Instructions:
1. Select datasets
Find the dataset that you will use for the project. There are numerous free datasets and data repositories that you can access online and use for this project. The important thing is finding an interesting one, which can lead to good research questions. Your dataset should contain a minimum of 5,000 records after preprocessing.
2. Define the business problem
Define the business problem you want to investigate using the dataset; for example, use customers’ transaction data to predict the approval of a loan.
3. Analyze data
Use R language to analyze the dataset and solve the problem you defined earlier. Utilize at least three data mining methods to solve the problem. Two of them should be predictive methods (e.g., decision tree, naïve Bayes, SVM, neural network), and the other should be a descriptive method (cluster analysis, association rule mining).
The steps involved are:
• Pre-processing of the datasets
• Descriptive statistics and exploratory analysis
• Classification and prediction
• Cluster analysis or association rule mining
• Model evaluation
4. Analyze and interpret results
• Provide insightful observations and comments on the results of the experiments.
• Present results and recommendations in the video presentation and the final report.
Deliverables:
Page Project Description - Word:
a brief introduction with the overall idea of your project, the goals, and business value
(who cares, why it is an important problem)
• the data source(s) you would like to use
• at least three (3) different data mining techniques that will be used to solve your problem
o two predictive methods (e.g., decision tree, naïve Bayes, SVM, neural network)
§ briefly explain why you choose each method.
o AND one descriptive method (cluster analysis, association rule mining)
2) -Powerpoint Presentation Slides - About 10min in length
Problem description and the current state of the domain: Define the business problem(s) you want to investigate using the dataset. Also, briefly describe existing work or what others have done in this domain.
• Dataset description: origin, data points, variables: Your data set should contain minimally 5,000 data points after preprocessing. Describe its origin, the number of variables (at least 5), names or description of variables, and descriptive statistics for your data (e.g., mean, standard deviation, min, max, etc.)
• Data preprocessing activities and results: Describe your data preprocessing activities. Describe the data transformations made, the rationale behind them, and the results with descriptive statistics. You need to show descriptive data that shows WHY you will preprocess your data as claimed. Then briefly describe the transformations you made, the rationale behind them, and the transformation results with descriptive statistics.
• Algorithms used and rationale: Describe your data mining approach. Which algorithms did you use, which variables, and why? Show details of the algorithm, such as the parameters used and the model illustrations (equations or plots). OR Intended algorithms to be used and rationale: Describe your data mining approach. Which algorithms do you plan to use, with which variables, and why? (The algorithms can be improved in the final report.)
• Preliminary analysis and results: Show the details of your preliminary analysis and the results. The analysis is based on a smaller dataset or a subset of variables, or both. The goal is to show that your project has a good chance of being successful.
• Results and Interpretation: Show the results of your analysis. What are the explanations for these results? What conclusion, suggestion, or recommendation do you want to make?
• Evaluation: Describe your evaluation of your project. This should be more comprehensive than one simple measure (such as accuracy). You should provide a detailed evaluation of strengths and weaknesses for the datasets, subsets of data, different algorithms, etc. The entire project and its outcomes need to be evaluated rather than simply an individual algorithm.
3) -Final Project Report - Word -
Executive summary of results and findings
2. A section about the business problem, the importance of the problem and the implications of solving the problem, etc.
3. A section about the dataset: What the data is about, what the records and attributes are, what kind of preprocessing it required, etc.
4. A section for summary statistics of data.
5. One section for each model: Two models are predictive methods, one is descriptive. You need to explain each model and justify the parameters that you use.
6. Results from model executions.
7. Model evaluation and a recommendation of a better model.
8. Implications and conclusion
9. Additional screenshots, images, etc., can be provided in appendices.
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