{"id":3523,"date":"2023-08-31T11:12:58","date_gmt":"2023-08-31T11:12:58","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/telecommunications-churn\/"},"modified":"2026-10-06T13:35:23","modified_gmt":"2026-10-06T11:35:23","slug":"telecommunications-churn","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/","title":{"rendered":"Telecommunications customer churn classification"},"content":{"rendered":"<style>\n.ndb{--ndb-code-bg:#f3f7fa;--ndb-code-fg:#12354b;box-sizing:border-box;width:100%;margin-left:0;margin-right:0;padding:22px 0 14px;background:#fff;color:#1b2635;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif}\n.ndb *{box-sizing:border-box}.ndb-wrap{width:min(calc(100% - 48px),1160px);margin:0 auto}.ndb a{text-decoration:none}\n.ndb-executive{margin:0 0 34px;padding:32px;border-radius:20px;}\n.ndb-executive h2{margin:0 0 12px;font-size:30px}.ndb-executive p{font-size:18px;line-height:1.55}\n.ndb-kpis{display:grid;grid-template-columns:repeat(4,minmax(0,1fr));gap:14px;margin-top:22px}.ndb-kpi{padding:18px;border:1px solid rgba(255,255,255,.18);border-radius:14px}.ndb-kpi strong{display:block;font-size:25px}.ndb-kpi span{font-size:13px}\n.ndb-actions,.ndb-audience,.ndb-downloads{display:flex;flex-wrap:wrap;justify-content:center;gap:12px;margin:22px 0}\n.ndb-toc{display:flex;flex-wrap:wrap;justify-content:center;gap:10px;margin:0 0 42px;padding:0;list-style:none}.ndb-toc a,.ndb-audience span{display:block;padding:9px 14px;border-radius:20px;background:#e9f2f8;color:#12354b;font-weight:600}\n.ndb-card{margin:0 0 54px;scroll-margin-top:90px}.ndb-card h2{margin:0 0 20px;padding-bottom:12px;border-bottom:1px solid #dbe5ec;color:#001233;font-size:24px}.ndb-card h3{margin:30px 0 12px;color:#12354b;font-size:19px}.ndb-card p,.ndb-card li{font-size:16.5px;line-height:1.6}.ndb-card img{display:block;width:auto;max-width:min(640px,100%);height:auto;margin:24px auto;border-radius:12px}.ndb-card img.ndb-architecture{width:min(1000px,100%);max-width:100%;margin:0 auto}.ndb-card>figure{width:min(760px,100%);margin:24px auto}.ndb-card figcaption{color:#405361;font-size:14px;line-height:1.45}.ndb-architecture-figure{width:min(1000px,100%)!important}.ndb-architecture-link{display:block}.ndb-architecture-figure figcaption{margin-top:12px}\n.ndb-value-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:16px;margin:22px 0}.ndb-value{padding:20px;border:1px solid #dce8ef;border-radius:14px;background:#f8fbfd}\n.ndb-note{margin:20px 0;padding:18px 20px;border-left:4px solid #56a1c8;border-radius:0 12px 12px 0;background:#f6fafc}.ndb-note--warning{border-left-color:#d79a29;background:#fff9ed}\n.ndb-table-scroll{max-width:100%;overflow-x:auto}.ndb-card table{width:auto;max-width:100%;margin:24px auto;border-collapse:collapse;background:#fff}.ndb-card th,.ndb-card td{padding:11px 16px;border-bottom:1px solid #e2e9ee}.ndb-card thead th{background:#12354b!important;color:#fff!important}.ndb-card tbody th{background:#eaf2f6!important;color:#12354b!important;text-align:left}.ndb-card tbody tr:nth-child(even) th{background:#f4f8fa!important}.ndb-card tbody td{color:#33424f!important}\n.ndb-pipeline{display:grid;grid-template-columns:repeat(auto-fit,minmax(min(100%,190px),1fr));gap:12px;margin:24px 0;padding:0;list-style:none}.ndb-pipeline>li{padding:16px;border:1px solid #dce8ef;border-radius:12px;background:#f8fbfd}.ndb-pipeline>li strong{display:block;margin-bottom:5px;color:#12354b}.ndb-pipeline>br,.ndb-pipeline>p:empty{display:none!important}\n.ndb-figure-grid{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));align-items:start;gap:20px}.ndb-figure-grid>.ndb-figure-item{min-width:0}.ndb-figure-grid>br,.ndb-figure-grid>p:empty{display:none!important}.ndb-figure-grid figure{margin:0;padding:16px;border:1px solid #dce8ef;border-radius:15px;background:#f8fbfd}.ndb-figure-grid img{width:100%;max-width:100%;margin:0 auto 12px}\n.ndb-comparison-grid{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));align-items:start;gap:20px;margin:24px 0}.ndb-comparison-grid>.ndb-comparison-item{min-width:0}.ndb-comparison-grid>br,.ndb-comparison-grid>p:empty{display:none!important}.ndb-interactive-bundle{width:min(840px,100%);margin:24px auto}.ndb-interactive-figure{margin:0;padding:14px;border:1px solid #dce8ef;border-radius:15px;background:#fff;overflow:hidden}.ndb-interactive-chart{width:100%;height:500px;min-height:500px}.ndb-interactive-figure figcaption{margin-top:10px}.ndb-figure-grid .ndb-interactive-chart,.ndb-comparison-grid .ndb-interactive-chart{height:520px;min-height:520px}\n.ndb pre,.ndb .wp-block-code,.ndb pre[class*=\"language-\"]{display:block;max-width:100%;overflow:auto;padding:18px 20px!important;border:1px solid #d5e2e9!important;border-radius:12px!important;background:var(--ndb-code-bg)!important;color:var(--ndb-code-fg)!important;box-shadow:none!important;text-shadow:none!important;white-space:pre}\n.ndb pre code,.ndb pre code *,.ndb .wp-block-code code,.ndb .wp-block-code code *{border:0!important;background:transparent!important;color:var(--ndb-code-fg)!important;box-shadow:none!important;text-shadow:none!important;font-family:Consolas,\"Liberation Mono\",monospace!important;font-size:14px;line-height:1.55}\n.ndb :not(pre)>code{padding:.08em .32em;border-radius:4px;background:#e6eef3!important;color:#12354b!important}\n.ndb .ndb-expression{max-width:100%;overflow:auto;margin:22px auto;padding:18px 20px;border:1px solid #d5e2e9;border-radius:12px;background:#fff!important;color:#12354b!important;text-align:center}\n.ndb .ndb-expression math,.ndb .ndb-expression mjx-container,.ndb .ndb-expression .MathJax{background:transparent!important;color:#12354b!important}\n@media(max-width:1020px){.ndb-figure-grid,.ndb-comparison-grid{grid-template-columns:1fr}.ndb-figure-grid .ndb-interactive-chart,.ndb-comparison-grid .ndb-interactive-chart{height:500px;min-height:500px}}\n@media(max-width:820px){.ndb-kpis{grid-template-columns:repeat(2,minmax(0,1fr))}.ndb-value-grid{grid-template-columns:1fr}}\n@media(max-width:620px){.ndb{padding:12px 0}.ndb-kpis{grid-template-columns:1fr}.ndb-executive{padding:24px 20px}.ndb-card table{font-size:13px}.ndb-card th,.ndb-card td{padding:9px 8px}}\n.ndb .ndb-calculator-grid>br,.ndb .ndb-calculator-grid>p:empty{display:none!important}\n\/* nd-shared-components:start *\/html body.nds-shared:not(.wp-admin), html body.nds-shared:not(.wp-admin) :is(.site-main,.site-content,.entry-content,.page-content,.nd-native-article,.nd-native-article__content,.nd-home-page,.nd-product-page,.nd-company-page,.nd-contact-page,.nd-downloads-page,.nd-account-page,.nd-plain-page,.nd-blog-page,.ndlh-page,.ndhub,.ndth,.ndts,.ndareas,.ndarea,.ndeu), html body:not(.wp-admin) :is(.ndb,.nds,.ndg,.nd-page-surface){background-color:#fff!important;background-image:none!important} :is(.ndb,.nds) :is(.ndb-executive,.nds-executive,.ndb-highlight){background:#f4f7f9;color:#193645} :is(.ndb,.nds) :is(.ndb-executive,.nds-executive,.ndb-highlight) :is(h2,h3){color:#193645} :is(.ndb,.nds) :is(.ndb-executive,.nds-executive,.ndb-highlight) p{color:#52636f} :is(.ndb,.nds) :is(.ndb-kpi,.nds-kpi){background:#245e80;color:#fff} :is(.ndb,.nds) :is(.ndb-kpi,.nds-kpi) strong{color:#fff} :is(.ndb,.nds) :is(.ndb-kpi,.nds-kpi) span{color:#d9edf7} :is(.ndb,.nds) :is(.aui-button,.ndb-actions a,.ndb-downloads a,.nds-actions a,.nds-downloads a){box-sizing:border-box;display:inline-flex;align-items:center;justify-content:center;gap:8px;min-width:0;min-height:48px;max-width:100%;height:auto;padding:11px 20px;background:#176b91;color:#fff;border:1px solid #176b91;border-radius:6px;font-family:Roboto,\"Segoe UI\",Arial,sans-serif;font-size:16px;font-weight:700;line-height:1.5;letter-spacing:normal;text-align:center;text-decoration:none;text-transform:none;white-space:normal;overflow-wrap:anywhere;box-shadow:none;cursor:pointer} :is(.ndb,.nds) :is(.aui-button,.ndb-actions a,.ndb-downloads a,.nds-actions a,.nds-downloads a):is(:hover,:focus-visible){background:#125574;color:#fff;border-color:#125574} :is(.ndb,.nds) :is(.aui-button,.ndb-actions a,.ndb-downloads a,.nds-actions a,.nds-downloads a):focus-visible{outline:3px solid #176b91;outline-offset:4px} :is(.ndb,.nds) .aui-button.aui-button--secondary{background:#fff;color:#176b91;border-color:#176b91} :is(.ndb,.nds) .aui-button.aui-button--secondary:is(:hover,:focus-visible){background:#f4f7f9;color:#125574;border-color:#125574} :is(.ndb,.nds) .aui-button:is(:disabled,[aria-disabled=\"true\"],.disabled){opacity:.55;cursor:not-allowed} @media(forced-colors:active){:is(.ndb,.nds) .aui-button{border-color:ButtonText}:is(.ndb,.nds) .aui-button:focus-visible{outline-color:Highlight}} html body :is(.nd-native-article__content,.entry-content,.page-content,.ndb,.nds,.ndg) :is(.nd-table-scroll,.ndb-table-scroll,.nds-table-scroll,.ndg-table-wrap,.ndb-table-wrap,.ndb-confusion-wrap,.wp-block-table){box-sizing:border-box;display:block!important;width:100%!important;min-width:0!important;max-width:100%!important;padding:0!important;background:transparent!important;border:0!important;box-shadow:none!important;overflow-x:auto!important;overscroll-behavior-inline:contain;-webkit-overflow-scrolling:touch} html body :is(.nd-native-article__content,.entry-content,.page-content,.ndb,.nds,.ndg) :is(.nd-table-scroll,.ndb-table-scroll,.nds-table-scroll,.ndg-table-wrap,.ndb-table-wrap,.ndb-confusion-wrap,.wp-block-table)>table, html body :is(.nd-native-article__content,.entry-content,.page-content,.ndb,.nds,.ndg) table.nd-content-table{display:table!important;width:auto!important;min-width:0!important;max-width:100%!important;margin:24px auto!important;float:none!important;table-layout:auto!important;overflow:visible!important} html body :is(.nd-native-article__content,.entry-content,.page-content,.ndb,.nds,.ndg) .nd-table-scroll:focus-visible{outline:3px solid #176b91;outline-offset:3px} html body.nds-shared:not(.wp-admin) .aui-button.aui-button, html body :is(.ndb,.nds,.nd-page-surface) .aui-button.aui-button{box-sizing:border-box;min-height:48px!important;height:auto!important;padding:11px 20px!important;border:1px solid #176b91!important;border-radius:6px!important;font-family:Roboto,\"Segoe UI\",Arial,sans-serif!important;font-size:16px!important;font-weight:700!important;line-height:1.5!important;color:#fff!important;-webkit-text-fill-color:#fff!important;background:#176b91!important;text-shadow:none!important;box-shadow:none!important;transform:none!important;translate:none!important;scale:none!important;rotate:none!important;transition:none!important;animation:none!important} html body.nds-shared:not(.wp-admin) .aui-button.aui-button:is(:hover,:focus-visible), html body :is(.ndb,.nds,.nd-page-surface) .aui-button.aui-button:is(:hover,:focus-visible){color:#fff!important;-webkit-text-fill-color:#fff!important;background:#125574!important;border-color:#125574!important} html body.nds-shared:not(.wp-admin) .aui-button.aui-button:is(:hover,:focus,:focus-visible), html body :is(.ndb,.nds,.nd-page-surface) .aui-button.aui-button:is(:hover,:focus,:focus-visible){box-shadow:none!important;transform:none!important;translate:none!important;scale:none!important;rotate:none!important;transition:none!important;animation:none!important} html body.nds-shared:not(.wp-admin) .aui-button.aui-button.aui-button--secondary, html body :is(.ndb,.nds,.nd-page-surface) .aui-button.aui-button.aui-button--secondary{color:#176b91!important;-webkit-text-fill-color:#176b91!important;background:#fff!important;border-color:#176b91!important} html body.nds-shared:not(.wp-admin) .aui-button.aui-button.aui-button--secondary:is(:hover,:focus-visible), html body :is(.ndb,.nds,.nd-page-surface) .aui-button.aui-button.aui-button--secondary:is(:hover,:focus-visible){color:#125574!important;-webkit-text-fill-color:#125574!important;background:#f4f7f9!important;border-color:#125574!important} html body.nds-shared:not(.wp-admin) .aui-button.aui-button:focus-visible, html body :is(.ndb,.nds,.nd-page-surface) .aui-button.aui-button:focus-visible{outline:3px solid #176b91!important;outline-offset:4px!important} html body.nds-shared:not(.wp-admin) .aui-button.aui-button :is(span,strong,b,em,i), html body :is(.ndb,.nds,.nd-page-surface) .aui-button.aui-button :is(span,strong,b,em,i){color:inherit!important;-webkit-text-fill-color:inherit!important} @media(forced-colors:active){html body .aui-button.aui-button{border-color:ButtonText!important}html body .aui-button.aui-button:focus-visible{outline-color:Highlight!important}} html body{--nd-content-width:1160px;--nd-content-gutter:24px} @media(max-width:640px){html body{--nd-content-gutter:20px}} html body.nds-shared .aui{--aui-width:var(--nd-content-width);--aui-gutter:var(--nd-content-gutter)} html body.nds-shared :is(.aui-container,.nd-native-article__content,.nds-article-content,.nd-home-wrap,.nd-product-wrap,.nd-company-main,.ndlh-wrap,.nd-blog-main,.ndhub-wrap,.ndth-wrap,.ndts-wrap,.ndareas-wrap,.ndarea-wrap,.ndeu-wrap,.nd-contact-wrap,.nd-downloads-wrap,.nd-plain-wrap,.nd-account-hero-inner,.nd-account-main), html body :is(.nd-content-container,.ndb-wrap,.nds-wrap,.ndg-wrap){box-sizing:border-box!important;width:min(calc(100% - 2 * var(--nd-content-gutter)),var(--nd-content-width))!important;max-width:var(--nd-content-width)!important;margin-left:auto!important;margin-right:auto!important;padding-left:0!important;padding-right:0!important} html body :is(.ndb,.nds,.ndg){box-sizing:border-box!important;width:100%!important;max-width:100%!important;margin-left:0!important;margin-right:0!important;padding-left:0!important;padding-right:0!important;text-align:left} html body :is(.aui-container,.nd-native-article__content,.nds-article-content,.nd-content-container) :is(.aui-container,.nd-content-container,.ndb-wrap,.nds-wrap,.ndg-wrap,.nds-editorial-wrap){width:100%!important;max-width:100%!important;margin-left:0!important;margin-right:0!important;padding-left:0!important;padding-right:0!important} html body.nds-shared :is(.nd-home-service-grid,.nds-editorial-wrap){width:100%!important;max-width:100%!important;margin-left:0!important;margin-right:0!important} html body.nds-shared :is(.aui-content-copy,.aui-section-heading,.aui-section-note,.nds-legal-copy,.nd-legal-copy):not(.aui-container):not(.nd-content-container){width:100%!important;max-width:none!important;text-align:left!important} html body.nds-shared :is(.aui-section-heading p,.nds-learning-closing h2,.aui-closing-inner>div){max-width:none!important;text-align:left!important} html body :is(.nds-article-section,.ndb-wrap>.ndb-card,.nds-wrap>.nds-card){box-sizing:border-box!important;width:100%!important;max-width:100%!important;padding-left:0!important;padding-right:0!important;text-align:left!important} html body:not(.wp-admin) :is(.nds-article-section,.ndb-card,.nds-card,.aui-content-copy,.nds-legal-copy,.nd-legal-copy,.nd-native-article__content,.nds-article-content,.nds-editorial-page-content,.nd-content-container)>:is(p,h1,h2,h3,h4,ul,ol):not(:has(img,svg,canvas,iframe,video,math,mjx-container,.MathJax)){max-width:none!important;text-align:left!important} html body :is(.ndb,.nds) :is(.nds-comparison-grid,.ndb-comparison-grid,.ndb-figure-grid){width:100%!important;max-width:100%!important;margin-left:0!important;margin-right:0!important} html body.nds-shared main.site-main>.page-content:not(.aui-container):not(:has(.aui-container,.nd-native-article__content,.nds-article-content,.nd-content-container,.nd-home-wrap,.nd-product-wrap,.nd-company-main,.ndlh-wrap,.nd-blog-main,.ndhub-wrap,.ndth-wrap,.ndts-wrap,.ndareas-wrap,.ndarea-wrap,.ndeu-wrap,.nd-contact-wrap,.nd-downloads-wrap,.nd-plain-wrap,.nd-account-hero-inner,.nd-account-main,.ndb-wrap,.nds-wrap,.ndg-wrap)){box-sizing:border-box!important;width:min(calc(100% - 2 * var(--nd-content-gutter)),var(--nd-content-width))!important;max-width:var(--nd-content-width)!important;margin-left:auto!important;margin-right:auto!important;padding-left:0!important;padding-right:0!important} html body :is(main,.ndb,.nds,.ndg,.nd-content-container) :is(figure,.wp-caption,.nd-media-block):not(.wp-block-gallery){ float:none!important;margin-left:auto!important;margin-right:auto!important;max-width:100%;box-sizing:border-box; } html body :is(main,.ndb,.nds,.ndg,.nd-content-container) :is(.nd-content-image,figure>img,figure>a>img,figure>picture>img,.wp-caption>img,.wp-caption>a>img,.nd-media-block>img,.nd-media-block>a>img,.nd-media-block>picture>img){ display:block!important;float:none!important;margin-left:auto!important;margin-right:auto!important;max-width:100%!important;box-sizing:border-box; } html body :is(main,.ndb,.nds,.ndg,.nd-content-container) :is(.nd-media-link,.nd-media-block>picture){display:block;max-width:100%;margin-left:auto!important;margin-right:auto!important} html body :is(.nd-media-group,.ndb-figure-grid,.ndb-selection-grid,.nds-figure-grid,.ndb-comparison-grid,.nds-comparison-grid,.nd-comparison-grid,.wp-block-gallery){ display:flex!important;flex-wrap:wrap!important;justify-content:center!important;align-items:flex-start!important;gap:24px!important; max-width:100%!important;margin-left:auto!important;margin-right:auto!important;box-sizing:border-box; } html body :is(.ndb-figure-grid,.ndb-selection-grid,.nds-figure-grid,.ndb-comparison-grid,.nds-comparison-grid,.nd-comparison-grid)>:is(figure,.ndb-figure-item,.nds-figure-item,.ndb-comparison-item,.nds-comparison-item,.nd-comparison-item,.ndb-interactive-bundle,.nds-interactive-bundle){ flex:0 1 calc((100% - 24px)\/2)!important;width:calc((100% - 24px)\/2)!important;min-width:0!important;max-width:100%!important;margin:0!important;box-sizing:border-box; } html body .nd-media-group>:is(img,a,picture,figure,.nd-media-item){flex:0 1 auto;min-width:0;max-width:calc((100% - 24px)\/2)!important;margin:0!important;box-sizing:border-box} html body .nd-media-group>:is(a,picture,.nd-media-item)>img{max-width:100%!important} html body .nd-media-group>:is(.nd-content-image,.nd-media-link,.nd-media-item){margin-left:0!important;margin-right:0!important} html body :is(.nd-media-group,.ndb-figure-grid,.ndb-selection-grid,.nds-figure-grid,.ndb-comparison-grid,.nds-comparison-grid,.nd-comparison-grid)>:is(br,p:empty){display:none!important} html body .nd-media-helper{display:none!important} @media(max-width:1020px){ html body :is(.ndb-figure-grid,.ndb-selection-grid,.nds-figure-grid,.ndb-comparison-grid,.nds-comparison-grid,.nd-comparison-grid)>:is(figure,.ndb-figure-item,.nds-figure-item,.ndb-comparison-item,.nds-comparison-item,.nd-comparison-item,.ndb-interactive-bundle,.nds-interactive-bundle){flex-basis:100%!important;width:100%!important} } @media(max-width:640px){ html body .nd-media-group>:is(img,a,picture,figure,.nd-media-item){max-width:100%!important} } html body.nds-shared .aui.nds-content-scope :is(h1,h2,h3,h4,h5,h6), html body.nds-shared :is(main,.ndb,.nds,.ndg,.nd-content-container) :is(h1,h2,h3,h4,h5,h6), html body :is(main,.ndb,.nds,.ndg,.nd-content-container) :is(h1,h2,h3,h4,h5,h6){ font-family:Outfit,Roboto,Arial,sans-serif!important;font-weight:600!important; font-style:normal;letter-spacing:-.02em;text-transform:none;overflow-wrap:anywhere; } html body.nds-shared :is(main,.ndb,.nds,.ndg,.nd-content-container) :is(h1,h2,h3,h4,h5,h6)>:is(span,strong,b,a), html body :is(main,.ndb,.nds,.ndg,.nd-content-container) :is(h1,h2,h3,h4,h5,h6)>:is(span,strong,b,a){ font-family:inherit!important;font-weight:inherit!important;font-size:inherit!important;line-height:inherit!important; } html body.nds-shared .aui.nds-content-scope .nd-section-title, html body :is(.nd-section-title,.ndb-card>h2,.nds-card>h2){ display:block!important;box-sizing:border-box;width:100%!important;max-width:none!important; color:#193645!important;font-size:32px!important;font-weight:600!important;line-height:1.25!important; text-align:left!important;letter-spacing:-.02em!important; margin:0 0 20px!important;padding:0 0 12px!important; border:0!important;border-bottom:1px solid #dbe5ec!important;border-radius:0!important; background:none!important;box-shadow:none!important; } html body .nd-section-title>:is(span,strong,b,a){color:inherit!important} html body .nd-section-title::after{content:none!important} html body :is(.nd-heading-section,.nd-heading-step){border-top:0!important} html body .nd-heading-step{display:block!important;padding-left:0!important;padding-right:0!important} html body .nd-heading-step>:is(.ndg-step__body,.nds-step-body){width:100%!important;max-width:none!important;margin-left:0!important;padding-left:0!important} html body :is(.nd-heading-number-source,.nd-heading-divider){display:none!important} html body .nd-section-title>.nd-heading-number{display:inline!important;color:inherit!important;background:none!important;border:0!important;border-radius:0!important;box-shadow:none!important;padding:0!important;margin:0!important;font:inherit!important;white-space:normal} @media(max-width:640px){html body.nds-shared .aui.nds-content-scope .nd-section-title,html body :is(.nd-section-title,.ndb-card>h2,.nds-card>h2){font-size:28px!important}} html body{--nd-photo-tint:rgba(220,238,250,.9);--nd-photo-surface:#dceefa} html body :is(.ndlh-area-card,.nd-photo-card){position:relative;isolation:isolate;overflow:hidden;background-color:var(--nd-photo-surface)!important} html body .nd-photo-card{background-image:var(--nd-photo-bg,none);background-size:cover;background-position:center} html body :is(.ndlh-area-card,.nd-photo-card)::before{ content:\"\"!important;position:absolute!important;inset:0!important;z-index:1!important; background:var(--nd-photo-tint)!important;opacity:1!important;pointer-events:none!important; } html body :is(.ndlh-area-card,.nd-photo-card)>img{ position:absolute!important;inset:0!important;width:100%!important;height:100%!important; max-width:100%!important;object-fit:cover!important;margin:0!important;z-index:0!important; } html body :is(.ndlh-area-card,.nd-photo-card)>:is(.ndlh-area-body,.nds-card-body,.nd-photo-card__body){position:relative!important;z-index:2!important} html body .nd-photo-card__body{padding:24px;color:#193645} html body :is(.ndlh-area-card,.nd-photo-card) :is(.ndlh-area-body,.nds-card-body,.nd-photo-card__body) :is(h2,h3,h4){color:#193645!important} html body :is(.ndlh-area-card,.nd-photo-card) :is(.ndlh-area-body,.nds-card-body,.nd-photo-card__body) :is(p,li){color:#425563!important} html body :is(.ndlh-area-card,.nd-photo-card) :is(.ndlh-area-body,.nds-card-body,.nd-photo-card__body) a:not(:is(.aui-button,.button,.wp-element-button,.wp-block-button__link,[role=\"button\"])){color:#125574!important} html body :is(.ndareas-group-head,.ndhub-card-head,.nd-photo-header){ background-color:var(--nd-photo-surface)!important; background-image:linear-gradient(var(--nd-photo-tint),var(--nd-photo-tint)),var(--nd-photo-bg,var(--ndareas-bg,var(--ndhub-bg,none)))!important; background-size:cover!important;background-position:center!important; color:#193645; }\/* nd-shared-components:end *\/\n<\/style>\n<div class=\"ndb\">\n<div class=\"ndb-wrap\">\n<section class=\"ndb-executive\">\n<h2>Model churn in the Iranian Churn dataset<\/h2>\n<p>Use 3,150 customer records to study the binary churn label. The current saved network takes 13 inputs and is evaluated on 630 held-out records.<\/p>\n<div class=\"ndb-kpis\">\n<div class=\"ndb-kpi\"><strong>3,150<\/strong><span>Source records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>13<\/strong><span>Encoded input values<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>630<\/strong><span>Testing-role records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>1<\/strong><span>Model outputs<\/span><\/div>\n<\/div>\n<div class=\"ndb-actions\"><a class=\"aui aui-button\" href=\"#7-model-deployment\">Review the current model<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-telecommunicationschurn-licensed-20261006.zip\">Download model and data (ZIP)<\/a><\/div>\n<\/section>\n<ul class=\"ndb-toc\">\n<li><a href=\"#1-industrial-challenge\">Business decision<\/a><\/li><li><a href=\"#2-data-set\">Data set<\/a><\/li><li><a href=\"#3-model\">Model<\/a><\/li><li><a href=\"#4-training\">Training<\/a><\/li><li><a href=\"#5-selection\">Model selection<\/a><\/li><li><a href=\"#6-testing\">Testing<\/a><\/li><li><a href=\"#7-model-deployment\">Deployment<\/a><\/li><li><a href=\"#8-limitations\">Limitations<\/a><\/li>\n<\/ul>\n<section id=\"1-industrial-challenge\" class=\"ndb-card\"><h2>1. Business decision<\/h2><p>Retention analysts can review which customer patterns associate with recorded churn and assess the cost of classification errors. Prediction alone does not determine who benefits from a retention intervention.<\/p><div class=\"ndb-value-grid\"><div class=\"ndb-value-card\"><h3>Customer context<\/h3><p>Use the updated Iranian dataset schema.<\/p><\/div><div class=\"ndb-value-card\"><h3>Threshold trade-off<\/h3><p>Inspect missed churn and unnecessary contacts.<\/p><\/div><div class=\"ndb-value-card\"><h3>Action evidence<\/h3><p>Evaluate retention interventions independently.<\/p><\/div><\/div><div class=\"ndb-audience\"><span>Customer analytics<\/span><span>Retention planning<\/span><span>Model validation<\/span><\/div><div class=\"ndb-note\">Historical customer-record classification, not a causal estimate of retention uplift.<\/div><\/section>\n<section id=\"2-data-set\" class=\"ndb-card\"><h2>2. Data set<\/h2><p>The UCI Iranian Churn Dataset replaces the previous telephone-usage table. Inputs include call failures, complaints, subscription length, usage, tariff, status, age and customer value. Churn = 1 is the positive class.<\/p><p>Source: <a href=\"https:\/\/archive.ics.uci.edu\/dataset\/563\/iranian+churn+dataset\">Iranian Churn Dataset<\/a>. Dataset license: CC-BY-4.0. The downloadable ZIP includes the adapted data and attribution notices.<\/p><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Dataset measure<\/th><th scope=\"col\">Saved value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Analysis unit<\/th><td>customer record<\/td><\/tr><tr><th scope=\"row\">Records<\/th><td>3,150<\/td><\/tr><tr><th scope=\"row\">Raw variables<\/th><td>14<\/td><\/tr><tr><th scope=\"row\">Encoded model inputs<\/th><td>13<\/td><\/tr><tr><th scope=\"row\">Model outputs<\/th><td>1<\/td><\/tr><tr><th scope=\"row\">Training roles<\/th><td>1890<\/td><\/tr><tr><th scope=\"row\">Validation \/ selection roles<\/th><td>630<\/td><\/tr><tr><th scope=\"row\">Testing roles<\/th><td>630<\/td><\/tr><tr><th scope=\"row\">Unused roles<\/th><td>0<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Field<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Type<\/th><th scope=\"col\">Categories<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Call  Failure<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">Complains<\/th><td>Input<\/td><td>Binary<\/td><td>0, 1<\/td><\/tr><tr><th scope=\"row\">Subscription  Length<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">Charge  Amount<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">Seconds of Use<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">Frequency of use<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">Frequency of SMS<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">Distinct Called Numbers<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">Age Group<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">Tariff Plan<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">Status<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">Age<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">Customer Value<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">Churn<\/th><td>Target<\/td><td>Binary<\/td><td>0, 1<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-telecommunicationschurn-t0-s2-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-telecommunicationschurn-t0-s2-20261006\" role=\"img\" aria-label=\"Target class distribution pie chart\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-telecommunicationschurn-t0-s2-20261006.png\" alt=\"Target class distribution pie chart\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"direction\":\"clockwise\",\"hovertemplate\":\"%{label}\\u003cbr\\u003e%{value}\\u003cbr\\u003e%{percent}\\u003cextra\\u003e\\u003c\/extra\\u003e\",\"labels\":[\"0\",\"1\"],\"marker\":{\"colors\":[\"#209fdf\",\"#092d40\"]},\"showlegend\":true,\"sort\":false,\"textinfo\":\"label+percent\",\"type\":\"pie\",\"values\":[84.29000091552734,15.710000038146973]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{},\"margin\":{\"l\":95,\"r\":45,\"t\":65,\"b\":95},\"paper_bgcolor\":\"#fff\",\"plot_bgcolor\":\"#fff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Target class distribution pie chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\",\"font\":{\"size\":17}},\"height\":500}}<\/script><\/div><figcaption>Target class distribution pie chart. Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><div class=\"ndb-note\">The saved project assigns rows to training, validation and testing as shown above. This is internal record-level evaluation; it does not demonstrate separation by subject, device, site or acquisition batch.<\/div><\/section>\n<section id=\"3-model\" class=\"ndb-card\"><h2>3. Model<\/h2><p>The model has 13 encoded inputs and 1 outputs. No architecture-selection experiment is recorded in this project. The diagram shows the topology used by the saved model.<\/p><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Layer<\/th><th scope=\"col\">Input shape<\/th><th scope=\"col\">Output shape<\/th><th scope=\"col\">Activation<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Scaling<\/th><td>13<\/td><td>13<\/td><td><\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>13<\/td><td>3<\/td><td>Tanh<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>3<\/td><td>1<\/td><td>Sigmoid<\/td><\/tr><\/tbody><\/table><\/div><p>Output values are uncalibrated model scores. For binary evaluation, the saved positive class is <strong>1<\/strong>.<\/p><figure class=\"ndb-architecture-figure\"><a class=\"ndb-architecture-link\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-telecommunicationschurn-t3-s1-20261006.png\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"ndb-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-telecommunicationschurn-t3-s1-20261006.png\" alt=\"Telecommunications customer churn classification \u2014 initial network architecture\"><\/a><figcaption>Topology of the saved current model; no architecture selection is recorded.<\/figcaption><\/figure><\/section>\n<section id=\"4-training\" class=\"ndb-card\"><h2>4. Training strategy<\/h2><p>The saved training configuration uses QuasiNewton with WeightedSquaredError.<\/p><h3>Quasi-Newton method results<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Epochs number<\/th><td>103<\/td><\/tr><tr><th scope=\"row\">Elapsed time<\/th><td>00:00:00<\/td><\/tr><tr><th scope=\"row\">Stopping criterion<\/th><td>Minimum loss decrease<\/td><\/tr><tr><th scope=\"row\">Training error<\/th><td>0.225<\/td><\/tr><tr><th scope=\"row\">Validation error<\/th><td>0.213<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-telecommunicationschurn-t4-s2-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-telecommunicationschurn-t4-s2-20261006\" role=\"img\" aria-label=\"Quasi-Newton method error history\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-telecommunicationschurn-t4-s2-20261006.png\" alt=\"Quasi-Newton method error history\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"connectgaps\":false,\"line\":{\"color\":\"#529cc2\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Training error\",\"opacity\":1,\"showlegend\":true,\"type\":\"scatter\",\"x\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102],\"y\":[0.6930000185966492,0.6840000152587891,0.6449999809265137,0.5600000023841858,0.4580000042915344,0.382999986410141,0.36899998784065247,0.367000013589859,0.35499998927116394,0.35100001096725464,0.34599998593330383,0.33899998664855957,0.3310000002384186,0.3179999887943268,0.30000001192092896,0.28200000524520874,0.2800000011920929,0.27799999713897705,0.27799999713897705,0.2759999930858612,0.2750000059604645,0.27399998903274536,0.2720000147819519,0.2680000066757202,0.2639999985694885,0.2590000033378601,0.25699999928474426,0.2529999911785126,0.25099998712539673,0.24899999797344208,0.24699999392032623,0.2460000067949295,0.2409999966621399,0.23999999463558197,0.23800000548362732,0.23600000143051147,0.23499999940395355,0.23399999737739563,0.2329999953508377,0.23199999332427979,0.23100000619888306,0.23000000417232513,0.2290000021457672,0.2280000001192093,0.22699999809265137,0.22599999606609344,0.22499999403953552,0.22300000488758087,0.22100000083446503,0.21899999678134918,0.2160000056028366,0.21299999952316284,0.21199999749660492,0.210999995470047,0.210999995470047,0.210999995470047,0.20999999344348907,0.20900000631809235,0.20900000631809235,0.20900000631809235,0.20800000429153442,0.20800000429153442,0.20800000429153442,0.20800000429153442,0.20800000429153442,0.2070000022649765,0.2070000022649765,0.2070000022649765,0.2070000022649765,0.2070000022649765,0.2070000022649765,0.2070000022649765,0.2070000022649765,0.2070000022649765,0.2070000022649765,0.2070000022649765,0.2070000022649765,0.20600000023841858,0.20600000023841858,0.20600000023841858,0.20600000023841858,0.20600000023841858,0.20600000023841858,0.20600000023841858,0.20499999821186066,0.20499999821186066,0.20499999821186066,0.20399999618530273,0.20399999618530273,0.20399999618530273,0.20399999618530273,0.20399999618530273,0.20399999618530273,0.20399999618530273,0.2029999941587448,0.2029999941587448,0.2029999941587448,0.2029999941587448,0.2029999941587448,0.2029999941587448,0.2029999941587448,0.2029999941587448,0.2029999941587448]},{\"connectgaps\":false,\"line\":{\"color\":\"#ed982a\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Validation error\",\"opacity\":1,\"showlegend\":true,\"type\":\"scatter\",\"x\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102],\"y\":[0.6539999842643738,0.6169999837875366,0.5389999747276306,0.4449999928474426,0.414000004529953,0.4020000100135803,0.3610000014305115,0.3449999988079071,0.3409999907016754,0.335999995470047,0.33000001311302185,0.3230000138282776,0.3140000104904175,0.3009999990463257,0.28700000047683716,0.28700000047683716,0.2849999964237213,0.28700000047683716,0.2840000092983246,0.28200000524520874,0.27900001406669617,0.2759999930858612,0.2720000147819519,0.26899999380111694,0.26100000739097595,0.2549999952316284,0.25600001215934753,0.24699999392032623,0.24199999868869781,0.2370000034570694,0.23399999737739563,0.22300000488758087,0.22200000286102295,0.2199999988079071,0.22100000083446503,0.21899999678134918,0.2199999988079071,0.21899999678134918,0.2199999988079071,0.2199999988079071,0.21899999678134918,0.21799999475479126,0.21799999475479126,0.21699999272823334,0.2160000056028366,0.2150000035762787,0.21299999952316284,0.21400000154972076,0.21400000154972076,0.2150000035762787,0.2160000056028366,0.21899999678134918,0.21899999678134918,0.21899999678134918,0.21899999678134918,0.21799999475479126,0.2160000056028366,0.2160000056028366,0.2160000056028366,0.2150000035762787,0.2150000035762787,0.21400000154972076,0.21400000154972076,0.21400000154972076,0.2150000035762787,0.2160000056028366,0.2160000056028366,0.2160000056028366,0.2160000056028366,0.21699999272823334,0.21799999475479126,0.2199999988079071,0.21899999678134918,0.21899999678134918,0.21799999475479126,0.21799999475479126,0.21799999475479126,0.21799999475479126,0.2199999988079071,0.22100000083446503,0.2240000069141388,0.22499999403953552,0.22599999606609344,0.2280000001192093,0.2290000021457672,0.2290000021457672,0.2290000021457672,0.23000000417232513,0.23000000417232513,0.23000000417232513,0.23000000417232513,0.2290000021457672,0.2290000021457672,0.2280000001192093,0.22699999809265137,0.22699999809265137,0.22699999809265137,0.22699999809265137,0.22699999809265137,0.2240000069141388,0.2240000069141388,0.2240000069141388,0.2240000069141388]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{\"orientation\":\"h\",\"x\":0.5,\"xanchor\":\"center\",\"y\":-0.24,\"yanchor\":\"top\"},\"margin\":{\"l\":95,\"r\":45,\"t\":65,\"b\":95},\"paper_bgcolor\":\"#fff\",\"plot_bgcolor\":\"#fff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Training history\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\",\"font\":{\"size\":17}},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,102],\"title\":{\"text\":\"Epoch\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,0.7001000000000001],\"title\":{\"text\":\"Weighted squared error\"},\"zerolinecolor\":\"#aeb6c2\"},\"height\":500}}<\/script><\/div><figcaption>Quasi-Newton method error history. Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><\/section>\n<section id=\"5-selection\" class=\"ndb-card\"><h2>5. Model selection<\/h2><p>No model selection experiment is recorded for this version. The validation subset guides fitting where a training report is present; it is distinct from the held-out test rows.<\/p><p>On this test subset a majority-class baseline would correctly classify 85.24% of the records. This baseline does not detect both classes and is not an optimized model.<\/p><\/section>\n<section id=\"6-testing\" class=\"ndb-card\"><h2>6. Testing analysis<\/h2><p>The figures and tables below refer to the current project\u2019s saved testing analysis. The subset uses testing role 2; it contains 630 source records.<\/p><p>ROC AUC describes ranking on this testing subset. Any optimal threshold shown in the saved ROC report was selected descriptively on that same subset; it is not an independently validated operating policy.<\/p><p>These operating-point metrics are calculated from the saved confusion counts at threshold <strong>0.5<\/strong>. The positive-label coding is stated in the model section.<\/p><h3>Confusion table<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Predicted positive<\/th><th scope=\"col\">Predicted negative<\/th><th scope=\"col\">Total<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Actual positive<\/th><td>85 (13.5%)<\/td><td>8 (1.3%)<\/td><td>93 (14.8%)<\/td><\/tr><tr><th scope=\"row\">Actual negative<\/th><td>52 (8.3%)<\/td><td>485 (77.0%)<\/td><td>537 (85.2%)<\/td><\/tr><tr><th scope=\"row\">Total<\/th><td>137 (21.7%)<\/td><td>493 (78.3%)<\/td><td>630 (100.0%)<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Test measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Testing records<\/th><td>630<\/td><\/tr><tr><th scope=\"row\">Positive cases<\/th><td>93<\/td><\/tr><tr><th scope=\"row\">Positive prevalence<\/th><td>14.76%<\/td><\/tr><tr><th scope=\"row\">Accuracy<\/th><td>90.48%<\/td><\/tr><tr><th scope=\"row\">Sensitivity \/ recall<\/th><td>91.40%<\/td><\/tr><tr><th scope=\"row\">Specificity<\/th><td>90.32%<\/td><\/tr><tr><th scope=\"row\">Precision \/ PPV<\/th><td>62.04%<\/td><\/tr><tr><th scope=\"row\">F1 score<\/th><td>0.739<\/td><\/tr><\/tbody><\/table><\/div><h3>Area under curve<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Area under curve<\/th><td>0.922<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-telecommunicationschurn-t6-s1-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-telecommunicationschurn-t6-s1-20261006\" role=\"img\" aria-label=\"ROC chart\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-telecommunicationschurn-t6-s1-20261006.png\" alt=\"ROC chart\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"connectgaps\":false,\"fill\":\"tozeroy\",\"fillcolor\":\"rgba(82,156,194,0.300)\",\"line\":{\"color\":\"#ffffff\",\"dash\":\"solid\",\"width\":1},\"marker\":{\"color\":\"#529cc2\",\"size\":5,\"symbol\":\"circle\"},\"mode\":\"lines+markers\",\"name\":\"Random classifier\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[1,0.9981380105018616,0.9962760210037231,0.9944130182266235,0.9925510287284851,0.9906889796257019,0.9888269901275635,0.986965000629425,0.9851019978523254,0.983240008354187,0.9813780188560486,0.9795160293579102,0.977653980255127,0.9757909774780273,0.9739289879798889,0.9720669984817505,0.9702050089836121,0.9683430194854736,0.966480016708374,0.9646180272102356,0.9627559781074524,0.960893988609314,0.9590319991111755,0.9571689963340759,0.9553070068359375,0.9534450173377991,0.9515830278396606,0.9497209787368774,0.9478579759597778,0.9459959864616394,0.944133996963501,0.9422720074653625,0.9404100179672241,0.9385470151901245,0.9366850256919861,0.9348229765892029,0.9329609870910645,0.931098997592926,0.9292359948158264,0.927374005317688,0.9236500263214111,0.9217879772186279,0.9199259877204895,0.9180629849433899,0.9162009954452515,0.9162009954452515,0.914339005947113,0.9124770164489746,0.9106150269508362,0.9087520241737366,0.9068899750709534,0.9050279855728149,0.9031659960746765,0.9013040065765381,0.8994410037994385,0.8975790143013,0.8957170248031616,0.8938549757003784,0.89199298620224,0.8901299834251404,0.8901299834251404,0.888267993927002,0.8864060044288635,0.8845440149307251,0.8826820254325867,0.8808190226554871,0.8789569735527039,0.8770949840545654,0.875232994556427,0.8733710050582886,0.871508002281189,0.8696460127830505,0.8677840232849121,0.8640599846839905,0.8621969819068909,0.8603349924087524,0.858473002910614,0.8566110134124756,0.8528860211372375,0.8491619825363159,0.8472999930381775,0.8454380035400391,0.8435750007629395,0.841713011264801,0.8398510217666626,0.8379889726638794,0.8342639803886414,0.8324019908905029,0.8305400013923645,0.8286780118942261,0.8268160223960876,0.824953019618988,0.8230909705162048,0.8212289810180664,0.819366991519928,0.8175050020217896,0.8156419992446899,0.8137800097465515,0.8119180202484131,0.8100559711456299,0.8081939816474915,0.8063309788703918,0.8044689893722534,0.802606999874115,0.8007450103759766,0.7988830208778381,0.7970200181007385,0.7951580286026001,0.7932959794998169,0.7914339900016785,0.78957200050354,0.7877089977264404,0.785847008228302,0.7839850187301636,0.7821230292320251,0.7802609801292419,0.7783989906311035,0.7765359878540039,0.7746739983558655,0.772812008857727,0.7709500193595886,0.7690879702568054,0.7672250270843506,0.7653629779815674,0.763500988483429,0.7616389989852905,0.7597770094871521,0.7579140067100525,0.7579140067100525,0.7560520172119141,0.7541900277137756,0.7523279786109924,0.750465989112854,0.7486029863357544,0.746740996837616,0.7448790073394775,0.7430170178413391,0.7411550283432007,0.7392920255661011,0.7374299764633179,0.7355679869651794,0.733705997467041,0.733705997467041,0.7318440079689026,0.729981005191803,0.7281190156936646,0.7262570261955261,0.7243949770927429,0.7225329875946045,0.7206699848175049,0.7188079953193665,0.716946005821228,0.7150840163230896,0.7132220268249512,0.7113590240478516,0.7094969749450684,0.7076349854469299,0.7057729959487915,0.7039110064506531,0.7020480036735535,0.700186014175415,0.6983240246772766,0.6964619755744934,0.694599986076355,0.6927369832992554,0.6908749938011169,0.6890130043029785,0.6871510148048401,0.6852890253067017,0.683426022529602,0.6815639734268188,0.6797019839286804,0.677839994430542,0.6759780049324036,0.674115002155304,0.6722530126571655,0.6703910231590271,0.6685289740562439,0.6666669845581055,0.6648039817810059,0.6629419922828674,0.661080002784729,0.6592180132865906,0.6573560237884521,0.6554930210113525,0.6536309719085693,0.6517689824104309,0.6499069929122925,0.6461820006370544,0.644320011138916,0.6424580216407776,0.6405959725379944,0.638733983039856,0.6368719935417175,0.6350089907646179,0.6331470012664795,0.6312850117683411,0.6294230222702026,0.627560019493103,0.6256979703903198,0.6238359808921814,0.621973991394043,0.6201120018959045,0.6182500123977661,0.6163870096206665,0.6145250201225281,0.6126629710197449,0.6108009815216064,0.6108009815216064,0.608938992023468,0.6070759892463684,0.60521399974823,0.6033520102500916,0.6014900207519531,0.5996279716491699,0.5977650284767151,0.5959029793739319,0.5940409898757935,0.592179000377655,0.5903170108795166,0.588454008102417,0.5865920186042786,0.5847300291061401,0.5828679800033569,0.5810059905052185,0.5791429877281189,0.5772809982299805,0.575419008731842,0.5735570192337036,0.5716950297355652,0.5698320269584656,0.5679699778556824,0.566107988357544,0.5642459988594055,0.5623840093612671,0.5605210065841675,0.558659017086029,0.5567970275878906,0.5549349784851074,0.553072988986969,0.5512099862098694,0.549347996711731,0.5474860072135925,0.5456240177154541,0.5437620282173157,0.5418990254402161,0.5400369763374329,0.5381749868392944,0.536312997341156,0.5344510078430176,0.532588005065918,0.5307260155677795,0.5288640260696411,0.5270019769668579,0.5251399874687195,0.5232769846916199,0.5214149951934814,0.519553005695343,0.5176910161972046,0.5158290266990662,0.513966977596283,0.5121039748191833,0.5102419853210449,0.5083799958229065,0.5065180063247681,0.5046550035476685,0.50279301404953,0.5009310245513916,0.4990690052509308,0.49720698595046997,0.49534499645233154,0.49348199367523193,0.4916200041770935,0.4897580146789551,0.48789599537849426,0.48603400588035583,0.4841710031032562,0.4823090136051178,0.480446994304657,0.47858500480651855,0.47672298550605774,0.4748600125312805,0.4729979932308197,0.4711360037326813,0.46741199493408203,0.4655489921569824,0.463687002658844,0.46182501316070557,0.45996299386024475,0.4581010043621063,0.4562380015850067,0.4543760120868683,0.45251399278640747,0.45065200328826904,0.4487900137901306,0.446927011013031,0.4450649917125702,0.44320300221443176,0.44134101271629333,0.4394789934158325,0.4376159906387329,0.4357540011405945,0.43389201164245605,0.43202999234199524,0.4301680028438568,0.4283050000667572,0.4264430105686188,0.42271900177001953,0.4208570122718811,0.4189940094947815,0.4171319901943207,0.41527000069618225,0.4134080111980438,0.411545991897583,0.4096829891204834,0.40782099962234497,0.40595901012420654,0.4040969908237457,0.4022350013256073,0.4003719985485077,0.39851000905036926,0.39664798974990845,0.39478600025177,0.3929240107536316,0.391061007976532,0.38919898867607117,0.38733699917793274,0.3854750096797943,0.3836129903793335,0.3817499876022339,0.37988799810409546,0.37802600860595703,0.3761639893054962,0.3743019998073578,0.3724389970302582,0.37057700753211975,0.36871498823165894,0.3668529987335205,0.3649910092353821,0.36312800645828247,0.36126598715782166,0.3594039976596832,0.3575420081615448,0.355679988861084,0.3538169860839844,0.35195499658584595,0.3500930070877075,0.3482309877872467,0.3463689982891083,0.34450700879096985,0.34264400601387024,0.3407819867134094,0.338919997215271,0.33705800771713257,0.33333298563957214,0.3314709961414337,0.3296090066432953,0.3277469873428345,0.32588499784469604,0.32402199506759644,0.322160005569458,0.3202979862689972,0.31843599677085876,0.31657400727272034,0.3147110044956207,0.3128489851951599,0.3109869956970215,0.30912500619888306,0.30726298689842224,0.305400013923645,0.3035379946231842,0.3016760051250458,0.29981398582458496,0.29795199632644653,0.2960889935493469,0.2942270040512085,0.29236501455307007,0.29050299525260925,0.2886410057544708,0.2867780029773712,0.2849160134792328,0.283053994178772,0.28119200468063354,0.27932998538017273,0.2774670124053955,0.2756049931049347,0.27374300360679626,0.27188101410865784,0.270018994808197,0.2681559920310974,0.266294002532959,0.26443201303482056,0.26256999373435974,0.2607080042362213,0.2588450014591217,0.2569830119609833,0.25512099266052246,0.25325900316238403,0.2513970136642456,0.2495339959859848,0.24767200648784637,0.24581000208854675,0.24394799768924713,0.2420859932899475,0.2402230054140091,0.23836100101470947,0.23649899661540985,0.23463700711727142,0.2327750027179718,0.2309119999408722,0.22904999554157257,0.22718800604343414,0.22532600164413452,0.2234639972448349,0.2216009944677353,0.21973900496959686,0.21787700057029724,0.21601499617099762,0.2141530066728592,0.21229000389575958,0.21042799949645996,0.20856599509716034,0.2067040055990219,0.2048420011997223,0.20297999680042267,0.20111699402332306,0.19925500452518463,0.197393000125885,0.1955309957265854,0.19366900622844696,0.18994399905204773,0.1880819946527481,0.18622000515460968,0.18435800075531006,0.18249499797821045,0.18063299357891083,0.1787710040807724,0.17690899968147278,0.17504699528217316,0.17318400740623474,0.17132200300693512,0.1694599986076355,0.16759799420833588,0.16573600471019745,0.16387300193309784,0.16201099753379822,0.1601489931344986,0.15828700363636017,0.15828700363636017,0.1527000069618225,0.14897599816322327,0.14711399376392365,0.14525100588798523,0.1433890014886856,0.141526997089386,0.13966499269008636,0.13780300319194794,0.13594000041484833,0.1340779960155487,0.13221600651741028,0.13035400211811066,0.12849199771881104,0.12662899494171143,0.1247669979929924,0.12290500104427338,0.12104299664497375,0.11918099969625473,0.11731799691915512,0.1154559999704361,0.11359400302171707,0.11173199862241745,0.10800699889659882,0.10614500194787979,0.10428299754858017,0.10428299754858017,0.10242100059986115,0.10055900365114212,0.0986965000629425,0.09683430194854736,0.09497209638357162,0.09310989826917648,0.09124770015478134,0.09124770015478134,0.0893855020403862,0.08752329647541046,0.08566109836101532,0.08566109836101532,0.08566109836101532,0.08566109836101532,0.08379890024662018,0.08193670213222504,0.08193670213222504,0.08193670213222504,0.08193670213222504,0.0800744965672493,0.07821229845285416,0.07821229845285416,0.07635010033845901,0.07635010033845901,0.07448790222406387,0.07262569665908813,0.07262569665908813,0.07262569665908813,0.07262569665908813,0.07262569665908813,0.07262569665908813,0.070763498544693,0.070763498544693,0.070763498544693,0.070763498544693,0.06890130043029785,0.06890130043029785,0.06890130043029785,0.06703910231590271,0.06703910231590271,0.06517689675092697,0.06517689675092697,0.06517689675092697,0.06331469863653183,0.06331469863653183,0.06331469863653183,0.06145250052213669,0.06145250052213669,0.05959029868245125,0.05959029868245125,0.05959029868245125,0.05772810056805611,0.055865898728370667,0.054003700613975525,0.052141498774290085,0.052141498774290085,0.052141498774290085,0.05027930065989494,0.05027930065989494,0.0484170988202095,0.0484170988202095,0.04655490070581436,0.04469269886612892,0.04469269886612892,0.04469269886612892,0.04469269886612892,0.04469269886612892,0.04283050075173378,0.04283050075173378,0.04096829891204834,0.0391061007976532,0.0391061007976532,0.03724389895796776,0.03538170084357262,0.033519599586725235,0.033519599586725235,0.033519599586725235,0.033519599586725235,0.03165740147233009,0.029795199632644653,0.029795199632644653,0.029795199632644653,0.027932999655604362,0.02607079967856407,0.02420859970152378,0.02420859970152378,0.02234639972448349,0.0204841997474432,0.01862199977040291,0.01862199977040291,0.016759799793362617,0.014897599816322327,0.013035399839282036,0.013035399839282036,0.011173199862241745,0.011173199862241745,0.009310989640653133,0.00744879012927413,0.00744879012927413,0.00744879012927413,0.00744879012927413,0.00744879012927413,0.00744879012927413,0.005586590152233839,0.005586590152233839,0.0037243899423629045,0.0037243899423629045,0.0037243899423629045,0.0037243899423629045,0.0018621999770402908,0.0018621999770402908,0.0018621999770402908,0.0018621999770402908,0.0018621999770402908,0.0018621999770402908,0.0018621999770402908,0.0018621999770402908,0.0018621999770402908,0.0018621999770402908,0.0018621999770402908,0.0018621999770402908,0.0018621999770402908,0.0018621999770402908,0.0018621999770402908,0.0018621999770402908,0.0018621999770402908,0.0018621999770402908,0.0018621999770402908,0.0018621999770402908,0],\"y\":[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.9892470240592957,0.9892470240592957,0.9892470240592957,0.9892470240592957,0.9892470240592957,0.9892470240592957,0.9892470240592957,0.9892470240592957,0.9892470240592957,0.9892470240592957,0.9892470240592957,0.9892470240592957,0.9892470240592957,0.9892470240592957,0.9892470240592957,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9784950017929077,0.9677420258522034,0.9677420258522034,0.9677420258522034,0.9677420258522034,0.9677420258522034,0.9677420258522034,0.9677420258522034,0.9677420258522034,0.9677420258522034,0.9677420258522034,0.9677420258522034,0.9677420258522034,0.9677420258522034,0.9677420258522034,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9569889903068542,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.9462370276451111,0.935483992099762,0.935483992099762,0.935483992099762,0.935483992099762,0.935483992099762,0.935483992099762,0.935483992099762,0.935483992099762,0.935483992099762,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9247310161590576,0.9139789938926697,0.9139789938926697,0.9139789938926697,0.9139789938926697,0.9139789938926697,0.9139789938926697,0.9139789938926697,0.9139789938926697,0.9032260179519653,0.9032260179519653,0.9032260179519653,0.9032260179519653,0.8924729824066162,0.8817200064659119,0.8709679841995239,0.8709679841995239,0.8709679841995239,0.8602150082588196,0.8494619727134705,0.8387100100517273,0.8387100100517273,0.8387100100517273,0.8279569745063782,0.8279569745063782,0.8172039985656738,0.8172039985656738,0.8172039985656738,0.8064519762992859,0.7956990003585815,0.7849460244178772,0.7741940021514893,0.7634410262107849,0.7634410262107849,0.7526879906654358,0.7419350147247314,0.7311829924583435,0.7311829924583435,0.7204300165176392,0.70967698097229,0.70967698097229,0.6989250183105469,0.6989250183105469,0.6881719827651978,0.6774190068244934,0.6774190068244934,0.6666669845581055,0.6559140086174011,0.6559140086174011,0.645160973072052,0.645160973072052,0.6344090104103088,0.6236559748649597,0.6236559748649597,0.6021509766578674,0.6021509766578674,0.6021509766578674,0.5913980007171631,0.5806450247764587,0.5806450247764587,0.5698919892311096,0.5698919892311096,0.5591400265693665,0.5591400265693665,0.5483869910240173,0.537634015083313,0.526881992816925,0.5161290168762207,0.5053759813308716,0.5053759813308716,0.49462398886680603,0.49462398886680603,0.49462398886680603,0.4838710129261017,0.4838710129261017,0.4838710129261017,0.4838710129261017,0.47311800718307495,0.4623660147190094,0.45161300897598267,0.45161300897598267,0.45161300897598267,0.44086000323295593,0.4301080107688904,0.4301080107688904,0.4301080107688904,0.4301080107688904,0.41935500502586365,0.41935500502586365,0.41935500502586365,0.41935500502586365,0.4086019992828369,0.4086019992828369,0.4086019992828369,0.4086019992828369,0.3978489935398102,0.3978489935398102,0.38709700107574463,0.38709700107574463,0.38709700107574463,0.3763439953327179,0.36559098958969116,0.3548389971256256,0.3440859913825989,0.33333298563957214,0.3225809931755066,0.31182798743247986,0.31182798743247986,0.3010750114917755,0.2903229892253876,0.2795700132846832,0.2795700132846832,0.2688170075416565,0.25806498527526855,0.24731199443340302,0.23655900359153748,0.22580599784851074,0.2150540053844452,0.20430099964141846,0.19354799389839172,0.18279600143432617,0.17204299569129944,0.1612900048494339,0.15053799748420715,0.1397850066423416,0.12903200089931488,0.11828000098466873,0.09677419811487198,0.08602149784564972,0.0645160973072052,0.01075270026922226,0]},{\"connectgaps\":false,\"line\":{\"color\":\"#808080\",\"dash\":\"solid\",\"width\":1},\"mode\":\"lines\",\"name\":\"True Positive Rate (sensitivity)\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[0,1],\"y\":[0,1]},{\"connectgaps\":false,\"marker\":{\"color\":\"#ed982a\",\"size\":8,\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"True Positive Rate (sensitivity)\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[0.09124770015478134],\"y\":[0.9139789938926697]}],\"layout\":{\"annotations\":[{\"font\":{\"color\":\"#404044\"},\"showarrow\":false,\"text\":\"Optimal Threshold\",\"x\":0.2612477001547814,\"y\":0.8239789938926697},{\"font\":{\"color\":\"#404044\"},\"showarrow\":false,\"text\":\"Area under curve: 0.922\",\"x\":0.7,\"y\":0.02}],\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"l\":95,\"r\":45,\"t\":65,\"b\":95},\"paper_bgcolor\":\"#fff\",\"plot_bgcolor\":\"#fff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"ROC chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\",\"font\":{\"size\":17}},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1],\"title\":{\"text\":\"False positive rate\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1],\"title\":{\"text\":\"True positive rate\"},\"zerolinecolor\":\"#aeb6c2\"},\"height\":500}}<\/script><\/div><figcaption>ROC chart. Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><\/section>\n<section id=\"7-model-deployment\" class=\"ndb-card\"><h2>7. Model deployment<\/h2><p>Open the downloaded project in Neural Designer, inspect the dataset roles and preprocessing, then review the saved task report. Use the same input schema and category order when calculating outputs. The ZIP contains the exact current .nd, its source data and the applicable dataset notices.<\/p><p>Workflow: source measurements \u2192 schema and availability checks \u2192 model output \u2192 domain review. Keep model versions, validation evidence and incoming-data monitoring together.<\/p><div class=\"ndb-downloads\"><a class=\"aui aui-button\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-telecommunicationschurn-licensed-20261006.zip\">Download model, data and licenses (ZIP)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">Download Neural Designer<\/a><\/div><\/section>\n<section id=\"8-limitations\" class=\"ndb-card\"><h2>8. Evidence and limitations<\/h2><p>Check when complaints, account status and customer value become available relative to the churn label before prospective use. Random row testing does not establish transfer to later periods or other operators. Review calibration, subgroup performance and intervention costs separately.<\/p><\/section>\n<section id=\"references\" class=\"ndb-card\"><h2>References<\/h2><ul><li><a href=\"https:\/\/archive.ics.uci.edu\/dataset\/563\/iranian+churn+dataset\">Iranian Churn Dataset<\/a>. Ali Dehghan. 10.24432\/C5JW3Z<\/li><li>Dataset terms: Creative Commons Attribution 4.0 International. Full attribution and transformations are included in <code>LICENSES\/DATASET-LICENSE.txt<\/code>.<\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-telecommunicationschurn-licensed-20261006.zip\">Current Neural Designer project and saved task report<\/a>, snapshot 6 October 2026.<\/li><\/ul><\/section>\n<\/div>\n<\/div>\n<style>\n.nd-chart-bundle{width:100%;max-width:760px;min-width:0;margin:24px auto;box-sizing:border-box;position:relative;overflow-x:auto;text-align:center;background:#fff;color:#30343b}\n.nd-chart-bundle .nd-chart-host{width:100%;height:440px;min-width:0;box-sizing:border-box}\n.nd-chart-bundle:not(.is-ready) .nd-chart-host{position:absolute;visibility:hidden}\n.nd-chart-bundle.is-ready .nd-chart-fallback{display:none!important}\n.nd-chart-bundle .nd-chart-fallback{display:block;margin:0 auto!important;width:100%;height:auto;max-width:100%}\n.nd-chart-bundle>script,.nd-chart-bundle>br,.nd-chart-bundle>p:empty{display:none!important}\n@media(max-width:600px){.nd-chart-bundle{margin:20px auto}.nd-chart-bundle .nd-chart-host{height:440px}}\n.nd-chart-bundle .nd-chart-host{min-width:0!important}.nd-chart-bundle{max-width:100%}html body .nd-comparison-grid>.nd-comparison-item:only-child{flex-basis:min(760px,100%)!important;width:min(760px,100%)!important}.ndb-card details,.nds-card details{margin:20px 0}.ndb-card summary,.nds-card summary{cursor:pointer;font-weight:600}<\/style><script data-noptimize=\"1\" data-cfasync=\"false\" src=\"https:\/\/cdn.plot.ly\/plotly-basic-4.0.0.min.js\"><\/script><script data-noptimize=\"1\">(()=>{const start=()=>{if(!window.Plotly)return;document.querySelectorAll('.nd-chart-bundle').forEach(bundle=>{if(bundle.dataset.started)return;bundle.dataset.started='1';const host=bundle.querySelector('.nd-chart-host'),source=bundle.querySelector('script[type=\"application\/json\"]');if(!host||!source)return;const figure=JSON.parse(source.textContent);window.Plotly.newPlot(host,figure.data,figure.layout,figure.config).then(async()=>{const title=host.querySelector('.gtitle');if(title)host.style.minWidth=Math.ceil(title.getComputedTextLength()+48)+'px';await window.Plotly.Plots.resize(host);bundle.classList.add('is-ready');if(window.ResizeObserver){let timer;new ResizeObserver(()=>{clearTimeout(timer);timer=setTimeout(()=>window.Plotly.Plots.resize(host),80);}).observe(bundle);}}).catch(error=>{bundle.dataset.error=String(error);console.error(error);});});};const ready=()=>{if(window.Plotly){start();return;}let count=0;const timer=setInterval(()=>{if(window.Plotly){clearInterval(timer);start();}else if(++count>200)clearInterval(timer);},50);};if(document.readyState==='loading')document.addEventListener('DOMContentLoaded',ready,{once:true});else ready();})();<\/script>","protected":false},"author":13,"featured_media":1457,"template":"","categories":[29],"tags":[48],"class_list":["post-3523","learning","type-learning","status-publish","has-post-thumbnail","hentry","category-examples","tag-telecomunnications"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Telecommunications customer churn classification<\/title>\n<meta name=\"description\" content=\"Use 3,150 customer records to study the binary churn label. The current saved network takes 13 inputs and is evaluated on 630 held-out records.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Churn prediction in communications using machine learning\" \/>\n<meta property=\"og:description\" content=\"In this example we detect those customers which are more likely to leave a telecommunications company using machine learning.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/\" \/>\n<meta property=\"og:site_name\" content=\"Neural Designer\" \/>\n<meta property=\"article:modified_time\" content=\"2026-10-06T11:35:23+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/telecommunication-churn.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"628\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:title\" content=\"Churn prediction in communications using machine learning\" \/>\n<meta name=\"twitter:description\" content=\"In this example we detect those customers which are more likely to leave a telecommunications company using machine learning.\" \/>\n<meta name=\"twitter:image\" content=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/telecommunication-churn.webp\" \/>\n<meta name=\"twitter:site\" content=\"@NeuralDesigner\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/\",\"url\":\"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/\",\"name\":\"Telecommunications customer churn classification\",\"isPartOf\":{\"@id\":\"https:\/\/www.neuraldesigner.com\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/telecommunication-churn.webp\",\"datePublished\":\"2023-08-31T11:12:58+00:00\",\"dateModified\":\"2026-10-06T11:35:23+00:00\",\"description\":\"Use 3,150 customer records to study the binary churn label. The current saved network takes 13 inputs and is evaluated on 630 held-out records.\",\"breadcrumb\":{\"@id\":\"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/#primaryimage\",\"url\":\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/telecommunication-churn.webp\",\"contentUrl\":\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/telecommunication-churn.webp\",\"width\":1200,\"height\":628,\"caption\":\"Telecommunications customer churn\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.neuraldesigner.com\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Learning\",\"item\":\"https:\/\/www.neuraldesigner.com\/learning\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Telecommunications customer churn classification\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.neuraldesigner.com\/#website\",\"url\":\"https:\/\/www.neuraldesigner.com\/\",\"name\":\"Neural Designer\",\"description\":\"No-code AI Platform\",\"publisher\":{\"@id\":\"https:\/\/www.neuraldesigner.com\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.neuraldesigner.com\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/www.neuraldesigner.com\/#organization\",\"name\":\"Neural Designer\",\"url\":\"https:\/\/www.neuraldesigner.com\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.neuraldesigner.com\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/05\/logo-neural-1.png\",\"contentUrl\":\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/05\/logo-neural-1.png\",\"width\":1024,\"height\":223,\"caption\":\"Neural Designer\"},\"image\":{\"@id\":\"https:\/\/www.neuraldesigner.com\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/x.com\/NeuralDesigner\",\"https:\/\/es.linkedin.com\/showcase\/neuraldesigner\/\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Telecommunications customer churn classification","description":"Use 3,150 customer records to study the binary churn label. The current saved network takes 13 inputs and is evaluated on 630 held-out records.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/","og_locale":"en_US","og_type":"article","og_title":"Churn prediction in communications using machine learning","og_description":"In this example we detect those customers which are more likely to leave a telecommunications company using machine learning.","og_url":"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/","og_site_name":"Neural Designer","article_modified_time":"2026-10-06T11:35:23+00:00","og_image":[{"width":1200,"height":628,"url":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/telecommunication-churn.webp","type":"image\/webp"}],"twitter_card":"summary_large_image","twitter_title":"Churn prediction in communications using machine learning","twitter_description":"In this example we detect those customers which are more likely to leave a telecommunications company using machine learning.","twitter_image":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/telecommunication-churn.webp","twitter_site":"@NeuralDesigner","twitter_misc":{"Est. reading time":"7 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/","url":"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/","name":"Telecommunications customer churn classification","isPartOf":{"@id":"https:\/\/www.neuraldesigner.com\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/#primaryimage"},"image":{"@id":"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/#primaryimage"},"thumbnailUrl":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/telecommunication-churn.webp","datePublished":"2023-08-31T11:12:58+00:00","dateModified":"2026-10-06T11:35:23+00:00","description":"Use 3,150 customer records to study the binary churn label. The current saved network takes 13 inputs and is evaluated on 630 held-out records.","breadcrumb":{"@id":"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/#primaryimage","url":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/telecommunication-churn.webp","contentUrl":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/telecommunication-churn.webp","width":1200,"height":628,"caption":"Telecommunications customer churn"},{"@type":"BreadcrumbList","@id":"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.neuraldesigner.com\/"},{"@type":"ListItem","position":2,"name":"Learning","item":"https:\/\/www.neuraldesigner.com\/learning\/"},{"@type":"ListItem","position":3,"name":"Telecommunications customer churn classification"}]},{"@type":"WebSite","@id":"https:\/\/www.neuraldesigner.com\/#website","url":"https:\/\/www.neuraldesigner.com\/","name":"Neural Designer","description":"No-code AI Platform","publisher":{"@id":"https:\/\/www.neuraldesigner.com\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.neuraldesigner.com\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.neuraldesigner.com\/#organization","name":"Neural Designer","url":"https:\/\/www.neuraldesigner.com\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.neuraldesigner.com\/#\/schema\/logo\/image\/","url":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/05\/logo-neural-1.png","contentUrl":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/05\/logo-neural-1.png","width":1024,"height":223,"caption":"Neural Designer"},"image":{"@id":"https:\/\/www.neuraldesigner.com\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/x.com\/NeuralDesigner","https:\/\/es.linkedin.com\/showcase\/neuraldesigner\/"]}]}},"_links":{"self":[{"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/learning\/3523","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/learning"}],"about":[{"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/types\/learning"}],"author":[{"embeddable":true,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/users\/13"}],"version-history":[{"count":10,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/learning\/3523\/revisions"}],"predecessor-version":[{"id":24320,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/learning\/3523\/revisions\/24320"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/media\/1457"}],"wp:attachment":[{"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/media?parent=3523"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/categories?post=3523"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/tags?post=3523"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}