Passionate about AI, deep learning, and open source contributions. This portfolio showcases comprehensive Kaggle competition solutions demonstrating expertise across computer vision, NLP, and ensemble methods.
22 comprehensive Kaggle competition solutions
Ranked 3rd out of 1,621 participants. Multilingual solution using XLM-RoBERTa with adversarial training and two-stage model training pipeline.
Ranked 6th out of 3,308 participants. Deep learning solution using EfficientNet with metadata fusion and advanced augmentation techniques.
Ranked 16th out of 3,943 participants. Large-scale fraud detection using LightGBM ensemble with sophisticated feature engineering.
Ranked 58th out of 1,345 teams. Multi-label classification of intracranial hemorrhages in CT scans using advanced CNN architectures.
Ranked 589th out of 3,900 teams. Agricultural AI solution for identifying disease types on Cassava Leaf images.
Ranked 284th out of 2,120 teams. Fine-grained classification for identifying whales by their tail patterns.
Ranked 21st out of 1,868 teams. Predict demand for online classified ads using ensemble methods and feature engineering.
Ranked 21st out of 1,571 teams. Improve automated understanding of complex question answer content using NLP techniques.
Ranked 75th out of 2,928 teams. Detect diabetic retinopathy to prevent blindness using computer vision and medical AI.
Ranked 29th out of 4,539 teams. Identify and classify toxic online comments using NLP and text classification techniques.
Ranked 138th out of 1,309 teams. Identify doodles accurately using computer vision and sketch recognition techniques.
Ranked 61st out of 521 teams. Recognize artwork attributes from The Metropolitan Museum of Art using computer vision.
Ranked 208th out of 2,225 teams. Extract support phrases for sentiment labels using NLP and sentiment analysis.
Ranked 78th out of 1,143 teams. Automate detection of bird and frog species using audio processing and CNN.
Ranked 292nd out of 3,219 teams. Segment salt deposits beneath Earth's surface using computer vision and segmentation.
Ranked 61st out of 1,241 teams. Identify horror authors from their writings using text classification and NLP.
Ranked 336th out of 3,330 teams. Ship or iceberg detection from satellite images using CNN architectures.
Multi-label toxic comment classification using LSTM and CNN architectures with word embeddings.
Ranked 41st out of 838 teams. Pair pronouns to their correct entities using NLP and coreference resolution.