Amir
@AmirhosseinHonardoustData Scientist & ML Engineer | ML, NLP, Forecasting & Analytics | Building reliable AI systems from data to decision
Language Breakdown
Lines of code distribution across 116 owned repositories
I-Shaped Developer
I-shapedSpecialist β deep expertise in Python
Collaboration Network
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Repos
129
PRs
0
Growth
+18%
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Coding Streak
Contribution activity over the past year
Melika Tavakoli
@melikatavakoli
ERNINTEEN
@ERNINTEEN
parmis delfani
@p-delfani
HC59648
@HC59648
Jaweid Moraadi
@jaweid123
Top Repositories
A professional TF-IDF + Logistic Regression style-risk classifier for educational fake-news detection, with a Streamlit dashboard, honest evaluation, uncertainty handling, and leakage analysis.
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.
Predict the profitability of potential coffee shop locations using SQL and Python. Combines data engineering with feature-rich regression modeling, visual analytics, and business insights to support data-driven site selection and retail decision-making.
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.
Iβm 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.
Predict stock prices using LSTM networks in PyTorch. This project covers data preprocessing, sliding window creation, model training with early stopping, and evaluation with RMSE/MAE/MAPE. Includes visualizations of training loss, predicted vs actual prices, and short-horizon forecasts.
Python project for Market Basket Analysis. Generates synthetic retail transactions, mines frequent itemsets using Apriori & FP-Growth, derives association rules, and outputs CSVs + visualizations. Portfolio-ready example demonstrating data science methods for uncovering product co-purchase patterns.
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.
Detect and classify fraudulent transactions using SQL and Python. Generate behavioral features with SQLite, train a Logistic Regression model, and evaluate performance with AUC, precision, recall, and ROC analysis. A complete supervised fraud detection workflow.
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.
Open Source Impact
Contributions to external projects
