Drill Bit vision
This repository contains code for training a convolutional neural network (CNN) model to classify images of drilling bits. The dataset includes images of various types of bits, and the goal is to accurately classify the images based on the type of bit. The code is written in Python and uses TensorFlow as the backend for the deep learning model.
In Progress.
Realtime Bit GrADE ESTIMATION APPLICATION
An application for real-time Bit Grade estimation was developed as a side project. Dummy data was created in Python and populated onto a MongoDB cluster. A dummy OSU API was developed to communicate with MongoDB using PyMongo. AWS S3 was utilized for cache storage and retrieval between events. A code coverage of over 95% was achieved through a Github CICD setup.
A two-class classification exercise project
The data in this exercise have been simulated to mimic real, dirty data. Please clean the data with whatever method(s) you believe to be best/most suitable. Success in this exercise typically involves feature engineering and avoiding data leakage. You may create new features. However, you may not add or supplement with external data.
For this exercise, you are required to build two models. The first model must be a logistic regression. The second model may be any supervised learning algorithm that is not from the GLM family.
Interplanetary Coronal Mass Ejection (ICME) Forecasting - Geomagnetic Storms
A time-series project:
Project Summary: You may have already heard about the so-called Carrington event that happen back in 1859. The Carrington event was such a powerful solar storm that sat the telegraph papers on fire! The Coronal Mass Ejection (CME) starts on the surface of the sun where the intense magnetic field makes an arc shape of materials that have a high tendency to snap (well-known coronal mass ejection).
Data is scraped and used for ICME speed estimation.
Binary Class Classification (NASA and Space) on Reddit data using NLP
The goal is to develop a binary class classification that would be able to recognize the Nasa community from Space Discussion. These two close categories were selected to increase the challenge and see how close AI can get using common ML algorithm libraries in python.
XGBoost Hyperparameters Tuning using Differential Evolution Algorithm - Frauds detection application
In this project, the metaheuristic algorithm is used for tuning machine learning algorithms hyper-parameters. A fraud detection project from the Kaggle challenge is used as a base project. The Project is composed of three distinct sections. Metaheuristic Algorithm (MA): Differential Evolution Algorithm (DEA) selected as an intelligent searching tool. The DE Algorithm works on top of the ML Algorithm (in this case XGBoost) to find the best set of hyper-parameters. Machine Learning Algorithm: The XGBoost which is a powerful machine learning algorithm is selected and the DEA is applied to find the best set of hyper-parameters. Final step: The Tuned ML algorithm is applied to the Fraud detection challenge (training, validation, and test). The results were promising and showed 89% accuracy on test data.
Big Data Project using Pyspark in Docker
In this project, the Kaggle Fraud detection challenge was studied. A docker image of Pyspark along with the Jupyter Notebook was used.