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

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

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

Exercises related to data preprocessing and cleaning

Customer Segmentation using Clustering Algorithms

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%.