Lewis O.

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Finance

E-commerce

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Freelancer Overview

I have experience working with neural networks applied to financial data forecasting, particularly in building and experimenting with models designed for both classification and regression tasks. My work involved designing and training neural network architectures using Python, with the aim of identifying patterns in historical financial time-series data and using those patterns to generate predictive insights. For regression tasks, I focused on forecasting continuous outcomes such as future price movements, while in classification tasks I explored directional prediction, such as whether a financial instrument would move upward or downward over a given period. The workflow included preprocessing financial datasets, engineering relevant features, and normalizing input variables to improve model performance and stability. I also experimented with different network configurations, activation functions, and loss metrics to evaluate predictive accuracy and reduce curve fitting. Model evaluation was carried out using standard performance measures such as accuracy for classification and error-based metrics for regression. Through this process, I developed a practical understanding of how neural networks can be applied to financial forecasting problems, as well as their limitations when dealing with noisy and highly volatile market data.

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