  We propose three different data-driven approaches for pricing European-style
call options using supervised machine-learning algorithms. These approaches
yield models that give a range of fair prices instead of a single price point.
The performance of the models are tested on two stock market indices: NIFTY$50$
and BANKNIFTY from the Indian equity market. Although neither historical nor
implied volatility is used as an input, the results show that the trained
models have been able to capture the option pricing mechanism better than or
similar to the Black-Scholes formula for all the experiments. Our choice of
scale free I/O allows us to train models using combined data of multiple
different assets from a financial market. This not only allows the models to
achieve far better generalization and predictive capability, but also solves
the problem of paucity of data, the primary limitation of using machine
learning techniques. We also illustrate the performance of the trained models
in the period leading up to the 2020 Stock Market Crash (Jan 2019 to April
2020).
