JavaFX How To: Custom choice dialog from large table
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Updated
May 4, 2021 - Java
JavaFX How To: Custom choice dialog from large table
A simple Java program to perform quantified data analysis on a very large dataset of air traffic information
Modeling student's knowledge over time using Riiid's EdNet dataset comprising of 100M+ student interactions. EDA and Feature Engineering was performed on the data and models were trained on important features.
Chart.js plugin for large datasets.
A large number of files about football matches since 1990 are to be cleaned and fed to the model. Then, different models are trained with the dataset, and the best performing model is selected. The hyperparameters of this model are tuned, so its performance is improved.
Experimentation on PRODIGY framework
NYS Traffic Tickets Issued: Four Year Window Visualization using Matplotlib, Python, Pandas, Seaborn and Tableau
Large dataset ETL of JSON and CSV files using pandas, regex, and export to SQL database
Exploration of two important strategies to make our data analysis faster and independent of the dataset size.
Road energy profile estimation based on vehicle movement historical records
This project entails conducting an exploratory analysis of the Brazil Traffic Accidents spanning from 2007 to 2023. The aim is to utilize Python for merging the records from each year, thoroughly cleansing, validating the data, and subsequently visualizing it in Tableau.
A memory-efficient matching algorithm (Kuhn–Munkres and Hopcroft–Karp) implementation based on JGraphT in Java
Determine the image segmentation of nuclei cell from original image in training and testing. Image Segmentation
[2021 Summer Research in Applied Machine Learning] Built two desktop apps using PyQt5 to perform pixel-classification with k-means clustering to explore the chemical composition of objects scanned with high-resolution x-rays.
An interactive visualization that displays a large cluttered knowledge-base of Dietary Supplement data. Used the D3.js JavaScript library and Angular to develop the visualization and its animations. It allows exploring relationships of the data with ease.
Improve processing time for large data sets
The goal of this competition is to predict a neutrino particle’s direction, with a model based on data from the "IceCube" detector.
Example of processing large amount of data from the data source
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