https://www.kaggle.com/ashishpatel26/home-credit-default-risk-001

Assessing approval risk for home credit applications

Title /Abstract / Block Diagram - Nishad Data you plan to use/ ER Diagram - Vishal Machine Learning Algo / Metrics to measure - Naimesh Description of pipelines -Nishad List team member - VIshal

Indication - Nishad EDA Feature Engineering - Vishal Data Formatting / Feature Engineering Primary - EDA (Secondary) - Nishad Model selection / Hyper Parameter tuning / Feature Engineering - Naimesh

Feature engineering - Bureau and bureau balance - Vishal
Credit card , Installments, previous applications - Nishad Application train , POS_CASH_Balance , Previous applications - Naimesh

Phase - 1 submission work distribution

Downloaded the data correctly - Done (Kaggle Part)

Good EDA - In progress - Vishal

Additional feature engineering. Identification and development of additional features - In progress - Summary statistics

Use of Pipeline - Done

Data prep - Done Appropriate us of train, validate and test splits. Filled in missing data, aggregated data appropriately (e.g., aggregated by member ID/year to generate predictions for a given individual in a given year).

Experimental results table - Friday

Statistical significance test - Friday Baseline vs. challenger. Best "new" model versus prior best

Input Features/Feature Selection - Naimesh / Nishad Evaluation and selection of best features for the model.

Discussion/analysis of results.

Project/team participation

ppt - Nishad - Friday

Consolidated notebook - Naimesh