Engineered sequential key logs data from 11 to 146 features to capture its hidden sequential nature. Reduced dimensionality of data using PCA and further optimized SVM and LightGBM models using Bayesian hyperoptimization to predict essay scores.

Designed 3-layered multi-layer perceptron in PyTorch tuned hyperparameters using RandomizedSearchCV.

Achieved 35% boost on performance (from Baseline RMSE) through sequential data retention and transfer learning on transformers.

[Project Report | Github Link]

 Check whether the following list of words are used as a metaphor or literal, on training over the skewed dataset

1. 1. 1. 1. 1. Road

            2. Light

            3. Boat

            4. Candle

            5. Spice

            6. Train

            7. Ride

Got familiar with the NLTK, Spacy, and other word tokenization techniques

Used several machine learning models used in the course and techniques such as bagging and boosting y

Further, we used LSTM and BERT models to check the highest performance we can achieve using the Deep Learning Techniques

Access full project report here.

[Github Link]

Visualized various datasets in unique forms as presented on the slides and the website, The goal was to effectively how represent complex data in the most interpretable form using the classic visualization library D3.js

Dashboard: Tableau_DataViz_ClimateChange 

[ Project Slides | Project Report | Project Website | Github Link ]