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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>Examining interest in a vehicle-insurance offer<\/h2>\n<p>Cross-sell models can help a marketing team rank customers for further evaluation. This example evaluates the recorded response to a vehicle-insurance offer using demographic, vehicle and policy information.<\/p>\n<div class=\"ndb-kpis\">\n<div class=\"ndb-kpi\"><strong>381,109<\/strong><span>Source records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>7<\/strong><span>Final input features<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>76,221<\/strong><span>Test observations<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>0.843<\/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\/vehicle-insurance-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><span id=\"Contents\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"1-industrial-challenge\" class=\"ndb-card\"><h2>1. Business decision<\/h2><p>An insurance cross-sell exercise asks which customers show interest in an offer. This project encodes not-interested as the positive class, so interested customers lie at the low-score end of its native output.<\/p><div class=\"ndb-value-grid\"><div class=\"ndb-value\"><h3>Score direction<\/h3><p>High scores indicate not-interested.<\/p><\/div><div class=\"ndb-value\"><h3>Offer interest<\/h3><p>Use the opposite end of the score when studying interested customers.<\/p><\/div><div class=\"ndb-value\"><h3>Commercial outcome<\/h3><p>Interest classification alone does not establish conversion value or campaign profit.<\/p><\/div><\/div><div class=\"ndb-audience\"><span>Insurance analytics<\/span><span>Cross-sell planning<\/span><span>Campaign evaluation<\/span><\/div><div class=\"ndb-note\">Campaign profit is not established here. The exported profit task uses the opposite class from the intended conversion and its sample-ratio output is not valid, so that chart is excluded. Validate contact-time availability, calibration and uplift in a later campaign; include actual conversion value and intervention cost only after the target direction is correct.<\/div><\/section>\n<span id=\"DataSet\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><span id=\"DataSetInstances\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"2-data-set\" class=\"ndb-card\"><h2>2. Data set<\/h2><p>The supplied cross-sell dataset contains 381,109 records. Its category order is interested, not-interested, so the native output is a score for not-interested. Interested customers occupy the low-score end. The distinction is retained in the ROC, rate charts, confusion counts and calculator.<\/p><p>The downloadable project, saved report and supplied source CSV define the exact version used here. Repository: <a href=\"https:\/\/www.kaggle.com\/anmolkumar\/health-insurance-cross-sell-prediction\">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>228667<\/td><\/tr><tr><th scope=\"row\">Validation \/ selection<\/th><td>76221<\/td><\/tr><tr><th scope=\"row\">Testing<\/th><td>76221<\/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\">gender<\/th><td>Input<\/td><td>Binary<\/td><td>female; male<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">age<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">previously_insured<\/th><td>Input<\/td><td>Binary<\/td><td>no; yes<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">vehicle_age<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">vehicle_damage<\/th><td>Input<\/td><td>Binary<\/td><td>no; yes<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">annual_premium<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">vintage<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">response<\/th><td>Target<\/td><td>Binary<\/td><td>interested; not-interested<\/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-vehicle-insurance-t0-s11-20260921\"><div class=\"nd-chart-host\" id=\"nd-vehicle-insurance-t0-s11-20260921\" role=\"img\" aria-label=\"response pie chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: response 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\":[\"interested\",\"not-interested\"],\"marker\":{\"colors\":[\"#209fdf\",\"#092d40\"]},\"showlegend\":true,\"sort\":false,\"textinfo\":\"label+percent\",\"type\":\"pie\",\"values\":[12.300000190734863,87.69999694824219]}],\"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\":\"response pie chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"}}}<\/script><\/div><figcaption>response 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-vehicle-insurance-t2-s1-20260921\"><div class=\"nd-chart-host\" id=\"nd-vehicle-insurance-t2-s1-20260921\" role=\"img\" aria-label=\"response Pearson correlations chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: response 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\":[-0.3540000021457672,-0.21400000154972076,-0.09780000150203705,-0.052400000393390656,-0.02280000038444996,0.0010499999625608325,0.3409999907016754],\"y\":[\"vehicle_damage\",\"vehicle_age\",\"age\",\"gender\",\"annual_premium\",\"vintage\",\"previously_insured\"]}],\"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\":\"response 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\":[\"vehicle_damage\",\"vehicle_age\",\"age\",\"gender\",\"annual_premium\",\"vintage\",\"previously_insured\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>response 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.<\/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>7 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>7<\/td><td>7<\/td><td>\u2014<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>7<\/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>not-interested<\/strong>; interested 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-vehicle-insurance-t3-s1-20260921.png\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"ndb-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-vehicle-insurance-t3-s1-20260921.png\" alt=\"Examining interest in a vehicle-insurance offer: 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-vehicle-insurance-t4-s2-20260921\"><div class=\"nd-chart-host\" id=\"nd-vehicle-insurance-t4-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 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],\"y\":[0.6349999904632568,0.6269999742507935,0.6129999756813049,0.5889999866485596,0.550000011920929,0.5059999823570251,0.49399998784065247,0.44999998807907104,0.4059999883174896,0.4009999930858612,0.39800000190734863,0.3889999985694885,0.3869999945163727,0.38600000739097595,0.38499999046325684,0.3840000033378601,0.3840000033378601,0.382999986410141,0.38199999928474426,0.38199999928474426,0.38199999928474426,0.38100001215934753,0.3799999952316284,0.3790000081062317,0.3790000081062317,0.37700000405311584,0.37700000405311584,0.37599998712539673,0.37599998712539673,0.37599998712539673,0.375,0.37400001287460327,0.37400001287460327,0.37400001287460327,0.37400001287460327,0.37299999594688416,0.37299999594688416,0.37299999594688416,0.37299999594688416,0.37299999594688416,0.37299999594688416,0.3720000088214874,0.3720000088214874,0.3720000088214874,0.3720000088214874,0.3720000088214874,0.3720000088214874,0.3720000088214874,0.3720000088214874,0.3720000088214874,0.3720000088214874,0.3720000088214874,0.3720000088214874,0.3709999918937683,0.3709999918937683,0.3709999918937683,0.3709999918937683,0.3709999918937683,0.3709999918937683,0.3709999918937683,0.3709999918937683,0.3709999918937683,0.3709999918937683,0.3709999918937683,0.3709999918937683,0.3709999918937683,0.3709999918937683,0.3700000047683716,0.3709999918937683,0.3709999918937683,0.3709999918937683,0.3709999918937683,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716]},{\"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],\"y\":[0.628000020980835,0.6150000095367432,0.5899999737739563,0.5509999990463257,0.5059999823570251,0.4880000054836273,0.4440000057220459,0.4020000100135803,0.39800000190734863,0.39500001072883606,0.38600000739097595,0.3840000033378601,0.382999986410141,0.38199999928474426,0.38100001215934753,0.38100001215934753,0.3799999952316284,0.3799999952316284,0.3790000081062317,0.3790000081062317,0.3790000081062317,0.3779999911785126,0.37700000405311584,0.37700000405311584,0.375,0.37400001287460327,0.37400001287460327,0.37400001287460327,0.37299999594688416,0.37299999594688416,0.3720000088214874,0.3720000088214874,0.3720000088214874,0.3709999918937683,0.3709999918937683,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.3700000047683716,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36800000071525574,0.36800000071525574,0.36800000071525574,0.36800000071525574,0.36800000071525574,0.36800000071525574,0.36800000071525574,0.36800000071525574,0.36800000071525574,0.36800000071525574,0.36800000071525574,0.36800000071525574,0.36800000071525574,0.36800000071525574,0.36800000071525574,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36899998784065247,0.36800000071525574,0.36800000071525574,0.36800000071525574]}],\"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\":{\"b\":120,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Quasi-Newton method error history\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,97],\"title\":{\"text\":\"Epoch\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,0.7001000000000001],\"title\":{\"text\":\"Weighted squared error\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>Quasi-Newton method error history. 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>98<\/td><\/tr><tr><th scope=\"row\">Elapsed time<\/th><td>00:00:02<\/td><\/tr><tr><th scope=\"row\">Stopping criterion<\/th><td>Minimum loss decrease<\/td><\/tr><tr><th scope=\"row\">Training error<\/th><td>0.37<\/td><\/tr><tr><th scope=\"row\">Validation error<\/th><td>0.368<\/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 87.8%. 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>76,221 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 not-interested.<\/p><p>Test class prevalence is shown by the support counts. Accuracy is 68.8% and macro F1 is 0.604. ROC AUC is 0.843. 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\">interested<\/th><th scope=\"col\">not-interested<\/th><th scope=\"col\">Total<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">interested<\/th><td>8687<\/td><td>611<\/td><td>9298<\/td><\/tr><tr><th scope=\"row\">not-interested<\/th><td>23158<\/td><td>43765<\/td><td>66923<\/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\">interested<\/th><td>9298<\/td><td>93.4%<\/td><td>65.4%<\/td><td>27.3%<\/td><td>0.422<\/td><\/tr><tr><th scope=\"row\">not-interested<\/th><td>66923<\/td><td>65.4%<\/td><td>93.4%<\/td><td>98.6%<\/td><td>0.786<\/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-vehicle-insurance-t5-s1-20260921\"><div class=\"nd-chart-host\" id=\"nd-vehicle-insurance-t5-s1-20260921\" role=\"img\" aria-label=\"ROC chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: ROC chart. Enable JavaScript to explore it.<\/p><script type=\"application\/json\" data-src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-vehicle-insurance-t5-s1-20260921.json\"><\/script><\/div><figcaption>ROC 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-vehicle-insurance-t7-s1-20260921\"><div class=\"nd-chart-host\" id=\"nd-vehicle-insurance-t7-s1-20260921\" role=\"img\" aria-label=\"Positive (not-interested) rates chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: Positive (not-interested) rates 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,\"showlegend\":false,\"type\":\"bar\",\"x\":[\"Without model\",\"With model\"],\"y\":[0.8769999742507935,0.9860000014305115]}],\"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\":\"Positive rates\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"categoryarray\":[\"Without model\",\"With model\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1.0002],\"title\":{\"text\":\"Rate\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>Positive (not-interested) rates 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-vehicle-insurance-t7-s3-20260921\"><div class=\"nd-chart-host\" id=\"nd-vehicle-insurance-t7-s3-20260921\" role=\"img\" aria-label=\"Negative (interested) rates chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: Negative (interested) rates 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,\"showlegend\":false,\"type\":\"bar\",\"x\":[\"Without model\",\"With model\"],\"y\":[0.12300000339746475,0.27300000190734863]}],\"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\":\"Negative rates\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"categoryarray\":[\"Without model\",\"With model\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1.0002],\"title\":{\"text\":\"Rate\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>Negative (interested) rates 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-vehicle-insurance-t8-s1-20260921\"><div class=\"nd-chart-host\" id=\"nd-vehicle-insurance-t8-s1-20260921\" role=\"img\" aria-label=\"Cumulative gain chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: Cumulative gain chart. Enable JavaScript to explore it.<\/p><script type=\"application\/json\" data-src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-vehicle-insurance-t8-s1-20260921.json\"><\/script><\/div><figcaption>Cumulative gain chart. Exported with Neural Designer from the saved task report. This native curve is expressed in positive\/negative rates. It is a discrimination view, not a validated fraction-of-customers campaign gain.<\/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><div class=\"nd-model-calculator\" id=\"nd-model-vehicle-insurance\"><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 gender = +inputs[0];\n\tvar age = +inputs[1];\n\tvar previously_insured = +inputs[2];\n\tvar vehicle_age = +inputs[3];\n\tvar vehicle_damage = +inputs[4];\n\tvar annual_premium = +inputs[5];\n\tvar vintage = +inputs[6];\n\n\tvar scaled_gender = gender*2.0-1.0;\n\tvar scaled_age = age*0.06447286904-2.503004551;\n\tvar scaled_previously_insured = previously_insured*2.0-1.0;\n\tvar scaled_vehicle_age = vehicle_age*1.758303642-2.830200672;\n\tvar scaled_vehicle_damage = vehicle_damage*2.0-1.0;\n\tvar scaled_annual_premium = annual_premium*5.811419396e-05-1.777010083;\n\tvar scaled_vintage = vintage*0.01195201464-1.844769597;\n\tvar dense_layer_1_output_0 = Tanh( -0.07525964081 + (0.03136641532*scaled_gender) + (-0.4618100524*scaled_age) + (-0.6968461871*scaled_previously_insured) + (0.06026569754*scaled_vehicle_age) + (0.7736097574*scaled_vehicle_damage) + (-0.06314454228*scaled_annual_premium) + (0.009756513871*scaled_vintage) );\n\tvar dense_layer_1_output_1 = Tanh( -0.01377978642 + (-0.1034973413*scaled_gender) + (-1.340873122*scaled_age) + (1.353201151*scaled_previously_insured) + (-0.2833411992*scaled_vehicle_age) + (-0.142435953*scaled_vehicle_damage) + (0.02891632169*scaled_annual_premium) + (-0.008441716433*scaled_vintage) );\n\tvar dense_layer_1_output_2 = Tanh( -0.2074978352 + (-0.01334500592*scaled_gender) + (-0.4121468663*scaled_age) + (-0.4336774945*scaled_previously_insured) + (0.05919259042*scaled_vehicle_age) + (0.3061819673*scaled_vehicle_damage) + (0.1551056504*scaled_annual_premium) + (-0.01551497914*scaled_vintage) );\n\tvar response = Sigmoid( 1.611965656 + (-1.331129909*dense_layer_1_output_0) + (1.402276397*dense_layer_1_output_1) + (-0.808588326*dense_layer_1_output_2) );\n\tvar out = [];\n\tout.push(response);\n\n\treturn out;\n}\nconst cfg={\"fields\":[{\"name\":\"gender\",\"n\":1,\"type\":\"Binary\",\"categories\":[\"female\",\"male\"],\"min\":0.0,\"max\":1.0,\"value\":1,\"unit\":\"\"},{\"name\":\"age\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":20.0,\"max\":85.0,\"value\":21.0,\"unit\":\"\"},{\"name\":\"previously_insured\",\"n\":1,\"type\":\"Binary\",\"categories\":[\"no\",\"yes\"],\"min\":0.0,\"max\":1.0,\"value\":1,\"unit\":\"\"},{\"name\":\"vehicle_age\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":1.0,\"max\":3.0,\"value\":1.0,\"unit\":\"\"},{\"name\":\"vehicle_damage\",\"n\":1,\"type\":\"Binary\",\"categories\":[\"no\",\"yes\"],\"min\":0.0,\"max\":1.0,\"value\":0,\"unit\":\"\"},{\"name\":\"annual_premium\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":2630.0,\"max\":540165.0,\"value\":28619.0,\"unit\":\"\"},{\"name\":\"vintage\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":10.0,\"max\":299.0,\"value\":203.0,\"unit\":\"\"}],\"output_names\":[\"not-interested score\"],\"reference\":[0.9886124569141252]},root=document.getElementById(\"nd-model-vehicle-insurance\");if(!root)return;const grid=root.querySelector(\".nd-model-fields\"),result=root.querySelector(\".nd-model-result\"),controls=[];cfg.fields.forEach((f,i)=>{const wrap=document.createElement(\"div\");wrap.className=\"nd-model-field\";const label=document.createElement(\"label\"),id=root.id+\"-\"+i;label.htmlFor=id;label.textContent=f.name.replaceAll(\"_\",\" \")+(f.unit?\" (\"+f.unit+\")\":\"\");let input;if(f.categories?.length){input=document.createElement(\"select\");f.categories.forEach((name,k)=>{const option=document.createElement(\"option\");option.value=String(k);option.textContent=name;input.appendChild(option);});}else{input=document.createElement(\"input\");input.type=\"number\";input.step=\"any\";input.min=String(f.min);input.max=String(f.max);}input.id=id;input.value=String(f.value);wrap.append(label,input);grid.appendChild(wrap);controls.push(input);});const 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\/vehicle-insurance-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\/vehicle-insurance-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\/vehicle-insurance-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>Campaign profit is not established here. The exported profit task uses the opposite class from the intended conversion and its sample-ratio output is not valid, so that chart is excluded. Validate contact-time availability, calibration and uplift in a later campaign; include actual conversion value and intervention cost only after the target direction is correct.<\/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<span id=\"TutorialVideo\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"references\" class=\"ndb-card\"><h2>References<\/h2><p>Earlier video walkthroughs use a previous model version; the saved project and results above are the current reference.<\/p><ul><li><a href=\"https:\/\/www.youtube.com\/watch?v=WCVPOc_7U4g\">Watch the earlier tutorial<\/a><\/li><\/ul><ul><li><a href=\"https:\/\/www.kaggle.com\/anmolkumar\/health-insurance-cross-sell-prediction\">Dataset source and provenance<\/a>.<\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/vehicle-insurance-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 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 load=source.dataset.src?fetch(source.dataset.src).then(response=>{if(!response.ok)throw new Error(\"Chart data HTTP \"+response.status);return response.text();}).then(text=>{source.textContent=text;return JSON.parse(text);}):Promise.resolve(JSON.parse(source.textContent));load.then(figure=>window.Plotly.newPlot(host,figure.data,figure.layout,figure.config)).then(async()=>{const 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