Data Warehouse, OLAP, Visualization, CART and C5.0 evaluation

Results:

Looking in the evaluation tool results it is fair to say that CART decision tree is better for number of reasons: accuracy , area under the curve(AUC) and F1 Score, along with sensitivity scores are higher. According to these scores CART DT is better in predicting the “defaulted” class, with lower probabil-ity of missqualification. Especially F1 Score, which means test accuracy is considerably higher than c5.0 algorithm (0.8242812 opposed to 0.7957746). To improve the models I have manually forced the model to split according to Gini index, but it did not make any difference to the evaluation measurements. However, pruning the tree to avoid over-fitting the data, by following cross validated error achieved the best results.

Process

1. MySQL/R Queries/Visualization
   1.1. Import and inspect
   1.2. Query R using RMySQL/DBI
   1.3. DPLYR/DBPLYR methodology with pipe operations
   1.4. Visualisation
   
2. OLAP Operations in R
   2.1. Create data
   2.2. Generate sale table
   2.3. Revenue cube / Multi-dimentional cube
   2.4. OLAP Operations
   
3. Decision Support Systems in BI
   3.1. Data import from MySQL to R Studio
   3.2. Explore and prepare the data
   • Frequency, proportion and average
   • Visualisation
   • Shuffle and re-order the provided data, then split into training and testing sets
   3.3. C5.0 algorithm decision tree model
   3.4. Decision tree model based on CART
   3.5. Improvement of current models

Example of some rendered visualizations