@article{(Open Science Index):https://publications.waset.org/pdf/10013442,
	  title     = {Using Historical Data for Stock Prediction of a Tech Company},
	  author    = {Sofia Stoica},
	  country	= {},
	  institution	= {},
	  abstract     = {In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices over the past five years of 10 major tech companies: Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We implemented and tested three models – a linear regressor model, a k-nearest neighbor model (KNN), and a sequential neural network – and two algorithms – Multiplicative Weight Update and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement. },
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {18},
	  number    = {1},
	  year      = {2024},
	  pages     = {16 - 20},
	  ee        = {https://publications.waset.org/pdf/10013442},
	  url   	= {https://publications.waset.org/vol/205},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 205, 2024},
	}