These are various machine learning projects I have created showcasing my skills in using Python and its various libraries available for faster computations. These projects center around predicting data based on preexisting data, in order to predict trends and classify various types of data. These projects also showcase my problem solving and researching skills, which I use to create the various algorithms used in order to predict various types of data.
Below are the explanations for each python notebook:
LinearRegression: Demonstrates and implements linear regression to predict numerical data.
LogisticRegression: Demonstrates and implements logistic regression to predict categorical data.
NaiveBayesClassification: Demonstrates and implements naive bayes to predict categorical data.
KNearestNeighborclassification: Demonstrates and implements various KNN to predict categorical and numerical data.
NLP Spam Message Detection - Kelompok 9 (Felix, Michael, Tyrone): This is a group project done in Natural Language Processing class. Our group consists of me, Tyrone, and Felix from the same class. My part included doing text processing and modelling using Bag of Words using Python programming language. In this project, I demonstrated techniques to clean text to change it into a more understandable form by the computer, and techniques to change the text into vector form that can be used as data to train machine learning models to predict spam or ham text messages using Bag of Words vector representation and various classification models.
Project ML - Jeremy, Elroy, Joshua, Michael: This is a group project done in Machine Learning class. Our group consists of me, Jeremy, Elroy, and Joshua. This is a project demonstrating our abilities to implement various machine learning algorithms to try and predict a set of data. In this project, we use housing data from rumah123 (extracted in https://colab.research.google.com/drive/1-f21992kxdu5XbE_272mj6QhK5ZL5674?usp=sharing by Jeremy), and try to predict house prices based on the data of each house using data of each house. In this project, my part includes implementing a KNN algorithm which has been modified to accept numerical data and predict house prices by averaging the prices of K nearest neighbors.
To view the results directly, these links can be accessed:
LinearRegression: https://colab.research.google.com/drive/1efUZcdcitWCfiEx9y82giMWp7ed44SGq?usp=sharing
LogisticRegression: https://colab.research.google.com/drive/1--yLm0jgXGybq9hTBeRaZIOFP4A7LXA6?usp=sharing
NaiveBayesClassification: https://colab.research.google.com/drive/1IY2welgYH6AfjWHW50Ap72aZn3Z2pgRs?usp=sharing
KNearestNeighborclassification: https://colab.research.google.com/drive/1XTxOL7OLo5xyoQ4fUrpu0544vHKXoDGd?usp=sharing
NLP Spam Message Detection - Kelompok 9 (Felix, Michael, Tyrone): https://colab.research.google.com/drive/1gdDwXjNhCmuVGcOlF6r0vxke5SIi0Df6?usp=sharing
Project ML - Jeremy, Elroy, Joshua, Michael: https://colab.research.google.com/drive/1-f21992kxdu5XbE_272mj6QhK5ZL5674?usp=sharing