Difference between revisions of "Red Dot Payment Methodology"
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==<div style="background: #DD597D; line-height: 0.3em; font-family:calibri; border-left: #CFCFCF solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Tools Used</strong></font></div></div>== | ==<div style="background: #DD597D; line-height: 0.3em; font-family:calibri; border-left: #CFCFCF solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Tools Used</strong></font></div></div>== | ||
| + | Software: The sponsor has given us the flexibility to use any software tools suitable for the project. As such, all analyses and modelling processes will be done using tools that we are experienced in, such as JMP Pro and SAS Enterprise Miner. Both tools allow us to streamline the data mining process in developing models. | ||
==<div style="background: #DD597D; line-height: 0.3em; font-family:calibri; border-left: #CFCFCF solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Model Building</strong></font></div></div>== | ==<div style="background: #DD597D; line-height: 0.3em; font-family:calibri; border-left: #CFCFCF solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Model Building</strong></font></div></div>== | ||
| + | We will first start off with data cleaning. As the data that we have received from RDP is well documented and relatively clean, we foresee that data clean-up will be kept to a minimum. Next, we will proceed with data exploration. Based on our preliminary exploration of the data, we plan to use the following models: | ||
| + | <br><br> | ||
| + | • Cluster Analysis: Identifying different segments of merchants and customers that were not previously defined by RDP; Data may include, but not limited to the following: Merchant name, customer IP address, IP country, reason code and description. | ||
| + | <br><br> | ||
| + | • Logistic Regression: Analyzing the dataset to identify whether there are one or more independent variables that would determine an outcome measured with a dichotomous variable (e.g.what are the factors leading to rejected or approved transaction). | ||
| + | <br><br> | ||
| + | • Model Validation and Refinement: Verifying our analysis with a different fiscal year to ensure that our predicted results do not differ significantly. If possible, we may use an independent sample t-test to ensure that the differences are statistically insignificant. | ||
| + | <br><br> | ||
| + | Ultimately, we aim to create a visual dashboard for RDP to better conceptualise the nature of their data. | ||
Revision as of 13:05, 14 January 2018
| Background | Data Source | Methodology |
|---|
Tools Used
Software: The sponsor has given us the flexibility to use any software tools suitable for the project. As such, all analyses and modelling processes will be done using tools that we are experienced in, such as JMP Pro and SAS Enterprise Miner. Both tools allow us to streamline the data mining process in developing models.
Model Building
We will first start off with data cleaning. As the data that we have received from RDP is well documented and relatively clean, we foresee that data clean-up will be kept to a minimum. Next, we will proceed with data exploration. Based on our preliminary exploration of the data, we plan to use the following models:
• Cluster Analysis: Identifying different segments of merchants and customers that were not previously defined by RDP; Data may include, but not limited to the following: Merchant name, customer IP address, IP country, reason code and description.
• Logistic Regression: Analyzing the dataset to identify whether there are one or more independent variables that would determine an outcome measured with a dichotomous variable (e.g.what are the factors leading to rejected or approved transaction).
• Model Validation and Refinement: Verifying our analysis with a different fiscal year to ensure that our predicted results do not differ significantly. If possible, we may use an independent sample t-test to ensure that the differences are statistically insignificant.
Ultimately, we aim to create a visual dashboard for RDP to better conceptualise the nature of their data.
