In this Kaggle competition, Social Impact for Women in Impoverished Countries WIDS_DataThon_2018 competition, the aim is to predict the gender of each survey respondent based on demographic and behavioural information from a representative sample of survey respondents from India and their usage of traditional and mobile financial services. This notebook is an exploratory data analysis of the dataset.

In this Kaggle competition, Social Impact for Women in Impoverished Countries WIDS_DataThon_2018 competition, the aim is to predict the gender of each survey respondent based on demographic and behavioural information from a representative sample of survey respondents from India and their usage of traditional and mobile financial services. This notebook includes model training to create the predictions file for submission to Kaggle.

Inspired by Rob Hyndman’s talk at IASC-ARS/68th Annual NZSA Conference I decided to perform an exploratory data analysis of smart meter data. There are a variety of open energy data resources. This analysis will use ErNOC green button open data set.

The goal is to compare different models and system times, trialling H20 for supervised machine learning model training.

An exercise in ARIMA modeling and using time series R packages ggfortify, tseries and forecast

See Shiny App https://kimnewzealand.shinyapps.io/IMDBMovies/

Interactive map of Rangitoto Volcano Island in Auckland, New Zealand

Project 2