Automated Cross-Validation Framework for Time Series Model Selection

Created an automated framework for a demand forecasting problem to choose the best-performing model on a weekly basis. The models considered are: Facebook Prophet Moving Average Simple Exponential Smoothing Holt Winter's ARIMA

Medical Segmentation

Developed a model to automate the detection of Tumour in a MRI. Implemented the encoder-decoder-based deep learning architecture:- U-NET with Adam optimizer. Optimized the model by converting it to a TFLite Model to reduce the latency.

Plant Seedling Classification (Kaggle Competition)

Used pre-trained VGG16, ResNet50, and InceptionV3 networks to extract bottleneck features and build a model on top of each of them to evaluate and compare the model performances. (Model performances include classification report, confusion matrices, plots of Loss Vs Epochs) Optimized models...

Credit Card Default and LIME

Used tree-based algorithms (Decision tree, Random Forest, XGBoost) to determine whether a client will default or not. Explored LIME library and concepts like PD plots, local interpretation, surrogate models to explain the overall as well as instance-wise model performance.