The Photometric LSST Astronomical Time-Series Classiﬁcation Challenge Kaggle: PLAsTiCC Astronomical Classiﬁcation ? Oct2018–Dec2018 Brussels,Belgium
• This project aims to classify simulated astronomical time series data in preparation for observations from the Large Synoptic Survey Telescope (LSST).
• I used cesium library in Python to extract features from the raw time series data and then implemented Gradient Boosting for the classiﬁcation problem.
Biomedical Image Segmentation
Kaggle: Data Science Bowl 2018 ? Jan2018–Mar2018 Ahmedabad,India
• This project aims to automate nuclei identiﬁcation in divergent cells images to advance medical discovery.
• I used Deep Learning for this task and implemented the U-Net architecture. U-Net is a Fully Convolutional Network (FCN) that does image segmentation and then predicts each pixel’s class.
I used Keras framework with TensorFlow backend for the implementation.
Dental x-ray Image Segmentation
Byte Prophecy ? Nov2017–Apr2018 Ahmedabad,India
• This project aims to identify the cavities present in a given dental x-ray image.
• The project was done in three phases. During the ﬁrst phase I only used mathematical morphology. I implemented the sliding windows approach in Python and used OpenCV.
• Realizing the limitations of a simple morphological approach, for the second phase I implemented Random Forests and Gradient Boosting classiﬁers.The downside to this approach is that the features are hard-coded.
• During the third phase, I implemented my ﬁrst Deep Learning model.
I used a Fully Convolutional Neural Network which yielded the best results.
Miscelleneous ML Projects
• Quora Question Pairs: Identify question pairs that have the same intent.
• SpamCl assiﬁer:Classify an email as spam or non-spam.
• Movies Recommender: Recommend movies to user based on the ratings provided. Used CollaborativeFiltering.
• Digit Recognizer: Classify handwritten digits using the famous MNIST data.