Preparing and preprocessing the data
3. Finding rules, including appropriate parameter setting
4. Determining which of the resulting rules are interesting
5. Figuring out how the interesting rules could be useful
1. Objectives: What is the domain and what are the potential benefits to be derived from
association rule mining. This is high level - not find patterns, but what would improve
because of the use of the patterns.
2. Data set description: What is in the data, and what preprocessing was done to make it
amenable for association rule mining. Where choices were made (e.g., parameter settings
for discretization, or decisions to ignore an attribute), describe your reasoning behind the
3. Rule mining process: Parameter settings, choice of algorithm and the time required.
Using a dataset from the link : [login to view URL]
Should be Documented
6 freelancers are bidding on average $57 for this job
I had a PhD in computer science. My area of expertise is data mining. I worked on many data mining and machine learning projects. I think i can help you. Please contact me.
I have done multiple projects in Machine learning, both supervised and unsupervised learning methodologies. Also did the Association rule-based market basket analysis on kaggle data.
4.6 years of hands-on experience in analyzing data, designing, interpreting and deploying machine learning algorithm using R-language, Python and SparkMLlib.