数据挖掘代写 | Hidden Patterns And Relationships In Big Data Datasets
Data mining is used to reveal hard to see and hidden patterns and relationships in Big
Data datasets. Data mining helps to classify data for further examination or create
models to predict outcomes for a different set of data. As data miners, you should be able
to explain how the code used to mine the data is functioning and be able to analyze and
interpret the results of the mining. This allows you to summarize and clarify the results for
In this assignment, you will revisit the IceCubed dataset from the Week 2 Assignment.
This time we will see if using SVM will improve our models from that week. You will be
running three models this week, multiple logistic regression (all variables), forward or
backward selection and SVM.
Your report should include the following:
• Model Composition: in this section provide the context around building each
of the models and the advantages/disadvantages of each.
• Analysis: Based on the output, analyze the data and the relationships
revealed about the variables of interest. Explains the insights provided by the
output. Use visualizations to support your analysis. You should also
benchmark each of the models against each other using different metrics.
• Interpretation and Recommendations: Interpret the results of your analysis
and explain what the results mean for the data owner. Provide
recommendations for actions to be taken based on your interpretation.
Support those with the data. Ultimately have a final decision for your boss on
next steps (what variables need to be adjusted and what model should be