Customer Loan Enquiry - ANN, Django

The objective of this project is to predict if a customer will get a loan given applicant income, loan amount, loan amount term, credit history, education status, self-employment status, property area, etc. A model is trained using the training data on previous customers’ loan approval history. A web service is created, which runs the trained model in the background. The service presents an interface through which any user can request to get an automated decision/prediction (i.

April 5, 2017

Refining Word Embeddings - Word2Vec, Glove, FastText, LexVec

The objective is to implement different word embeddings and investigate the performance on a dataset. For this task, the chosen dataset includes tweets about disasters, e.g., earthquake, wildfire. Among the tweets, some are fake and some are real. So, there are two labels such as 1:Real tweet and 0: Fake tweet. This data has been chosen so that we can see if the features extracted using different embedding techniques can retain distinguishable information to detect which tweet is fake and which one is real.

April 5, 2017

Question Classification - SVM, Logistic Regression, LSTM, BERT, Doc2Vec, TF-IDF

The objective is to build a question classification model. The questions have six different categories such as: Description(DESC), Entity(ENTY), Abbreviation(ABBR), Human(HUM), Location(LOC), Numeric Value(NUM). To investigate different approaches, the following data is used (downloaded from https://cogcomp.seas.upenn.edu/Data/QA/QC/): Training set 5(5500 labeled questions) Test set: TREC 10 questions Different data analyses have been performed and four different models are trained. The models are the followings: Tf-Idf + SVM: Tf-Idf is used for vectorizing texts and a linear model (i.