Update README.md

Author: nikmuhammadnaim

DSC721 - Enterprise Data Analytics/README.md

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-# DSC721 - Enterprise Data Analytics
+# DSC721 - Enterprise Data Analytics
+
+## Introduction
+
+This project was carried out using RapidMiner software. The work can be replicated in RapidMiner using the files above.
+
+Effort to replicate the work into R is in progress.
+
+## Objectives
+  
+1. To correctly predict Taiwanese clients' credit card default payment for the month October 2005.
+
+2. To compare the performance of single machine learning models with an ensemble model. 
+    * Three single learners that will be used are:
+      1. Naïve Bayes
+      2. Decision Tree
+      3. Deep Learning
+        
+    * The ensemble model will use the majority voting technique of the three single learners.
+    
+## Results
+
+Decision Tree have the best performance out of the four models. It has the highest accuracy and recall. 
+
+| Machine Learning Model  | Accuracy | Precision | Recall |
+| ----------------------- |:--------:| --------- | ------ |
+| Decision Tree           | 83.34%   | 84.85%    | 96.09% |
+| Ensemble (voting)       | 83.29%   | 85.23%    | 95.41% |
+| Deep Learning           | 83.26%   | 85.48%    | 94.96% |
+| Naïve Bayes             | 65.29%   | 88.30%    | 64.68% |
+
+## Team Member
+
+* Muhammad Arief Roslan
+* Nur Faiqah Zulkefli
+
+## Lecturer
+
+[Dr. Ruhaila Maskat](https://fskm.uitm.edu.my/v4/index.php?option=com_content&view=article&id=178&catid=45&Itemid=227)
+
+## Dataset
+
+Data was made available by  I-Cheng Yeh and retrieved from UCI Machine Learning website.
+
+* Yeh, I. C., & Lien, C. H. (2009). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications, 36(2), 2473-2480.
+
+* Dua, D. and Karra Taniskidou, E. (2017). [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml). Irvine, CA: University of California, School of Information and Computer Science.