Skip to content

README.md

Latest commit

 

History

History
32 lines (28 loc) · 1.16 KB

README.md

File metadata and controls

32 lines (28 loc) · 1.16 KB

Machine Learning

This course featured three mini projects.

Mini Project 1 - Predicting Fuel Consumption

This paper explores the prediction of the estimated fuel consumption in tons/day based on sensor readings from the different sensors on a ship.

Compared models:

  • Linear Regression
  • Polynomial Regression
  • Random Forest Regressor

Mini Project 2 - IMDB Sentiment Analysis

This paper presents a sentiment analysis of the IMDB movie review dataset using a beginner-friendly approach. The analysis incorporates GloVe embeddings, tokenization, and performance evaluation of XGBoost.

Compared models:

  • XGBoost
  • CNN
  • RNN

Mini Project 3 - Biomass Characterization through NIR Spectra

In this paper, I explore spectral biomass data to predict the moisture target value. Moisture estimation based on spectral data is essential for saving costs compared to weghing the materials separately.

Compared models:

  • Partial Least Squares Regression
  • Support Vector Machine Regression
  • Recurrent Neural Network