Dear Reader,
This is the Repository of António Oliveira. Here I display the projects I already worked on, on which I apply Machine Learning, Deep Learning and Text Mining Methods
A brief description of each project follows. For more details the report of each project is also accessible.
Machine Learning I
In this project we were challenged to predict Hotel reservation cancelations. The notebook consists of an Exploratory Data Analysis, followed by Feature Selection and Modeling. The obtained results were then exported and introduced to kaggle, where we could see how our model performed in never-seen data. This was my first project using Machine Learning, in the 1st semester of the 2nd year of my bachelor.
Machine Learning II
This project is divided into 2 notebooks and an external py file. The first notebook consists of Exploratory Data Analysis, where we explore and clean our data to be used in the next notebook. The second notebook consists of the implementation of various clustering algorithms, where we aimed to group customers with similar characteristics. After doing so, Association Rules were created to try to identify what characterised each cluster. The py file named functions consists of functions used through both notebooks, keeping the notebooks cleaner and easier to read.
Deep Learning
In this project we were given images of people with cancer. Our goal was to preprocess this image and create a neural network that would accuratly identify the cancer displyed in the image it was given. We started by briefly exploring our data, and making the necessary adjustments. Then we processed the images by using methods such as bluring the background, aiming for our neural network to focus on the the cancer. Finally we made predictions and evaluated our results.
Text Mining
In this project we were given a dataset which included songs from various genres. We had 2 goals: predict the genre of the songs and then perform Sentiment Analysis. Consequently, this project is divided into 3 notebooks and an external py file. The first notebook is where EDA is performed, along with the required preprocessing for the data to be used in the following notebooks. The second notebook is where we import the preprocessed data and make predictions. In the third notebook Sentiment Analysis is performed by using lexicon-based algorithms. At the end, the performance of these algorithms is compared and conclusions are taken. Again, the py file named functions consists of functions used through the three notebooks, keeping the notebooks cleaner and easier to read.
Currently
Currently I am developing two different projects, while on Erasmus in the University of Mannheim, Germany. The first one consists on performing Sentiment Analysis on airline reviews by employing Machine Learning, Deep Learning and Lexicon-Based Methods. With the ML and DL models we aim to predict the Overall Rating given by the airline's clients given their review. With the Lexicon-based ones we aim to explore if the ratings present in our dataset match the sentiment transmited by the written reviews. In the second project, I was given the task to explore how AI influences Supply Chain Management, having chosen to predict the delivery time of food. I retrieved a dataset from Kaggle, and I am currently performing EDA and Training models, which will be used to make predictions.