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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>Evaluating response to a banking campaign<\/h2>\n<p>Campaign capacity is limited. This example uses customer and contact information to rank the likelihood of the recorded conversion outcome, then examines the trade-off between missed conversions and unnecessary contacts.<\/p>\n<div class=\"ndb-kpis\">\n<div class=\"ndb-kpi\"><strong>4,120<\/strong><span>Source records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>17<\/strong><span>Final input features<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>824<\/strong><span>Test observations<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>0.731<\/strong><span>Test ROC AUC<\/span><\/div>\n<\/div>\n<div class=\"ndb-actions\"><a class=\"aui aui-button\" href=\"#7-model-deployment\">Explore the model<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/bank-marketing-data-20260921.csv\">Download the data<\/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<span id=\"ApplicationType\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"1-industrial-challenge\" class=\"ndb-card\"><h2>1. Business decision<\/h2><p>A campaign response model can help organize customer analysis when its inputs are available at the time of contact. The saved schema omits call duration, which would only be known after a call.<\/p><div class=\"ndb-value-grid\"><div class=\"ndb-value\"><h3>Before contact<\/h3><p>Use customer and campaign information in the prepared schema.<\/p><\/div><div class=\"ndb-value\"><h3>Response ranking<\/h3><p>Examine how subscription outcomes vary with the model score.<\/p><\/div><div class=\"ndb-value\"><h3>Contact decisions<\/h3><p>Combine response evidence with separately measured costs and intervention effects.<\/p><\/div><\/div><div class=\"ndb-audience\"><span>Marketing analytics<\/span><span>Campaign planning<\/span><span>Customer operations<\/span><\/div><div class=\"ndb-note\">Past campaign outcomes depend on who was contacted and when. A new campaign needs temporal validation, verified contact-time feature availability, calibration and an experiment measuring incremental conversions. Codes such as job and education are numeric in this supplied project; their order is a modelling assumption.<\/div><\/section>\n<span id=\"DataSet\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"2-data-set\" class=\"ndb-card\"><h2>2. Data set<\/h2><p>This prepared version of the UCI Bank Marketing data contains 4,120 records. The local schema excludes call duration. Several fields contain missing values and use the application&#x27;s mean imputation; this preprocessing was not fitted exclusively on the training subset.<\/p><p>The downloadable project, saved report and supplied source CSV define the exact version used here. Repository: <a href=\"https:\/\/archive.ics.uci.edu\/dataset\/222\/bank+marketing\">original dataset\/source record<\/a>. <\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Subset<\/th><th scope=\"col\">Records<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Training<\/th><td>2472<\/td><\/tr><tr><th scope=\"row\">Validation \/ selection<\/th><td>824<\/td><\/tr><tr><th scope=\"row\">Testing<\/th><td>824<\/td><\/tr><tr><th scope=\"row\">Unused<\/th><td>0<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Variable<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Type<\/th><th scope=\"col\">Encoding<\/th><th scope=\"col\">Unit<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">age<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">job<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">marital_status<\/th><td>Input<\/td><td>Categorical<\/td><td>divorced; married; single<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">education<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">default<\/th><td>Input<\/td><td>Binary<\/td><td>0; 1<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">balance<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">housing<\/th><td>Input<\/td><td>Binary<\/td><td>0; 1<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">loan<\/th><td>Input<\/td><td>Binary<\/td><td>0; 1<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">contact_type<\/th><td>Input<\/td><td>Binary<\/td><td>0; 1<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">day<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">month<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">campaing_contacts<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">last_contact<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">previous_contacts<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">previous_conversion<\/th><td>Input<\/td><td>Binary<\/td><td>0; 1<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">conversion<\/th><td>Target<\/td><td>Binary<\/td><td>0; 1<\/td><td>As supplied<\/td><\/tr><\/tbody><\/table><\/div><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-bank-marketing-t0-s22-20260921\"><div class=\"nd-chart-host\" id=\"nd-bank-marketing-t0-s22-20260921\" role=\"img\" aria-label=\"conversion pie chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: conversion pie chart. Enable JavaScript to explore it.<\/p><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\":[88.5,11.5]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{},\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"conversion pie chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"}}}<\/script><\/div><figcaption>conversion pie chart. Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-bank-marketing-t1-s1-20260921\"><div class=\"nd-chart-host\" id=\"nd-bank-marketing-t1-s1-20260921\" role=\"img\" aria-label=\"conversion Pearson correlations chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: conversion Pearson correlations chart. Enable JavaScript to explore it.<\/p><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"marker\":{\"color\":\"#209fdf\"},\"name\":\"\",\"opacity\":1,\"orientation\":\"h\",\"showlegend\":false,\"type\":\"bar\",\"x\":[-0.13600000739097595,-0.09709999710321426,-0.06809999793767929,-0.06610000133514404,-0.030700000002980232,0.042500000447034836,0.05350000038743019,0.07519999891519547,0.08429999649524689,0.503000020980835],\"y\":[\"last_contact\",\"housing\",\"loan\",\"campaing_contacts\",\"job\",\"age\",\"education\",\"marital_status\",\"previous_contacts\",\"previous_conversion\"]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"conversion Pearson correlations chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[-1,1],\"title\":{\"text\":\"Correlation\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"autorange\":\"reversed\",\"categoryarray\":[\"last_contact\",\"housing\",\"loan\",\"campaing_contacts\",\"job\",\"age\",\"education\",\"marital_status\",\"previous_contacts\",\"previous_conversion\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>conversion Pearson correlations chart. Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><div class=\"ndb-note\">This is internal validation using the saved record-level split. Grouped or temporal independence has not been established. Mean across used rows, matching TabularDataset::scrub_missing_values; preprocessing is not isolated to training.<\/div><\/section>\n<span id=\"NeuralNetwork\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"3-model\" class=\"ndb-card\"><h2>3. Model<\/h2><p>The final model has <strong>17 encoded input features<\/strong> and <strong>1 outputs<\/strong>. The following dimensions describe the final saved network.<\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><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>17<\/td><td>17<\/td><td>\u2014<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>17<\/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 semantics: the sigmoid score increases toward <strong>1<\/strong>; 0 is the other class. Calibration has not been evaluated, so scores are not presented as calibrated probabilities.<\/p><figure class=\"ndb-architecture-figure\" data-model-stage=\"initial\"><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-bank-marketing-final-architecture-20260921.png\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"ndb-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-bank-marketing-final-architecture-20260921.png\" alt=\"Evaluating response to a banking campaign: initial Neural Designer architecture\"><\/a><figcaption>Architecture used for this model; no architecture-selection experiment is recorded. Diagram labels show original variables; categorical expansion and the numeric layer dimensions are documented in the model table.<\/figcaption><\/figure><\/section>\n<span id=\"TrainingStrategy\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"4-training\" class=\"ndb-card\"><h2>4. Training strategy<\/h2><p>The saved training configuration uses WeightedSquaredError with QuasiNewton. Training minimizes the recorded objective; the validation subset monitors generalization during fitting. The testing subset is used for the evaluation below.<\/p><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-bank-marketing-t2-s2-20260921\"><div class=\"nd-chart-host\" id=\"nd-bank-marketing-t2-s2-20260921\" role=\"img\" aria-label=\"Quasi-Newton method error history\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: Quasi-Newton method error history. Enable JavaScript to explore it.<\/p><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 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Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><h3>Quasi-Newton method results<\/h3><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Epochs number<\/th><td>184<\/td><\/tr><tr><th scope=\"row\">Elapsed time<\/th><td>00:00:01<\/td><\/tr><tr><th scope=\"row\">Stopping criterion<\/th><td>Minimum loss decrease<\/td><\/tr><tr><th scope=\"row\">Training error<\/th><td>0.522<\/td><\/tr><tr><th scope=\"row\">Validation error<\/th><td>0.536<\/td><\/tr><\/tbody><\/table><\/div><\/section>\n<span id=\"ModelSelection\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"5-selection\" class=\"ndb-card\"><h2>5. Model selection<\/h2><p>No model selection experiment is recorded in this supplied project. The displayed architecture is the trained model used for testing; earlier article claims about a different selected architecture do not apply to this version.<\/p><p>A transparent test-set comparator is the majority-class rule, with accuracy 88.3%. This is a baseline for interpretation, not an alternative model fitted on the test labels.<\/p><\/section>\n<span id=\"TestingAnalysis\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"6-testing\" class=\"ndb-card\"><h2>6. Testing analysis<\/h2><p>The final classifier is evaluated on <strong>824 testing records<\/strong>. The confusion counts below were reproduced from the saved model. Rows are actual classes and columns are predicted classes. The decision threshold is 0.5 on the score for 1.<\/p><p>Test class prevalence is shown by the support counts. Accuracy is 70.5% and macro F1 is 0.571. ROC AUC is 0.731. The native ROC optimal threshold is descriptive of this test set and is not an independently validated operating policy.<\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Actual \/ predicted<\/th><th scope=\"col\">0<\/th><th scope=\"col\">1<\/th><th scope=\"col\">Total<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">0<\/th><td>521<\/td><td>207<\/td><td>728<\/td><\/tr><tr><th scope=\"row\">1<\/th><td>36<\/td><td>60<\/td><td>96<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Class<\/th><th scope=\"col\">Test cases<\/th><th scope=\"col\">Sensitivity \/ recall<\/th><th scope=\"col\">Specificity<\/th><th scope=\"col\">Precision \/ PPV<\/th><th scope=\"col\">F1<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">0<\/th><td>728<\/td><td>71.6%<\/td><td>62.5%<\/td><td>93.5%<\/td><td>0.811<\/td><\/tr><tr><th scope=\"row\">1<\/th><td>96<\/td><td>62.5%<\/td><td>71.6%<\/td><td>22.5%<\/td><td>0.331<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-native-test-figures\"><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-bank-marketing-t3-s1-20260921\"><div class=\"nd-chart-host\" id=\"nd-bank-marketing-t3-s1-20260921\" role=\"img\" aria-label=\"ROC chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: ROC chart. 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chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1],\"title\":{\"text\":\"False Positive Rate (1-specificity)\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1],\"title\":{\"text\":\"True Positive Rate (sensitivity)\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>ROC chart. Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><\/div><\/section>\n<span id=\"ModelDeployment\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"7-model-deployment\" class=\"ndb-card\"><h2>7. Model deployment<\/h2><p>Validated inputs \u2192 saved preprocessing \u2192 neural network \u2192 score or estimate \u2192 domain review. The ZIP contains the original project, source CSV, schema, test metrics and standalone interactive chart exports. The project hash in the schema identifies this exact version.<\/p><h3>Directional response at a fixed reference point<\/h3><details><summary>Inspect the saved reference point<\/summary><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Variable<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">1<\/th><td>age<\/td><td>33<\/td><\/tr><tr><th scope=\"row\">2<\/th><td>job<\/td><td>2<\/td><\/tr><tr><th scope=\"row\">3<\/th><td>marital_status<\/td><td>married<\/td><\/tr><tr><th scope=\"row\">4<\/th><td>education<\/td><td>3<\/td><\/tr><tr><th scope=\"row\">5<\/th><td>default<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">6<\/th><td>balance<\/td><td>79<\/td><\/tr><tr><th scope=\"row\">7<\/th><td>housing<\/td><td>1<\/td><\/tr><tr><th scope=\"row\">8<\/th><td>loan<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">9<\/th><td>contact_type<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">10<\/th><td>day<\/td><td>22<\/td><\/tr><tr><th scope=\"row\">11<\/th><td>month<\/td><td>10<\/td><\/tr><tr><th scope=\"row\">12<\/th><td>campaing_contacts<\/td><td>2<\/td><\/tr><tr><th scope=\"row\">13<\/th><td>last_contact<\/td><td>335<\/td><\/tr><tr><th scope=\"row\">14<\/th><td>previous_contacts<\/td><td>2<\/td><\/tr><tr><th scope=\"row\">15<\/th><td>previous_conversion<\/td><td>0<\/td><\/tr><\/tbody><\/table><\/div><\/details><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-bank-marketing-t6-s2-20260921\"><div class=\"nd-chart-host\" id=\"nd-bank-marketing-t6-s2-20260921\" role=\"img\" aria-label=\"conversion - age directional output\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: conversion &#8211; age directional output. Enable JavaScript to explore it.<\/p><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\",\"shape\":\"spline\",\"width\":2},\"mode\":\"lines\",\"name\":\"conversion\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94,96,98,100],\"y\":[0.5270000100135803,0.5249999761581421,0.5230000019073486,0.5199999809265137,0.515999972820282,0.5130000114440918,0.5090000033378601,0.5049999952316284,0.5009999871253967,0.4970000088214874,0.49300000071525574,0.4880000054836273,0.48399999737739563,0.4790000021457672,0.4749999940395355,0.4699999988079071,0.4659999907016754,0.460999995470047,0.4560000002384186,0.45100000500679016,0.44699999690055847,0.44200000166893005,0.43700000643730164,0.43299999833106995,0.42800000309944153,0.4230000078678131,0.4180000126361847,0.414000004529953,0.4090000092983246,0.40400001406669617,0.39899998903274536,0.39500001072883606,0.38999998569488525,0.38600000739097595,0.38100001215934753,0.37599998712539673,0.3720000088214874,0.367000013589859,0.3630000054836273,0.3580000102519989,0.3540000021457672,0.3490000069141388,0.3449999988079071,0.3400000035762787,0.335999995470047,0.3319999873638153,0.328000009059906,0.3230000138282776,0.3190000057220459,0.3149999976158142,0.3109999895095825]},{\"connectgaps\":false,\"marker\":{\"color\":\"#3c3c3c\",\"size\":8,\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"conversion\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[33],\"y\":[0.463238000869751]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"conversion - age directional output\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,100.02],\"title\":{\"text\":\"age\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1.0002],\"title\":{\"text\":\"conversion\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>conversion &#8211; age directional output. Exported with Neural Designer from the saved task report. Other inputs are held at the saved reference point. This is a model response, not a causal effect.<\/figcaption><\/figure><div class=\"nd-model-calculator\" id=\"nd-model-bank-marketing\"><h3>Explore the exported model<\/h3><p>This research demonstration runs locally in your browser. Values outside the training range are outside the validated domain and are rejected. A valid input range does not guarantee that a combination is physically or operationally plausible.<\/p><p>This is a research demonstration, not a validated decision policy.<\/p><div class=\"nd-model-fields\"><\/div><div class=\"nd-model-actions\"><button class=\"aui aui-button nd-model-calculate\" type=\"button\">Calculate<\/button><button class=\"aui aui-button aui-button--secondary nd-model-reset\" type=\"button\">Reset example<\/button><\/div><div class=\"nd-model-result\" role=\"status\" aria-live=\"polite\"><\/div><\/div><script data-noptimize=\"1\">(()=>{function Identity(x) {\n\treturn x;\n}\nfunction Sigmoid(x) {\n\tvar z = 1\/(1+Math.exp(-x));\n\treturn z;\n}\nfunction Tanh(x) {\n\treturn Math.tanh(x);\n}\nfunction calculate_outputs(inputs)\n{\n\tvar age = +inputs[0];\n\tvar job = +inputs[1];\n\tvar divorced = +inputs[2];\n\tvar married = +inputs[3];\n\tvar single = +inputs[4];\n\tvar education = +inputs[5];\n\tvar default_ = +inputs[6];\n\tvar balance = +inputs[7];\n\tvar housing = +inputs[8];\n\tvar loan = +inputs[9];\n\tvar contact_type = +inputs[10];\n\tvar day = +inputs[11];\n\tvar month = +inputs[12];\n\tvar campaing_contacts = +inputs[13];\n\tvar last_contact = +inputs[14];\n\tvar previous_contacts = +inputs[15];\n\tvar previous_conversion = +inputs[16];\n\n\tvar scaled_age = age*0.0947894305-3.907599449;\n\tvar scaled_job = job*1.518655896-2.115335703;\n\tvar scaled_divorced = divorced*2.0-1.0;\n\tvar scaled_married = married*2.0-1.0;\n\tvar scaled_single = single*2.0-1.0;\n\tvar scaled_education = education*1.530987859-3.298467398;\n\tvar scaled_default_ = default_*2.0-1.0;\n\tvar scaled_balance = balance*0.0003260642989-0.46947065;\n\tvar scaled_housing = housing*2.0-1.0;\n\tvar scaled_loan = loan*2.0-1.0;\n\tvar scaled_contact_type = contact_type*2.0-1.0;\n\tvar scaled_day = day*0.1211057007-1.933161855;\n\tvar scaled_month = month*0.4186587632-2.585724354;\n\tvar scaled_campaing_contacts = campaing_contacts*0.3185961246-0.8940794468;\n\tvar scaled_last_contact = last_contact*0.02058549225-4.604357243;\n\tvar scaled_previous_contacts = previous_contacts*0.5849971771-0.3166367114;\n\tvar scaled_previous_conversion = previous_conversion*2.0-1.0;\n\tvar dense_layer_1_output_0 = Tanh( -0.3255149722 + (0.2760237455*scaled_age) + (-0.3945139945*scaled_job) + (-0.002717128024*scaled_divorced) + (-0.1700698882*scaled_married) + (0.5006844997*scaled_single) + (0.08514449*scaled_education) + (0.4409671426*scaled_default_) + (0.4575832784*scaled_balance) + (-1.165626407*scaled_housing) + (0.08251368254*scaled_loan) + (0.5014973283*scaled_contact_type) + (-0.3375565708*scaled_day) + (-0.3038415015*scaled_month) + (-0.7118424773*scaled_campaing_contacts) + (-1.080514431*scaled_last_contact) + (1.953245401*scaled_previous_contacts) + (-0.01433659159*scaled_previous_conversion) );\n\tvar dense_layer_1_output_1 = Tanh( -0.01329175383 + (-0.5238117576*scaled_age) + (-0.6199336052*scaled_job) + (0.631274581*scaled_divorced) + (-0.8109204173*scaled_married) + (0.196600616*scaled_single) + (0.1874686033*scaled_education) + (-0.4036793709*scaled_default_) + (0.5231378078*scaled_balance) + (-1.187178016*scaled_housing) + (0.2821001112*scaled_loan) + (0.3396334946*scaled_contact_type) + (-0.05954987556*scaled_day) + (-0.07923880965*scaled_month) + (0.2932513952*scaled_campaing_contacts) + (-0.1676790565*scaled_last_contact) + (0.8285474181*scaled_previous_contacts) + (0.2301079631*scaled_previous_conversion) );\n\tvar dense_layer_1_output_2 = Tanh( -0.3080500662 + (0.04306419194*scaled_age) + (0.006945644505*scaled_job) + (-0.04634362087*scaled_divorced) + (0.2651463449*scaled_married) + (0.0878451243*scaled_single) + (-0.06806605309*scaled_education) + (0.1114098877*scaled_default_) + (0.04630449042*scaled_balance) + (0.09327366203*scaled_housing) + (0.1160442308*scaled_loan) + (0.05674093217*scaled_contact_type) + (-0.04490764812*scaled_day) + (-0.08219872415*scaled_month) + (-0.0204320401*scaled_campaing_contacts) + (-0.1204703823*scaled_last_contact) + (0.05457574129*scaled_previous_contacts) + (-0.6556266546*scaled_previous_conversion) );\n\tvar conversion = Sigmoid( 0.04496863857 + (1.428119779*dense_layer_1_output_0) + (-0.8936713338*dense_layer_1_output_1) + (-2.403325081*dense_layer_1_output_2) );\n\tvar out = [];\n\tout.push(conversion);\n\n\treturn out;\n}\nconst 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calculate=()=>{const inputs=[];let valid=true;cfg.fields.forEach((f,i)=>{const el=controls[i],v=Number(el.value);let bad=el.value.trim()===\"\"||!Number.isFinite(v);if(!f.categories?.length)bad=bad||v<f.min||v>f.max;el.setAttribute(\"aria-invalid\",bad?\"true\":\"false\");if(bad)valid=false;if(f.n>1){for(let k=0;k<f.n;k++)inputs.push(k===v?1:0);}else inputs.push(v);});if(!valid){result.textContent=\"Enter a valid value within every displayed input range.\";return;}let outputs=calculate_outputs(inputs);if(outputs.some(v=>!Number.isFinite(v))){result.textContent=\"The model could not evaluate these inputs.\";return;}result.textContent=outputs.map((v,i)=>cfg.output_names[i]+\": \"+Number(v.toPrecision(7))).join(\"\\n\");root.dataset.outputs=JSON.stringify(outputs);};root.querySelector(\".nd-model-calculate\").addEventListener(\"click\",calculate);root.querySelector(\".nd-model-reset\").addEventListener(\"click\",()=>{controls.forEach((el,i)=>el.value=String(cfg.fields[i].value));calculate();});calculate();})();<\/script><div class=\"ndb-downloads\"><a class=\"aui aui-button\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/bank-marketing-neural-designer-20260921.zip\">Download project and exports (ZIP)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/bank-marketing-data-20260921.csv\">Download source data (CSV)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/bank-marketing-schema-20260921.zip\">Download input schema (ZIP)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">Reproduce with Neural Designer<\/a><\/div><\/section>\n<section id=\"8-limitations\" class=\"ndb-card\"><h2>8. Evidence and limitations<\/h2><p>Past campaign outcomes depend on who was contacted and when. A new campaign needs temporal validation, verified contact-time feature availability, calibration and an experiment measuring incremental conversions. Codes such as job and education are numeric in this supplied project; their order is a modelling assumption.<\/p><p>No external validation or independent calibration study is included. Preprocessing statistics and model choices should be refitted within a prospective or grouped validation design. Correlations and directional responses describe associations, not causes. Human review is required before an operational decision.<\/p><p>Confidence intervals, subgroup performance, calibration curves and decision-cost validation are not established by these tasks. Predictive values apply to the observed test class distribution and may change when prevalence shifts.<\/p><\/section>\n<section id=\"references\" class=\"ndb-card\"><h2>References<\/h2><ul><li><a href=\"https:\/\/archive.ics.uci.edu\/dataset\/222\/bank+marketing\">Dataset source and provenance<\/a>.<\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/bank-marketing-neural-designer-20260921.zip\">Neural Designer project and saved task evidence, 21 September 2026<\/a>.<\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/\">Neural Designer testing-analysis documentation<\/a>.<\/li><\/ul><\/section>\n<\/div>\n<\/div>\n<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 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