Amir

An end-to-end image captioning project using a CNN encoder (ResNet-50) and LSTM decoder in PyTorch. Includes vocabulary building, preprocessing, training with BLEU evaluation, and inference. Generates natural language captions for images with saved metrics, model checkpoints, and visualization outputs.

End-to-end sentiment analysis of tweets using BERT. Includes preprocessing, training, and evaluation with classification reports, confusion matrices, ROC curves, and word clouds. Demonstrates fine-tuning of transformer models for text classification with modular, reproducible code.

I’m Amirhosein Honardoust, a passionate developer and data scientist with experience in Python, PyTorch, deep learning, and machine learning projects across vision, NLP, and forecasting. I enjoy turning data into insights and building portfolio-ready AI applications that combine practicality with creativity.

Analyze and predict daily productivity using SQL, machine learning, and psychology. This project combines behavioral data, circadian rhythm analysis, and ElasticNet regression to model focus, stress, and performance, transforming work patterns into actionable insights.

An AI-driven productivity tracking app built with Python, Streamlit, SQLite, and Machine Learning. It logs and analyzes study sessions, predicts productivity using Random Forest models, and visualizes key insights to help learners improve focus, habits, and overall academic efficiency.

Image classification with PyTorch using Convolutional Neural Networks (CNNs). Trains on MNIST with convolution, pooling, and fully connected layers. Achieves over 99% accuracy with early stopping and checkpoints. Includes training/evaluation scripts, metrics, confusion matrix, training curves, and sample prediction visualizations.