My submission for the DeepTraffic Competition by MIT 6.S094: Deep Learning for Self-Driving Cars

Churn Modelling Using ANN with Parameter tuning for best accuracy

Using machine learning techniques with Kepler Space Telescope exoplanet search data to train and tune classification models

Predict SpaceX first stage landing status, part of IBM data science professional capstone project

UK Bank, WeWashUSleep

Machine Learning Classification on Unbalanced Real World Dataset

Assignments, Projects and other course related material.

BRKGA for the Home Health Care Routing and Scheduling Problem

This project is to help NASA in discovering hidden planets outside of our solar system using the data collected from NASA Kepler space telescope over nine years. To make it happen, we will create machine learning models capable of classifying candidate exoplanets from the raw dataset.

Densim is a library for efficient similarity search and clustering of dense vectors, which are numerical representations of data such as images, text, or audio.

We compared the predictive accuracy and sparsity of support vector machines and relevance vector machines for a range of synthetic data sets differing in signal-to-noise ratio and other measures of difficulty.

MAG Data Exploration and Predict Citations

This repository contains the codebase and resources for a machine learning-based project aimed at predicting loan eligibility for individuals. The project utilizes various algorithms and data preprocessing techniques to build predictive models that assess the likelihood of an applicant being eligible for a loan based on historical data.

Genetic Algorithms in Java. Easy to use and extensible by design

Binary classification and Multiclass classification with pipelining and parameter tuning with GridsearchCV and RandomizedSearchCV

Applied ML algorithms on Enron Dataset to predict Person of Interest (POI)

Collaborated with Guoliang Li, Jiesi Liu, and Jianming Wu

mengetahui apa itu data training dan apa itu data testing. Selain itu, kita juga telah mempraktikkan bagaimana cara melakukan pembagian (*splitting*) antara data training dan data testing menggunakan berbagai rasio

Parameter tuning and Architecture Design for Face Recognition using Convolutional Neural Networks