Anomaly Detection
Spring 2023
Tools:
In this project, we explored
machine learning techniques such as Hidden Markov Models (HMM) and
Reinforcement Learning (RL) to detect anomalies in household electricity consumption data.
I was actively involved in preprocessing the dataset using Principal Component Analysis (PCA),
training and optimizing the HMM model, and evaluating its performance based on Log Likelihoods and
Bayesian Information Criterion (BIC). We also discussed how RL could enhance flexibility in handling sequential
and incomplete data. Through this project, I gained valuable insights into applying advanced machine learning
methodologies effectively.
