This article is a nice application of supervised learning, one of the sub categories of ML. The steps for the method they use are as follows:
Data Collection: Collect data for the dataset
Data Normalization: Make the dataset more efficient for applying the K Nearest Neighbor (KNN) classifier. This is done by computing the stock return, computing the z-score (open value - (mean value/standard deviation)), applying z-score for outlier detection, and removing the outliers using panda's code
Apply KNN classifier
This was helpful for me to understand how this type of supervised learning would work. Another helpful part of the article is that it suggests looking into deep learning techniques such as LSTM, RNN and CNN.
Noahhobe
commented
4 years ago
This article is a nice application of supervised learning, one of the sub categories of ML. The steps for the method they use are as follows:
This was helpful for me to understand how this type of supervised learning would work. Another helpful part of the article is that it suggests looking into deep learning techniques such as LSTM, RNN and CNN.