machine_learning

Email notification I finally bit the bullet and updated my previously hideous email notification! You may find the updated email notification template here - alongside with the code. Feel free to ping me if you are keen to be on the email list too. ~ Jirong import smtplib, ssl import datetime import pandas as pd from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart #Format text data = pd.read_csv(‘/home/jirong/Desktop/github/ETF_watchlist/Output/yahoo_crawled_data.csv’) data[‘Change_fr_52_week_high’] = round(100 * data[‘Change_fr_52_week_high’], 1) data = data[[‘Name’, ‘Price’, ‘Change_fr_52_week_high’]].

Q learning I just completed a Reinforcement Learning assignment - in particular on Q-learning. According to Wikipedia here, it’s a model-free Rl algorithm. The goal for the algo is to learn a policy, which tells an agent what action to take under different circumstances. Here’s my confession. What I’m doing in this post is to summarise what I’ve just learnt so that I may come back to this at any point in future.

Building a decision tree from scratch Sometimes to truly understand and internalise an algorithm, it’s always useful to build from scratch. Rather than relying on a module or library written by someone else. I’m fortunate to be given the chance to do it in 1 of my assignments for decision trees. From this exercise, I had to rely on my knowledge on recursion, binary trees (in-order traversal) and object oriented programming.