{"id":3126,"date":"2019-12-31T06:55:32","date_gmt":"2019-12-31T06:55:32","guid":{"rendered":"http:\/\/34.74.67.11\/?page_id=3126"},"modified":"2021-05-17T07:19:27","modified_gmt":"2021-05-17T07:19:27","slug":"revenue-forecasting","status":"publish","type":"page","link":"https:\/\/www.iventura.ai\/index.php\/revenue-forecasting\/","title":{"rendered":"Revenue Forecasting"},"content":{"rendered":"<section class=\"kc-elm kc-css-819176 kc_row\"><div class=\"kc-row-container  kc-container\"><div class=\"kc-wrap-columns\"><div class=\"kc-elm kc-css-120009 kc_col-sm-12 kc_column kc_col-sm-12\"><div class=\"kc-col-container\"><div class=\"kc-elm kc-css-499722 kc_row kc_row_inner\"><div class=\"kc-elm kc-css-518569 kc_col-sm-12 kc_column_inner kc_col-sm-12\"><div class=\"kc_wrapper kc-col-inner-container\"><div class=\"kc-elm kc-css-623783 kc_text_block\"><\/p>\n<h4 dir=\"ltr\" style=\"line-height: 1.2; margin-top: 0pt; margin-bottom: 0pt; color: #00aeef;\"><strong>Problem Statement:<\/strong><\/h4>\n<ol>\n<li>Many entrepreneurs complain that building forecasts with any degree of accuracy takes a lot of time&#8211;time that could be spent selling rather than planning. But few investors will put money in your business if you&#8217;re unable to provide a set of thoughtful forecasts.More important, proper financial forecasts will help you develop operational and staffing plans that will help make your business a success.<\/li>\n<li>Company fetches the financial data from various clients as individual excel sheets. These sheets are then clubbed as zip file in empirical model sheet . Financial Company calculates regression model using statistical way.But we can solve this problem using machine learning.Healthcare company wants to forecast sales of revenues from empirical sheet based on past sales for better results,reduce labour work and fastest computations.<\/li>\n<\/ol>\n<h4 dir=\"ltr\" style=\"line-height: 1.2; margin-top: 0pt; margin-bottom: 0pt; color: #00aeef;\"><strong>Challenge:<\/strong><\/h4>\n<ul>\n<li>The challenge is that the building forecasts with a degree of accuracy take a lot of time. It is also important to take the seasonal &amp; holiday cycles into account to make it more realistic. Thus time-series analysis was used to build the prediction<\/li>\n<\/ul>\n<p>\n<\/div><\/div><\/div><\/div><div class=\"kc-elm kc-css-496526 kc_row kc_row_inner\"><div class=\"kc-elm kc-css-742631 kc_col-sm-12 kc_column_inner kc_col-sm-12\"><div class=\"kc_wrapper kc-col-inner-container\"><div class=\"kc-elm kc-css-482845 kc_shortcode kc_single_image\">\n\n        <img decoding=\"async\" src=\"https:\/\/www.iventura.ai\/wp-content\/uploads\/2019\/12\/RFgif.gif\" class=\"\" alt=\"\" \/>    <\/div>\n<div class=\"kc-elm kc-css-877668 kc_text_block\"><\/p>\n<h4 dir=\"ltr\" style=\"line-height: 1.2; margin-top: 0pt; margin-bottom: 0pt; color: #00aeef;\"><strong>Solution:<\/strong><\/h4>\n<ol>\n<li>iVentura Machine Learning Platform was used for building the solution. iVentura provides the complete ecosystem for data scientists to build models without worrying about the underlying Infra &amp; Security. Either for a team or an individual data scientist, iVentura is ideally suited as a platform of choice.<\/li>\n<li>To deal with the above problem statement ,datasets needs to be analysed and evaluated with metrics to acquire best outcome. Here we go:<\/li>\n<li>1) Extract zip file and read empirical model sheet.<\/li>\n<li>2) Extracted excel file is in unstructured format.perform all data preprocessing operation according to excel file sets.<\/li>\n<li>3) Analysis month sales graph to understand pattern.<\/li>\n<li>4) Perform dickey-fuller stationarity test<\/li>\n<li>5) Trained model using FBProphet on time series data<\/li>\n<li>6) Plot forecasting results and revenue component<\/li>\n<li>7) Forecast for quarterly sales<\/li>\n<li>8) Save the data into pickle<\/li>\n<li>9) Deployment &amp; Visualization<\/li>\n<\/ol>\n<p>\n<\/div><\/div><\/div><\/div><div class=\"kc-elm kc-css-38698 kc_row kc_row_inner\"><div class=\"kc-elm kc-css-851532 kc_col-sm-12 kc_column_inner kc_col-sm-12\"><div class=\"kc_wrapper kc-col-inner-container\">\n<div class=\"kc-elm kc-css-155480 kc-title-wrap \">\n\n\t<h3 class=\"kc_title\">SOLUTION WORKFLOW<\/h3>\n<\/div>\n<div class=\"kc-elm kc-css-400856 kc_shortcode kc_single_image\">\n\n        <img decoding=\"async\" src=\"https:\/\/www.iventura.ai\/wp-content\/uploads\/2020\/01\/WF4-e1578550947110.png\" class=\"\" alt=\"\" \/>    <\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/section>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"tpl-king-composer.php","meta":{"footnotes":""},"yst_prominent_words":[92,82,95,87,80,97,84,79,96,86,88,94,93,89,85,91,81,90,98,83],"class_list":["post-3126","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.iventura.ai\/index.php\/wp-json\/wp\/v2\/pages\/3126","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.iventura.ai\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.iventura.ai\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.iventura.ai\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.iventura.ai\/index.php\/wp-json\/wp\/v2\/comments?post=3126"}],"version-history":[{"count":29,"href":"https:\/\/www.iventura.ai\/index.php\/wp-json\/wp\/v2\/pages\/3126\/revisions"}],"predecessor-version":[{"id":3803,"href":"https:\/\/www.iventura.ai\/index.php\/wp-json\/wp\/v2\/pages\/3126\/revisions\/3803"}],"wp:attachment":[{"href":"https:\/\/www.iventura.ai\/index.php\/wp-json\/wp\/v2\/media?parent=3126"}],"wp:term":[{"taxonomy":"yst_prominent_words","embeddable":true,"href":"https:\/\/www.iventura.ai\/index.php\/wp-json\/wp\/v2\/yst_prominent_words?post=3126"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}