Data Mining Final Project
The data mining project involve the application of data mining techniques discussed in class to one data set. The goal of the project is to go through the full data mining cycle with respect to a particular data set, including the specification of the business problem to be solved, the specification of the data mining tasks to be performed, preprocessing and transformation of the data, application of several data mining methods and the discovery of patterns, evaluation of patterns, and recommendation of specific actions with respect to relevant findings.
Project Dataset:
Please download a Bank Marketing data set (bank.csv) from http://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank.zip and import the data set into SAS Enterprise Miner (Please see Appendix A for instructions on how to download the data set and perform data importation). This data set was collected from a Portuguese bank that used its own contact-center to do direct marketing campaigns in order to motivate and attract the deposit clients. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the bank term deposit would be (“yes”) or would not be (“no”) subscribed. The data set contains 4521 instances and 17 variables (16 input variables and 1 target variable). Please read the bank-names.txt file (downloadable at http://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank.zip) for a detailed description of the data set.
Project Deliverable:
Data Mining Final Report (Submitted through the Assignment Submission Folder on CourseDen)
This is a comprehensive description of your project, which should fully describe the work done for the whole data mining analysis, not just the end results. Often, the whole data mining analysis may iterate the data mining processes several times, not just one-shot. The process may include data preparation, data exploration and preprocessing, data mining methodologies, results analysis, conclusions, lessons learned and so on. Therefore, presenting merely the SAS Enterprise Miner output report will receive a very low score for the report. You should demonstrate your work using not only textual descriptions but also detailed screen shots in your report. The project final report should include the following:
1. Cover page: your name, project name, and source of the dataset.
2. Objectives: Clear statement of objectives of the data mining project; the problem that you are investigating and summarize your goals for this project. As stated in Section 4 of the bank- names.txt file (downloadable at http://archive.ics.uci.edu/ml/machine-learning- databases/00222/bank.zip), "The classification goal is to predict if the client will subscribe a term deposit (variable y)." So, the objective of the final project is to develop models to
predict the subscription of term deposit and identify the important variables that influence the subscription of term deposit.
3. Data preparation: Discussion of the structure and characteristics of the data. After importing the data set into SAS Enterprise Miner, please set the appropriate roles (e.g., Input, Target, and etc.) and levels (e.g., Interval, Nominal, Binary, and etc.) for the variables in the data set. Please read Section 7 - Attribute Information in the bank-names.txt file (downloadable at http://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank.zip) to understand the role and level of each variable in the dataset. For example, the role of the y variable should be set to Target, and the roles of the other variables should be set to Input. The levels of the numeric variables should be set to Interval, the levels of the categorical variables should be set to Nominal, and the levels of the binary variables should be set to Binary. Please note that you must have a valid reason if you want to reject a variable. Otherwise, DO NOT reject any variable. Please provide both detailed textual descriptions and relevant screen shots of data importation and the roles and levels of the variables in the data set.
4. Data exploration and preprocessing: Discussion of the processes and results of any exploratory data analysis and data visualization performed on the data. Examination of different data preparation and transformation approaches to improve results for the given analysis tasks. What data exploration steps were performed? What are results of data exploration? What preprocessing and transformation were done to make the data amenable for data mining? Describe your reasoning behind the performed data exploration, preprocessing and transformation. Please provide both detailed textual descriptions and relevant screen shots regarding the issues of data exploration, preprocessing and transformation.
For example, you should perform data exploration to determine whether there are any unusual values, whether there is any missing data, and whether data transformation is required, and then perform certain data preprocessing and/or transformation (such as data replacement and/or filtering, data imputation, variable transformation, and etc.) when necessary. More specifically, you should examine the distributions of the variables by creating histograms for the variables in your data set (right click the “File Import” node, select “Edit Variables”, select the variable(s) you want to explore, and click the “Explore” button). Please provide both detailed textual descriptions and relevant screen shots of the histograms of the variables. According to the histograms, are there any unusual values in any variables? Do you need to change the unusual values using the replacement node or remove the unusual cases using the filter node? If you decide to use the replacement node and/or the filter node, please provide both detailed textual descriptions and relevant screen shots of data replacement and/or filtering. After that, please perform data partition. Again, please provide both detailed textual descriptions and relevant screen shots of data partition. Are there any missing values? Please provide both detailed textual descriptions and relevant screen shots to show whether there are missing values or not. Do you need to impute any missing values? Please provide both detailed textual descriptions and relevant screen shots if data imputation is done. According
to the histograms, do you find any skewed distributions? Do you need to transform any variables due to skewed distributions? Please provide both detailed textual descriptions and relevant screen shots if skewed distributions are discovered and variable transformations are done.
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