Dimensionality reduction is an important technique to overcome the curse of dimensionality in data science and machine learning. As the number of predictors (or dimensions or features) in the dataset increase, it becomes computationally more expensive (ie. increased storage space, longer computation time) and exponentially more difficult to produce accurate predictions in classification or regression models. Moreover, it is hard to wrap our head around to visualize the data points in more than 3 dimensions.
Recent posts by Khaing Win
9 min read
Visualizing Time Series Data of Stock Prices
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Time series data, simply put, is a set of data points collected at regular time intervals. We encounter time series data every day in our lives - stock prices, real estate market prices, energy usage at our homes and so on. So why should we care about this data? Because understanding time series data, especially of stock prices, could help you to be on a path to make $$$.
