Asset price Prediction Using Principal Component Analysis And Machine Learning Regression Model
In this post, we are trying to predict tomorrow’s price of a financial asset using a machine learning method and show how we can improve the prediction result by using a feature extraction technique such as principal component analysis (PCA). What is feature extraction? feature extraction is the process of selecting the most relevant and informative features from a dataset to improve the performance of machine learning models used for financial analysis. Feature extraction helps in reducing the number of features in the model and creating new features from the existing attributes. The feature selection process delivers unique features that contribute the most to the prediction outcomes by removing noise and irrelevant features. In finance, several types of feature extraction techniques are used to identify the most relevant features from a dataset. Some of the most commonly used feature extraction techniques include: - Principal Component Analysis (PCA): A statist