia.edu/attachments/95684375/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Enhancing_Customer_Loyalty_with_Market_B.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/95684375/IJICC_MBA_Python-libre.pdf?1670918032=\u0026response-content-disposition=attachment%3B+filename%3DEnhancing_Customer_Loyalty_with_Market_B.pdf\u0026Expires=1733098142\u0026Signature=N4aPFDRzjIBuWwUqCCy4snby~5XpJfXVGY0y7-yeoEzGf9gGe6sTwAio-bPG4ihnRs8L3-PEJpC63uQLZIIh4J~T5E3pCUpNyaiRacUChwexWd1JpDSUcdRDy5tyUTNPOdJid7D6d6xitAXmKr8UMYzWOOGz1EzisIRFwTfwc8tnNqz0mBnv5c21Nc19q8KpVapyydztpdypyteKFOJdhCShM2BxuZ0-RQixQzfT6UQqHrm62ipeiJcwbXq7GZbwIQWPsxjy8brjOeE~RfkvGNSURiSWdJ9vZykoWRbKMgUErGYw0Z0UQ47Uhs76HNq8R2ihSuyVE~JyaYlnorjcRg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); 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For this purpose a unified model that allows a better understanding on the nature and significance of strategic alignment in theory and practice called 'Abstraction Standardization Customization Personalization model' (ASCP Model) is conceived based on Abstraction-Standardization-Concretization Methodology. Classification is an important tool for perception and can be found in numerous scientific disciplines. Several application areas of classification are described in the context of information modeling. The usefulness of classification both as a reference model and a methodology is required for the purpose of systematically positioning the macro level problems in various layers defined in the classification scheme and is adopted in ASCP Model. ASCP Model is a classification scheme to drive and derive Process Standardization. Consistency in operations is necessary for claiming the process as a reference to set of standard patterns. Business Process Modeling has undoubtedly emerged as a popular and relevant practice in this regard. Our findings suggest that the critical areas of concern are standardization of processes, process modeling approaches, identification of the value and its proposition in business process modeling and in model-driven process execution. It is believed that ASCP both as a model and methodology is expected to persist as a road for building standard process blocks targeting the digital governance and digital business community in the future.","publication_date":{"day":null,"month":null,"year":2013,"errors":{}},"publication_name":"Journal of Supply Chain and Customer Relationship Management","grobid_abstract_attachment_id":93939137},"translated_abstract":null,"internal_url":"https://www.academia.edu/90353047/Achieving_Process_Standardization_in_Digital_Society_with_ASCP_Model_","translated_internal_url":"","created_at":"2022-11-09T01:23:59.415-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":805201,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":93939137,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/93939137/thumbnails/1.jpg","file_name":"307ee81a0b63c5539fbf6de4c3ef8eb8e049.pdf","download_url":"https://www.academia.edu/attachments/93939137/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Achieving_Process_Standardization_in_Dig.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/93939137/307ee81a0b63c5539fbf6de4c3ef8eb8e049-libre.pdf?1667986342=\u0026response-content-disposition=attachment%3B+filename%3DAchieving_Process_Standardization_in_Dig.pdf\u0026Expires=1733098142\u0026Signature=d3juK2YeXn4jFUIe3lHOE-QDdQN-rKL4KBvqKmD99D2lutN2gFdANvmI7ryHfX8RCTs3vJZu8CL2wUquMIaz9BWI5rBmdepanJAQdQ5zi~Xg9Oq90eJWjhxDxLw6n9W8kJ0ozwX5u95tBm~WyAN5BuxJc9D-f8YY8ijuZffcSYsthwWTh05Ffhtj9Ts1z27eHMYzkvxyRPz3dgo00-f0LeC1uwd8PhD7ej2llXRxoyPAQcYmyv8aXNSNqUWBh6Osnl8cCe2FCFcuJMFOEdc7rcyaQC2G6izbfLNv2ZBnFwz~-J9GPLJSQbwrjJJJKXLyRKqLA8HbYQLVXgn3NFlzKQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Achieving_Process_Standardization_in_Digital_Society_with_ASCP_Model_","translated_slug":"","page_count":12,"language":"en","content_type":"Work","owner":{"id":805201,"first_name":"Tgk","middle_initials":null,"last_name":"Vasista","page_name":"TgkVasista","domain_name":"king-saud","created_at":"2011-10-01T22:36:22.310-07:00","display_name":"Tgk Vasista","url":"https://king-saud.academia.edu/TgkVasista"},"attachments":[{"id":93939137,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/93939137/thumbnails/1.jpg","file_name":"307ee81a0b63c5539fbf6de4c3ef8eb8e049.pdf","download_url":"https://www.academia.edu/attachments/93939137/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Achieving_Process_Standardization_in_Dig.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/93939137/307ee81a0b63c5539fbf6de4c3ef8eb8e049-libre.pdf?1667986342=\u0026response-content-disposition=attachment%3B+filename%3DAchieving_Process_Standardization_in_Dig.pdf\u0026Expires=1733098142\u0026Signature=d3juK2YeXn4jFUIe3lHOE-QDdQN-rKL4KBvqKmD99D2lutN2gFdANvmI7ryHfX8RCTs3vJZu8CL2wUquMIaz9BWI5rBmdepanJAQdQ5zi~Xg9Oq90eJWjhxDxLw6n9W8kJ0ozwX5u95tBm~WyAN5BuxJc9D-f8YY8ijuZffcSYsthwWTh05Ffhtj9Ts1z27eHMYzkvxyRPz3dgo00-f0LeC1uwd8PhD7ej2llXRxoyPAQcYmyv8aXNSNqUWBh6Osnl8cCe2FCFcuJMFOEdc7rcyaQC2G6izbfLNv2ZBnFwz~-J9GPLJSQbwrjJJJKXLyRKqLA8HbYQLVXgn3NFlzKQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":43492,"name":"Standardization","url":"https://www.academia.edu/Documents/in/Standardization"}],"urls":[{"id":25743284,"url":"http://www.ibimapublishing.com/journals/JSCCRM/2013/759359/759359.html"}]}, dispatcherData: dispatcherData }); 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Corrupt officials and greedy lawyers may instead of speeding up, actually can cause administrative delays in which they have no effect on outcomes but transfer apparent responsibility for decision from elected political officials to agencies or courts. So reformation in administrative procedure in the form of promoting e-government can provide a net benefit to citizens. Developing countries usually face problem of having insufficient infrastructure in fulfilling the needs of providing citizen centric or user centric e-services in various domains such as Judiciary Administration. SaaS based Cloud computing model has a potential to offer solutions in the form of automated useroriented legal case management system while addressing the capital expenditure and it's Return on Investment. It provides service oriented access to end users without compromising on security. It reduces total cost of ownership, facilitates easy deployment and maintenance services by standardizing services and increases end user satisfaction levels. When properly applied to the development of E-Governance Applications, SaaS based Cloud computing architecture models can transform the nation into an Information Society. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84411727"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/84411727/Strategic_Cost_Management_for_Construction_Project_Success_A_Systematic_Study"><img alt="Research paper thumbnail of Strategic Cost Management for Construction Project Success : A Systematic Study" class="work-thumbnail" src="https://attachments.academia-assets.com/89445755/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84411727/Strategic_Cost_Management_for_Construction_Project_Success_A_Systematic_Study">Strategic Cost Management for Construction Project Success : A Systematic Study</a></div><div class="wp-workCard_item"><span>Civil Engineering and Urban Planning: An International Journal (CiVEJ)</span><span>, 2017</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c4030d167548e3a42b001d3df3e800b4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89445755,"asset_id":84411727,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89445755/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="84411727"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84411727"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84411727; 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Many projects start with good ideas, huge investments and great efforts. However, most of them do not achieve much success. A major contribution to unsuccessful projects is the lack of understanding on scope, time, cost and quality. Projects as powerful strategic weapons when initiated create economic value and competitive advantage. The objective of the research is to explicitly declare the scope of the research to by considering only the scope, time, cost and quality as process success parameters and how specifically the cost element would influence the project success when all other elements or factors other than cost are represented in terms of cost factor along with the contract conditions as basic rules or constraints that drive the strategic cost based on applying the CRASP methodology concept. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="7352630" id="drafts"><div class="js-work-strip profile--work_container" data-work-id="92761107"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/92761107/Design_Thinking_An_Innovative_Approach_for_Engineering_Design_and_Technological_Development"><img alt="Research paper thumbnail of Design Thinking: An Innovative Approach for Engineering Design and Technological Development" class="work-thumbnail" src="https://attachments.academia-assets.com/95684694/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/92761107/Design_Thinking_An_Innovative_Approach_for_Engineering_Design_and_Technological_Development">Design Thinking: An Innovative Approach for Engineering Design and Technological Development</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Innovation is an important concern to every organization as it plays important role in human deve...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Innovation is an important concern to every organization as it plays important role in human development and growth of an organization or a society. Innovation is expected as an inclusive design thinking based product, process or an organizational design. Developing countries, developed countries including European Union are seeing design thinking as an important means to achieve innovation. Design thinking is the sensibility of the designer in adopting and applying the design methods to match the customer needs through determining the technical feasibility and business strategy in order to produce the customer value and market opportunity. 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This is even well observed in the field of civil engineering (i.e. construction activities development). There is a concern and need to enable better living standards to be made as sustainable by means of not impacting the environmental aspects. This concern has addressed the demand of creating innovative approaches and technologies to be considered for the purpose of minimizing resources such as energy, water and material consumption that decreases the green house effect and enhance the surrounding ecological systems and services. There is also concern about how to improve construction practices in order to minimize their detrimental affects on natural environment. The environmental impacts of construction, green buildings, designing etc. have captured the attention of building professional across the world. Building performance is a major concern of professionals in building industry and environmental building performance has emerged as one of the major issues in sustainable construction (Ding, 2008). Therefore objective of this paper is to review, overview, analyze, change and classify the factors of sustainability of the current built environment assessment related to construction industry by appraising factors derived from the green building rating system models. Such derived factors will be subjected to make an attempt to classify them according to the basic triple bottom line sustainability factors as fundamental categories of sustainability assessment.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="413d5dcd61fcb455946f6db34f1f060a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":54204728,"asset_id":34301654,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/54204728/download_file?st=MTczMzA5NDU0Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="34301654"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="34301654"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 34301654; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=34301654]").text(description); $(".js-view-count[data-work-id=34301654]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 34301654; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='34301654']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 34301654, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "413d5dcd61fcb455946f6db34f1f060a" } } $('.js-work-strip[data-work-id=34301654]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":34301654,"title":"Environmental Impact Assessment in Green Building Supply and Value Chain Construction Project Management for Sustainable Development","translated_title":"","metadata":{"abstract":"Education and Research concerns pertaining to environment and sustainability issues such as deterioration in resources, decrease in biodiversity and climate change are becoming evident contemporarily. 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