A Python package develop for transportation spatio-temporal big data processing, analysis and visualization.

Implements an entire machine learning pipeline to train and evaluate a Random Forest Classifier on labeled gait data for walking. Data generated during the experiment has led to helpful insights in to the problem domain.

Code for PerCom paper 'Edge2Guard: Botnet Attacks Detecting Offline Models for Resource-Constrained IoT Devices'

Dynamic Data-set preparation for audio, video, images

Maching Learning Project : A classification problem using K Nearest Neighbor(KNN), Decision Tree, Support Vector Machine, and Logistic Regression algorithms

Transfer learning for image classification using pre-trained models like ResNet50, ResNet100, EfficientNetB0, and VGG16 in Keras. Fine-tunes the last layers, applies image augmentation, and evaluates with Precision, Recall, AUC, F1 score, and early stopping for improved performance.

A series of six hands-on projects completed during my PGP ML and AI academic training with UT Austin and Great Learning

Customer Segmentation using Clustering Algorithms

Tugas akhir saat saya melakukan virtual internship ID/X pada laman rakamin

Exercises related to data preprocessing and cleaning

Machine Learning Algorithm to predict the House prices.

Using data pre-processing and machine learning methods to predict loan defaults of a large Freddie Mac dataset. Collaborative project with George Boutselis for Machine Learning in Python (MATH11205) at the University of Edinburgh. Awarded 80%.