Salary prediction
Estimate salaries using employee features and patterns learned from available training data.
Explore salary prediction →Explore how experience, education, and skills come together to create a thoughtful salary estimate.
Try the estimate workflow with sample profiles and get a clear, labeled salary insight in seconds.
Go beyond the prediction. Explore the learning process.
Estimate salaries using employee features and patterns learned from available training data.
Explore salary prediction →Explore how inputs move through hidden layers to generate a numerical salary prediction.
Explore the network →Understand how prediction errors drive parameter updates during neural network training.
Understand the learning process →Explore the mathematics behind the model. This visual map is illustrative; live training happens in the browser training center.
Weighted inputs plus a bias form the pre-activation.
An activation function transforms the signal.
Loss measures the gap between reality and the estimate.
Gradient descent nudges parameters toward lower loss.
Experience, education, job role, and supported skills.
Turn your answers into a clear profile the app can read.
The app compares your profile with learned salary patterns.
You receive a simple salary estimate.
Review the estimate and the context behind it.
Explore the sample profiles behind the salary patterns. These are helpful signals, not guarantees.
Sample profiles are ready to help you explore salary patterns.
Try an estimate →See which profile details shape the estimate.
Compare your result with the available sample profiles.
Estimates depend on the training data and are not guaranteed compensation.
Explore the relationship between experience, skills, and estimated compensation through neural intelligence.
Training, evaluation, data, and learning tools live here so the main salary experience stays simple.
Choose a focused workspace for the next step.
Choose the learning setup, start training, and save a new model.
Open training →Check evaluation charts and compare the active model with its test results.
Open performance →Inspect records, import a CSV, or restore the sample dataset.
Open dataset →Use the visual lab and step-by-step simulator to understand the learning flow.
Open network lab →Explore salary estimates built from experience, education, skills, and available sample profiles.
Explore the sample profiles behind each estimate.
A simple comparison with available examples
Available after a saved estimate
Available sample profiles · patterns are not guarantees
A simple view of your saved profile insights.
Add a few details to explore a personalized salary estimate.
Add your details and get a salary estimate in a moment.
ESTIMATED SALARY
This is an estimate for exploration, not a promise of compensation. Sample profiles may not reflect every workplace or market.
Real gradient-based learning, with every epoch accounted for.
Only compare rows with the same dataset fingerprint and test split. Training uses the final epoch, without test-based selection.
| Version | Dataset / split | Architecture | MAE | RMSE | R² | Date |
|---|
No saved models yet. Train your first network to see real evaluation results.
Follow the signal. Trace the gradient. Understand how a network learns.
A real scalar calculation: x = 1, y = 2, ŷ = wx + b. Starts with w = 0.5, b = 0. This is not the salary model.
The salary model averages squared errors over each batch: MSE = (1/n) Σ(yᵢ − ŷᵢ)². Gradients flow through hidden layers using the chain rule.
| Years of experience | Education level | Job role | Skill score | Department | Annual salary |
|---|
No matching records.
Required columns: experience, education, role, skill, department, salary. 60–5,000 rows; maximum 2 MB. Experience: 0–40, skill: 0–100, annual salary: 1,000–100,000,000. No missing fields. Up to 30 categories per categorical feature. Extra columns are excluded.
Use only data you have permission to process. Do not include names or protected attributes. Imported data is unverified; meaningful categories and appropriate licensing remain your responsibility. Replacing the dataset invalidates the active model, but preserves historical predictions.
Saved estimates, their inputs, and the exact model behind them.
| ID | Date | Job role | Years of experience | Annual salary | Version | Actions |
|---|
Save a prediction from the studio to build your history. No sample predictions are added.
Salary estimateDisplay preferences and transparent runtime information.
Currency labels, not foreign-exchange conversion. Set this to the source dataset's currency. Saved predictions retain their original currency.
This miniapp runs a real JavaScript ANN, not a hosted Python API. Training yields to the browser between epochs. Models, dataset and history use the platform's per-user storage, not SQL. There is no external salary data service, guaranteed service availability, or independently validated real-world model.
Learn moreA real learning engine. An honest account of its limits.
The feedforward multilayer perceptron uses standardized experience and skill, plus one-hot encoded education, role and department. Hidden layers use ReLU or tanh; the salary regression output is linear. MSE gradients are computed by the chain rule and optimized with Adam or minibatch SGD.
A fixed seeded shuffle reserves 20% for testing. Validation is taken from the remaining records using the training seed. Numerical scaling, categorical vocabulary and target standardization are fitted only on training data. Unknown categories use an explicit unknown slot. Test data is evaluated only after the final epoch. Artifacts store weights, biases, preprocessing, configuration and evaluation together.
The initial 480 records are reproducible synthetic examples generated with seed 2026. They are not real employee salaries and carry no market-validity claim. The synthetic generator defines a teaching dataset; predictions always come from the trained ANN, never from that generator. User CSV uploads replace the demo and are marked unverified.
Educational exploration only. Not for hiring, compensation decisions or employment screening. No fairness audit, causal feature attribution, confidence interval or external validation is provided. Salary outputs can be negative or implausible; they are not silently clipped. Protected attributes are not predictive features. Categories may still carry societal biases.
The active model and up to six version summaries are saved per user. Predictions are saved only on request, up to 500 records. Dataset changes invalidate the active model. Stored artifacts are validated before loading. Storage failures are reported and the current session remains usable. No server or database is provisioned by this browser edition.
The engine checks numerical gradients against finite differences before training. Training rejects nonfinite loss. Metrics are computed in original annual salary units on the held-out test data. This is not a substitute for a full independently executed automated test suite or production security review.