Team Fast-tabulous was formed for the Queensland AI’s Fast.ai course project. Our focus is on tabular data using the Homesite Competition data from Kaggle. We’re made up of enthusiastic learners comprising

• Tim Cummings
• Tracy Dinh
• Jorge
• Nissan Dookeran
• Owen Powell
• Michael Senescall

Posts

• Deploying App in Voila by Tim Cummings

  App was deployed on an Amazon AWS spot instance. Final app source code available at https://github.com/timcu/fast-tabulous-app/blob/main/fast-tabulous-with-db.ipynb](https://github.com/timcu/fast-tabulous-app/blob/main/fast-tabulous-with-db.ipynb) . Final app can be used at [https://tabulous.pythonator.com

  Jul 30, 2021

• First Pass Using Fastai Tabular For Homesite Competition by Nissan Dookeran

  Here I use fastai with as many defaults as possible to make a "first-pass" submission to Kaggle for the Homesite competition.

  Jul 29, 2021

• Another Pass Using Fastai Tabular For Homesite Competition by Nissan Dookeran

  Here I use fastai with some changes to the defaults to make another submission to Kaggle for the Homesite competition.

  Jul 29, 2021

• Random Forest Classifier or Regressor - Which to choose by Owen

  A basic comparison of predictions when using an off the shelf sklearn classifier verus regressor, with different criterion to split decision trees. The data is taken from the Homesite Competition on Kaggle.

  Jul 21, 2021

• Moving TabularLearner from GPU to CPU by Tim Cummings

  Once the TabularLearner which contains a TabularModel has been trained on a GPU, we no longer require the GPU as predictions work on much smaller amounts of data. However, moving the TabularLearner to a CPU is not straightforward. This post shows you how.

  Jul 19, 2021

• Pickled Model using ipywidgets by Tim Cummings

  This notebook loads a previously trained model and uses it to predict quote success rate using user input to change fields. User input uses ipywidgets generated on the fly to match allow altering of the most sensitive fields. Final app source code available at https://github.com/timcu/fast-tabulous-app/blob/main/fast-tabulous-with-db.ipynb](https://github.com/timcu/fast-tabulous-app/blob/main/fast-tabulous-with-db.ipynb) . Final app can be used at [https://tabulous.pythonator.com

  Jul 13, 2021

• Load pickled model and convert quote to successful by Tim Cummings

  This notebook loads a previously trained model and uses it to predict quote success rate. Then any quote can be chosen (from train or test) and a sensitivity analysis will determine which fields can be changed to turnaround quote success.

  Jul 12, 2021

• HomeSite Quote Conversion A Fastai Tabular Approach by Nissan Dookeran and Tracy Dinh

  We used Fastai Tabular libary and WalkWithFastai functions to build our Deep Learning model for HomeSite Quote Conversion competition in Kaggle. The techniques used in this notebook include Permutation Importance Analysis, Model Ensembling, Bayesian Optimisation for hyperparameter tuning and Entity Embddings.

  Jul 8, 2021

• Fastai EDA AND Xgboost Optuna Hyperparameters Tuninhg by Tracy Dinh

  This notebook uses Fast AI library for EDA and Optuna for hyperparameter tuning on Kaggle Homesite Quote Conversion Data set.

  Jul 4, 2021

• Permutation Importance and Ensemble Experiments in Fastai Tabular For Homesite Competition by Nissan Dookeran

  Here I borrow generously from Zach's notebook

  Jun 29, 2021

• Improving `cont_cat_split` in Fastai Tabular For Homesite Competition by Nissan Dookeran

  Here I improve on fastai's `cont_cat_split` function and add some changes to the defaults to make another submission to Kaggle for the Homesite competition.

  Jun 27, 2021

• Improving `cont_cat_split` in Fastai Tabular For Homesite Competition by Nissan Dookeran

  Here I improve on fastai's `cont_cat_split` function and add some changes to the defaults to make another submission to Kaggle for the Homesite competition.

  Jun 27, 2021

• Random Forest and Data Exploration by Michael Senescall

  This is a basic random forest model for the purposes of data exploration and establishing a basline performance which can be referenced when more advanced models are used. At the end of this notebook, the most important columns are identified.

  Jun 26, 2021

• Python EDA Librares for Large Dataset (Google Colab) by Tracy Dinh

  This notebook applies 4 different python EDA Packages (Pandas Profiling, Sweetviz, Dtale and Autoviz) on Kaggle Homesite Quote Conversion Data set (~200k rows and 300 columns)

  Jun 26, 2021

• Synthentic data by Jorge

  Synthethic data work by Jorge

  Jun 25, 2021