Applied Data Analysis: Machine learning approaches to predict the demands for bike sharing based on relevant data such as weather, season, holiday, etc, which are known to influence the demands for bike renting.

• FIT5149_S2_2020_A1.pdf: Assignment specifications (i.e. questions)
• 30945305_FIT5149_Ass1.ipynb/pdf: Assignment solutions documented in Markdown, analysis was done using R.
• test.csv: Testing dataset.
• train.csv: Training dataset.
• Input dataset contains weather information, number of bikes rented per hour and date information.

Tasks completed:

• Developed models to accurately predict the number of bikes required.
• Described and justified the choice of my models.
• Analysed and interpreted the results.
• Identified a subset of attributed that have a significant impact on the prediction of the bike demands.
• Reported these attributes with statistical evidence

R libraries used: ggplot2, coorplot, car, reshape2, e1071, stats, scales, grid, gridExtra, glmnet, lattice, repr, lubridate