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In this work we have analyzed the variation of speed of sound and traceanomaly inside a neutron star using neural network. We construct family ofagnostic equation of state by maintaining thermodynamic stability, speed ofsound bound with constraints from recent observation of neutron stars. The dataof mass and radius serves an input for the neural network and we obtain speedof sound as an output. The speed of sound shows non-monotonic behaviour and thetrained data predicts softer equation of state. The neural network predictsstiff equation of state at the centre of intermediate mass stars and massivestars have relatively softer equation of state at their centre. The traceanomaly shows a non-monotonic behaviour for the untrained data however, when wetrain our data; with neural network the trace anomaly shows monotonic behaviourand the condition of $\Delta \ge 0$ is maintained.

Analyzing the speed of sound in neutron star with machine learning