{"id":3472,"date":"2023-08-31T11:12:59","date_gmt":"2023-08-31T11:12:59","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/car-price-assignment\/"},"modified":"2026-08-05T14:58:27","modified_gmt":"2026-08-05T12:58:27","slug":"car-price-assignment","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/car-price-assignment\/","title":{"rendered":"Pricing cars using machine learning"},"content":{"rendered":"\n<style>\n.ndb{box-sizing:border-box;overflow-x:hidden}.ndb a{color:#2d799f}.ndb-executive a{color:inherit}\n.ndb-wrap{line-height:1.45}.ndb-lead{margin:0 0 28px;color:#3a4a5a;font-size:18px;line-height:1.6}\n.ndb-card h2{position:relative}.ndb-card h2:after{content:\"\";position:absolute;bottom:-1px;left:0;width:62px;height:3px;border-radius:2px;background:#56a1c8}\n.ndb-card h3{margin:28px 0 12px;color:#12354b;font-size:20px}.ndb-card code{padding:2px 5px;border-radius:4px;background:#e8eef2;color:#12354b}\n.ndb-card img{box-shadow:0 12px 28px rgba(0,18,51,.12)}.ndb-card img.ndb-architecture{width:min(1080px,100%)}.ndb-card img.ndb-chart-wide{width:min(780px,100%);max-width:100%}.ndb-card img.ndb-chart-square{width:min(560px,100%);max-width:100%}\n.ndb-figure-grid{margin:24px 0}.ndb-figure-grid figure{display:flex;flex-direction:column;justify-content:space-between}.ndb-figure-grid img{box-shadow:none}.ndb-figure-grid figcaption{color:#405361;font-size:14px;line-height:1.45}.ndb-figure-full{grid-column:1\/-1;width:min(100%,620px);justify-self:center}\n.ndb-table-scroll{max-width:100%;overflow-x:auto}.ndb-card table{border-collapse:separate;border-spacing:0;overflow:hidden;border-radius:12px;box-shadow:0 10px 24px rgba(0,18,51,.08)}.ndb-card tbody tr:last-child td{border-bottom:0}.ndb-card table.ndb-metrics{font-variant-numeric:tabular-nums}.ndb-card table.ndb-metrics td:not(:first-child){text-align:right}\n.ndb-card .ndb-kpi{border-color:#dce8ef;background:#f8fbfd}.ndb-card .ndb-kpi strong{color:#12354b}.ndb-card .ndb-kpi span{color:#5e707d}\n.ndb-flow{display:grid;grid-template-columns:repeat(5,minmax(0,1fr));gap:10px;margin:24px 0}.ndb-flow div{position:relative;display:flex;min-height:94px;align-items:center;justify-content:center;padding:16px 13px;border-radius:13px;background:#12354b;color:#fff;text-align:center;font-weight:700}.ndb-flow div:not(:last-child):after{content:\"\u2192\";position:absolute;right:-15px;top:50%;z-index:2;transform:translateY(-50%);color:#56a1c8;font-size:21px}\n.ndb-case{margin:26px 0;padding:24px;border:1px solid #cfe0ea;border-radius:16px;background:#f8fbfd}.ndb-case h3{margin-top:0}.ndb-case-grid{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:20px}.ndb-case-grid table{width:100%!important;margin:0 auto!important}\n.ndb-case-grid table thead th{background:#123f55!important;color:#fff!important}.ndb-case-grid table tbody th{background:#eaf2f6!important;color:#12354b!important;text-align:left!important;font-weight:700!important}.ndb-case-grid table tbody tr:nth-child(even) th{background:#f4f8fa!important}.ndb-case-grid table tbody td{background:#fff!important;color:#102f43!important}\n.ndb-result-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:12px;margin:20px 0}.ndb-result{padding:17px;border:1px solid #d8e4eb;border-radius:12px;background:#fff;text-align:center}.ndb-result span{display:block;color:#5e707d;font-size:13px}.ndb-result strong{display:block;margin-top:6px;color:#12354b;font-size:23px}\n.ndb-note--warning{border-left-color:#d79a29;background:#fff9ed}.ndb-note--critical{border-left-color:#c64c4c;background:#fff5f5}\n@media(max-width:900px){.ndb-flow{grid-template-columns:1fr 1fr}.ndb-flow div:after{display:none}.ndb-case-grid{grid-template-columns:1fr}}\n@media(max-width:620px){.ndb-flow,.ndb-result-grid{grid-template-columns:1fr}.ndb-card table{font-size:13px}.ndb-card th,.ndb-card td{padding:9px 8px}}\n<\/style>\n<style>\n.ndb{width:100vw;margin-left:calc(50% - 50vw);padding:22px 24px 14px;background:#eee;color:#1b2635;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif}\n.ndb *{box-sizing:border-box}.ndb-wrap{width:min(100%,1200px);margin:0 auto}.ndb a{text-decoration:none}\n.ndb-executive{margin:0 0 34px;padding:32px;border-radius:20px;background:linear-gradient(135deg,#12354b,#245e80);color:#fff}\n.ndb-executive h2{margin:0 0 12px;color:#fff;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;background:rgba(255,255,255,.1)}.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}.ndb-actions a,.ndb-downloads a{padding:12px 20px;border-radius:24px;background:#245e80;color:#fff;font-weight:700}.ndb-executive .ndb-actions a{background:#fff;color:#12354b}\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 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%}\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}\n.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-figure-grid{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:20px}.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@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 14px}.ndb-kpis,.ndb-figure-grid{grid-template-columns:1fr}.ndb-executive{padding:24px 20px}}\n<\/style>\n<div class=\"ndb\">\n<div class=\"ndb-wrap\">\n<section class=\"ndb-executive\">\n<h2>Estimate vehicle price from specifications for market benchmarking<\/h2>\n<p>A compact neural network maps vehicle configuration, dimensions, powertrain and efficiency data to an indicative price. The model can support early product planning and portfolio benchmarking, while commercial decisions remain subject to current-market data and expert review.<\/p>\n<div class=\"ndb-kpis\">\n<div class=\"ndb-kpi\"><strong>205<\/strong><span>vehicles<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>25 \u2192 208<\/strong><span>source inputs \u2192 encoded features<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>3<\/strong><span>hidden neurons<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>0.912<\/strong><span>testing R\u00b2<\/span><\/div>\n<\/div>\n<div class=\"ndb-actions\"><a href=\"#7-model-deployment\">Review the pricing scenario<\/a><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/10\/car_price_assignment.csv\">Download the data<\/a><\/div>\n<\/section>\n<ul class=\"ndb-toc\">\n<li><a href=\"#1-industrial-challenge\">Industrial challenge<\/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. Industrial challenge<\/h2><p>Automotive product teams need an early view of how a proposed vehicle sits within a reference market before detailed costing, positioning and launch decisions are complete. This example builds an approximation model that estimates dataset price from 25 vehicle descriptors and exposes local sensitivity for specification reviews.<\/p><div class=\"ndb-value-grid\"><div class=\"ndb-value\"><strong>Benchmark new concepts<\/strong><span>Estimate the price position associated with a proposed vehicle specification.<\/span><\/div><div class=\"ndb-value\"><strong>Review specification scenarios<\/strong><span>Explore how a modelled price changes around a named reference configuration.<\/span><\/div><div class=\"ndb-value\"><strong>Support portfolio decisions<\/strong><span>Combine technical evidence with pricing, cost and market expertise.<\/span><\/div><\/div><div class=\"ndb-audience\"><span>Product planning<\/span><span>Pricing strategy<\/span><span>Vehicle engineering<\/span><span>Market intelligence<\/span><span>Data science<\/span><\/div><div class=\"ndb-note\"><strong>Scope of this example.<\/strong> This is a supervised pricing surrogate for the published dataset. It is not a live valuation service, a transaction-price engine or evidence that changing one component causes the predicted price change.<\/div><\/section>\n<section id=\"2-data-set\" class=\"ndb-card\"><h2>2. Data set<\/h2>\n<p>The dataset contains <strong>205 vehicles<\/strong> and <strong>27 columns<\/strong>: one identifier, 25 candidate inputs and the target <code>price<\/code>. <code>car_id<\/code> is excluded from modelling. Categorical variables are encoded by Neural Designer, so the 25 source inputs expand to 208 numerical features.<\/p>\n<div class=\"ndb-table-scroll\"><table>\n<thead><tr><th>Variable group<\/th><th>Fields<\/th><th>Role in the study<\/th><\/tr><\/thead>\n<tbody>\n<tr><th scope=\"row\">Market and configuration<\/th><td><code>symboling<\/code>, <code>car_brand<\/code>, <code>car_name<\/code>, fuel, aspiration, doors, body, drive and engine location<\/td><td>Vehicle identity, risk rating and configuration<\/td><\/tr>\n<tr><th scope=\"row\">Dimensions and mass<\/th><td>Wheelbase, length, width, height and curb weight<\/td><td>Package size and vehicle mass<\/td><\/tr>\n<tr><th scope=\"row\">Powertrain<\/th><td>Engine type, cylinders, engine size, fuel system, bore, stroke, compression, horsepower and peak RPM<\/td><td>Mechanical specification and performance<\/td><\/tr>\n<tr><th scope=\"row\">Efficiency<\/th><td>City and highway MPG<\/td><td>Fuel-consumption indicators<\/td><\/tr>\n<tr><th scope=\"row\">Target<\/th><td><code>price<\/code><\/td><td>Dataset price in US dollars<\/td><\/tr>\n<\/tbody><\/table><\/div>\n<p>The configured random split assigns 123 rows to training, 41 to selection and 41 to testing. The published CSV has been byte-checked against the local dataset used to build the current project.<\/p>\n<div class=\"ndb-figure-grid\">\n<figure><img decoding=\"async\" class=\"ndb-chart-wide\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/car-price-distribution-2026.png\" alt=\"Distribution of vehicle prices in the car-price dataset\"><figcaption>Prices are strongly right-skewed: 40.5% of the vehicles fall in the lowest histogram bin, while high-price examples are sparse.<\/figcaption><\/figure>\n<figure><img decoding=\"async\" class=\"ndb-chart-wide\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/car-price-horsepower-scatter-2026.png\" alt=\"Vehicle price versus horsepower scatter chart\"><figcaption>Horsepower has a strong positive association with price, but the dispersion shows why a multivariable model is needed.<\/figcaption><\/figure>\n<figure class=\"ndb-figure-full\"><img decoding=\"async\" class=\"ndb-chart-square\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/car-price-correlations-2026.png\" alt=\"Pearson correlations between numeric vehicle inputs and price\"><figcaption>Curb weight, engine size and horsepower have the strongest positive Pearson correlations; city and highway MPG have strong negative correlations. These are univariate associations, not causal effects.<\/figcaption><\/figure>\n<\/div>\n<div class=\"ndb-note\"><strong>Validation boundary.<\/strong> The 60\/20\/20 random split reproduces the project, but a professional assessment should hold out complete model families or later market periods. High-cardinality vehicle names make row-level validation especially optimistic for catalogue expansion.<\/div><\/section>\n<section id=\"3-model\" class=\"ndb-card\"><h2>3. Model<\/h2>\n<p>The model receives <strong>25 source variables<\/strong>. Categorical expansion produces <strong>208 numerical input features<\/strong>, which feed a dense hidden layer with three tanh neurons and a one-neuron identity output layer. Scaling is applied before the dense layers and the output is unscaled back to dollars.<\/p>\n<div class=\"ndb-note\">The compact hidden layer is intentional: with only 123 training rows, increasing network width would add parameters faster than the dataset adds independent evidence.<\/div>\n<img decoding=\"async\" class=\"ndb-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/car-price-network-2026.png\" alt=\"Neural network architecture for vehicle price estimation\"><\/section>\n<section id=\"4-training\" class=\"ndb-card\"><h2>4. Training strategy<\/h2>\n<p>Training minimizes normalized squared error with L2 regularization (weight 0.01) using the quasi-Newton method. The latest run contains 114 completed epochs: training error ends at 0.00318, while selection error reaches a minimum of 0.0308 at epoch 14 and ends at 0.0426.<\/p>\n<img decoding=\"async\" class=\"ndb-chart-wide\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/car-price-training-history-2026.png\" alt=\"Quasi-Newton training and validation error history\">\n<div class=\"ndb-note ndb-note--warning\"><strong>Small-data signal.<\/strong> Training error continues decreasing after validation error reaches its minimum. The testing results below come from the final project, but a production workflow should lock the stopping rule and confirm stability across repeated splits.<\/div>\n<\/section>\n<section id=\"5-selection\" class=\"ndb-card\"><h2>5. Model selection<\/h2>\n<p>No additional neuron-selection sweep was used for the updated model. The existing three-neuron hidden layer already provides a compact baseline with good selection and testing performance, so adding complexity would not be justified by the current evidence.<\/p>\n<div class=\"ndb-note\"><strong>What this decision means.<\/strong> The architecture is deliberately restrained; it is not claimed to be a global optimum. A future comparison should use repeated or nested validation rather than selecting a neuron count from a single 41-row selection subset.<\/div>\n<\/section>\n<section id=\"6-testing\" class=\"ndb-card\"><h2>6. Testing analysis<\/h2>\n<p>The independent testing subset contains 41 vehicles. The exported project gives a determination coefficient of 0.912; error metrics in dollars make the result easier to interpret operationally.<\/p>\n\n<div class=\"ndb-table-scroll\"><table class=\"ndb-metrics\">\n<thead><tr><th>Testing metric<\/th><th>Result<\/th><th>Interpretation<\/th><\/tr><\/thead>\n<tbody>\n<tr><th scope=\"row\">Testing vehicles<\/th><td>41<\/td><td>Independent rows in the configured random split<\/td><\/tr>\n<tr><th scope=\"row\">R\u00b2 (determination)<\/th><td>0.912<\/td><td>Agreement between observed and predicted prices<\/td><\/tr>\n<tr><th scope=\"row\">R\u00b2 (SSE definition)<\/th><td>0.908<\/td><td>Variance explained relative to the testing-set mean<\/td><\/tr>\n<tr><th scope=\"row\">Mean absolute error<\/th><td>$1,392<\/td><td>Typical absolute pricing error<\/td><\/tr>\n<tr><th scope=\"row\">Root mean squared error<\/th><td>$1,881<\/td><td>Gives more weight to large misses<\/td><\/tr>\n<tr><th scope=\"row\">95th-percentile absolute error<\/th><td>$3,729<\/td><td>Only about 5% of testing errors are larger<\/td><\/tr>\n<tr><th scope=\"row\">Maximum absolute error<\/th><td>$4,891<\/td><td>Largest miss among the 41 testing vehicles<\/td><\/tr>\n<\/tbody><\/table><\/div>\n<div class=\"ndb-note\">The model is useful as a benchmarking surrogate inside this dataset. These metrics do not establish accuracy for current listings, new model years, unseen brands or transaction prices in another geography.<\/div>\n<div class=\"ndb-figure-grid\">\n<figure class=\"ndb-figure-full\">\n<img decoding=\"async\" class=\"ndb-chart-square\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/car-price-goodness-of-fit-2026.png\" alt=\"Observed versus predicted vehicle prices for the testing subset\">\n<figcaption>Observed and predicted prices for the 41 testing vehicles. Most points remain close to the diagonal, although the sparse upper-price range contains larger absolute errors.<\/figcaption>\n<\/figure>\n<\/div><\/section>\n<section id=\"7-model-deployment\" class=\"ndb-card\"><h2>7. Model deployment<\/h2>\n<p>In a professional workflow, the network would act as a specification-to-price surrogate. Its estimate should support product planning and market benchmarking, with input validation and analyst review rather than automatic price publication.<\/p>\n<div class=\"ndb-flow\"><div>Vehicle specification or portfolio database<\/div><div>Schema and category validation<\/div><div>Car-price neural network<\/div><div>Estimate and local sensitivity<\/div><div>Pricing and engineering review<\/div><\/div>\n<div class=\"ndb-case\">\n<h3>Representative specification review<\/h3>\n<p>The reference point used by Neural Designer is a Honda Civic CVCC configuration from the dataset. It is shown as an operational example, not as an independent validation case: the row belongs to the training subset.<\/p>\n<div class=\"ndb-case-grid\">\n<table><thead><tr><th>Input<\/th><th>Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Insurance risk rating<\/th><td>2<\/td><\/tr><tr><th scope=\"row\">Brand \/ model<\/th><td>Honda \/ Civic CVCC<\/td><\/tr><tr><th scope=\"row\">Fuel \/ aspiration<\/th><td>Gas \/ standard<\/td><\/tr><tr><th scope=\"row\">Doors \/ body<\/th><td>2 \/ hatchback<\/td><\/tr><tr><th scope=\"row\">Drive \/ engine location<\/th><td>Front-wheel drive \/ front<\/td><\/tr><tr><th scope=\"row\">Wheelbase<\/th><td>86.6 in<\/td><\/tr><tr><th scope=\"row\">Length \u00d7 width \u00d7 height<\/th><td>144.6 \u00d7 63.9 \u00d7 50.8 in<\/td><\/tr><tr><th scope=\"row\">Curb weight<\/th><td>1,819 lb<\/td><\/tr><tr><th scope=\"row\">Engine type \/ cylinders<\/th><td>OHC \/ 4<\/td><\/tr><\/tbody><\/table>\n<table><thead><tr><th>Input<\/th><th>Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Engine size<\/th><td>92 in\u00b3<\/td><\/tr><tr><th scope=\"row\">Fuel system<\/th><td>1bbl<\/td><\/tr><tr><th scope=\"row\">Bore \u00d7 stroke<\/th><td>2.91 \u00d7 3.41 in<\/td><\/tr><tr><th scope=\"row\">Compression ratio<\/th><td>9.2<\/td><\/tr><tr><th scope=\"row\">Horsepower<\/th><td>76 hp<\/td><\/tr><tr><th scope=\"row\">Peak engine speed<\/th><td>6,000 rpm<\/td><\/tr><tr><th scope=\"row\">City \/ highway economy<\/th><td>31 \/ 38 mpg<\/td><\/tr><tr><th scope=\"row\">Dataset price<\/th><td>$6,855<\/td><\/tr><\/tbody><\/table>\n<\/div>\n<div class=\"ndb-result-grid\">\n<div class=\"ndb-result\"><span>Model estimate<\/span><strong>$6,682<\/strong><\/div>\n<div class=\"ndb-result\"><span>Dataset price<\/span><strong>$6,855<\/strong><\/div>\n<div class=\"ndb-result\"><span>Difference<\/span><strong>$-173 (-2.5%)<\/strong><\/div>\n<\/div>\n<p>The model estimates $6,682, compared with the dataset value of $6,855. This close match demonstrates how the deployed calculation is read, but it must not be added to the testing performance because the row was used for training.<\/p>\n<\/div>\n<h3>Engine-size sensitivity at the reference point<\/h3>\n<p>Holding all other fields fixed, the directional output increases from approximately $6,510 at 50 in\u00b3 to $15,500 at 350 in\u00b3. The grey point marks the 92 in\u00b3 reference configuration and its $6,682 prediction.<\/p>\n<img decoding=\"async\" class=\"ndb-chart-wide\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/car-price-engine-size-directional-2026.png\" alt=\"Directional output of predicted price against engine size\">\n<div class=\"ndb-note ndb-note--warning\"><strong>Engineering constraint.<\/strong> Changing engine size alone while freezing horsepower, cylinder count, mass and fuel economy can create an unrealistic vehicle. For design decisions, evaluate coherent specification packages or apply explicit feasibility constraints.<\/div>\n<h3>Integrate the exported model<\/h3>\n<p>The Python package contains input preprocessing, the trained 208\u20133\u20131 network and output unscaling. Its <code>predict_vehicle()<\/code> function accepts the original 25-field vehicle record and performs the categorical encoding internally. The repaired vector implementation was checked against the Neural Designer project; the maximum difference across the 41 testing rows was below $43, caused by the decimal precision of the generated Python weights.<\/p>\n<div class=\"ndb-downloads\"><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/car-price-python-model-2026.zip\">Download Python model (ZIP)<\/a><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/car-price-neural-designer-project-2026.zip\">Download Neural Designer project (ZIP)<\/a><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/10\/car_price_assignment.csv\">Download dataset (CSV)<\/a><\/div><\/section>\n<section id=\"8-limitations\" class=\"ndb-card\"><h2>8. Scope and limitations<\/h2>\n<ul>\n<li>The dataset contains 205 vehicles and no transaction date, model year, inflation index, dealer incentive or regional market field; it is not a current-market pricing feed.<\/li>\n<li><code>car_name<\/code> has 144 categories, and the complete categorical encoding expands 25 source variables to 208 features while only 123 rows are used for training. This creates a material overfitting risk.<\/li>\n<li>The random row split does not prove transfer to unseen models or brands. In the configured testing subset, 24 of the 35 represented model-name levels do not appear in training.<\/li>\n<li>Pearson correlations and one-variable directional outputs describe associations around this dataset; they are not causal estimates of the commercial value of an engineering change.<\/li>\n<li>Before production use, retrain on recent transactions or validated list prices, separate data by model family and time, compare against a transparent baseline and report prediction intervals.<\/li>\n<li>Deployment should reject unknown categories, monitor price and feature drift, retain model\/version traceability and require commercial review for decisions outside the validated domain.<\/li>\n<\/ul>\n<\/section>\n<section id=\"references\" class=\"ndb-card\"><h2>References<\/h2><ul><li><a href=\"https:\/\/www.kaggle.com\/datasets\/hellbuoy\/car-price-prediction\" target=\"_blank\" rel=\"noopener\">Car Price Prediction dataset<\/a>, Kaggle.<\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\">Neural Designer testing analysis<\/a>.<\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment#DirectionalOutputs\">Neural Designer directional outputs<\/a>.<\/li><\/ul><\/section>\n<\/div>\n<\/div>\n","protected":false},"author":13,"featured_media":2484,"template":"","categories":[29],"tags":[41],"class_list":["post-3472","learning","type-learning","status-publish","has-post-thumbnail","hentry","category-examples","tag-automotive"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Pricing cars using machine learning<\/title>\n<meta name=\"description\" content=\"Use machine learning to create a pricing model from a dataset with car features (power, dimensions, etc.) and prices.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, 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