🚀 Kaggle Competitions I’ve Completed:

Here’s a list of Kaggle competitions I’ve worked on, using various tools and frameworks such as Python, Pandas, TensorFlow, Keras, and PyTorch. Each project has helped me develop skills in key areas like Data Analysis, Computer Vision, and Deep Learning.

1. Titanic: Machine Learning from Disaster

• Technologies: Python, Machine Learning, Pandas
• Focus: Binary classification of survival outcomes on the Titanic dataset.

2. House Prices: Advanced Regression Techniques

• Technologies: Python, Machine Learning, Pandas, Keras, Gradient Boosting
• Focus: Predicting house prices based on various features using regression models.

3. Digit Recognizer

• Technologies: Python, Machine Learning, Computer Vision, Gradient Boosting, Loss Function, Pandas, Keras, TensorFlow
• Focus: Image classification using MNIST dataset to recognize handwritten digits.

4. I’m Some Kind of a Painter Myself

• Technologies: Python, Machine Learning, Computer Vision, Pandas, Keras, TensorFlow, GANs
• Focus: A creative project using Generative Adversarial Networks (GANs) for painting generation.

5. Flowers Classification with TPU

• Technologies: Python, Machine Learning, Computer Vision, Pandas, Keras, TensorFlow, TPU, GANs
• Result: Accuracy of 0.87, ranked 47 out of 126.
• Focus: Flower classification using Tensor Processing Units (TPUs) to speed up model training.

6. SIIM-ISIC Melanoma Classification

• Technologies: Python, PyTorch, TorchVision, Machine Learning, Deep Learning, Computer Vision, GANs, EfficientNet, ResNet, etc.
• Focus: Classifying melanoma from skin images using advanced Deep Learning techniques.

7. UBC Ovarian Cancer Subtype Classification and Outlier Detection

• Technologies: Python, PyTorch, TorchVision, Machine Learning, Deep Learning, Computer Vision, EfficientNet, ResNet, etc.
• Result: Ranked 180 out of 1026.
• Focus: Detecting cancer subtypes and outliers in medical images.

8. Multi-Class Prediction of Cirrhosis Outcomes

• Technologies: Python, Machine Learning, Numpy, XGBoost, etc.
• Result: Ranked 468 out of 780.
• Focus: Predicting outcomes of cirrhosis patients using multi-class classification.

9. Hacking the Human Vasculature in 3D

• Technologies: Python, Machine Learning, Deep Learning, EfficientNet, etc.
• Focus: Segmenting vasculature in 3D scans of human kidney using advanced imaging techniques.

🎓 Relevant Coursework:

• Neural Networks and Computer Vision, Samsung AI Center Russia (December 2023)
  • Deep learning techniques applied to neural networks and computer vision problems.

🔭 What’s Next?

I’m always eager to expand my knowledge and tackle new challenges in Machine Learning and AI. Check out my projects, follow me on Kaggle, and feel free to reach out for collaborations!

📫 How to Reach Me:

• LinkedIn
• Kaggle
• Email 🚀