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Stuart Lee's Projects

bondora_defaults icon bondora_defaults

Prediction of default rates for loan book of peer-2-peer lending company Bondora using logistic regression, decision tree and random forest models.. This is based on a dataset made available on Kaggle (https://www.kaggle.com/sid321axn/bondora-peer-to-peer-lending-loan-data) which was retrieved from a publicly available data set of a leading European P2P lending platform, Bondora (https://www.bondora.com/en). The retrieved data is a pool of both defaulted and non-defaulted loans from the time period between February 2009 and July 2021. The data comprises demographic and financial information of borrowers and loan transactions.

car_damage_classifier icon car_damage_classifier

Simple web app that classifies images of car damage using a Convolutional Neural Network model.

colombo-stock-exchange-investigation icon colombo-stock-exchange-investigation

Investigated seasonality effects* of stock prices on the Colombo Stock Exchange in Sri Lanka. Applied GARCH time series models in R. *(at certain times of the year, month share prices can rise or fall)

ds_project_auto_setup icon ds_project_auto_setup

Mini bash project: an automatic system and Github setup for Data Science Projects. Creates a local repository system for a data science project using cookiecutter. Establishes a virtual environment, installs commonly used DS libraries and creates a Jupiter Notebook Kernel. Creates a GitHub repo.

fastai icon fastai

The fastai deep learning library

fastbook icon fastbook

The fastai book, published as Jupyter Notebooks

ga-capstone-project icon ga-capstone-project

Classification of Chinese song lyrics using supervised and unsupervised models. Also a prototype music player that interfaces with Spotify to filter and play songs by language difficulty.

road_damage icon road_damage

Road damage segmentation project using FastAi. Data collection, labelling and inference.

webscraping-indeed icon webscraping-indeed

Web scraping salaries from indeed and salary prediction using Python and Scikitlearn

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