Machine Learning Prediction in R (R Programming Language)

Description: The description of the dataset is to come. This will be a supervised machine learning problem only. Where minimum of 3 machine learning algorithms must be used and you need to perform performance comparisons against each of the algorithms. Please state how much you expect for the project milestones and take into consideration this is in NZD. There will be two milestones for this project. Milestone 1: Detailed descriptive statistical analysis of the data (charts, graphs, interactions, etc). Data preprocessing (dealing with missing values, outliers, data transformation, data aggregation, etc.). I want to stress it is important to detail the preprocessing steps as it needs to be justified and each step needs to be clearly articulated why it’s being done. When it comes to charts, graphs, numerical summaries, etc. the analysis should be tied to the goals and objectives rather than being just random. Completion Date: 2nd April Milestone 2: As mentioned above, this will be a supervised machine learning problem only. Where 3 machine learning algorithms must be used and you need to perform performance comparisons against each of the algorithms. Please speak to me about the algorithms you want to apply before you start. Inclusive of the performing the ML algorithms, a detailed complete overview of your methodology used will be needed (CRISP-DM is the preference), details of the algorithms’ fine-tuning process and a detailed performance analysis of each algorithm/performance comparison of the algorithms as appropriate (cross-validation, AUC, etc.) I will need a critique of your approach, which underlines your assumptions, its limitations, its strengths and its weaknesses in a summary/conclusion format. More details to come. Completion Date: 7th May Process: Before you accept the project, I will chat with yourself and go through a more detailed version of what the expectations will be from the outset, so we’re both aware of what to expect. I’d prefer people who dedicate time a good amount of their working week to freelancing and are available to discuss the project as we go along.