Difference between revisions of "ANLY482 AY2016-17 T2 Group 2 Project Overview Methodology"
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For missing values, we will determine the number of missing values. If the number is significant, we will use prediction techniques to predict these values based on the data set. Otherwise, we will remove these transactions from our analysis so that it will not affect our findings.<br> | For missing values, we will determine the number of missing values. If the number is significant, we will use prediction techniques to predict these values based on the data set. Otherwise, we will remove these transactions from our analysis so that it will not affect our findings.<br> | ||
Lastly, we will perform data normalization and transformation. Some fields in the phone purchasing dataset and internet purchasing dataset have different scales and values even though they represent the same information. Also, due to system changes in Kaiso's IT infrastructure, there are some differences in the way the data is stored and named. Therefore, we will perform data normalization and transformation to ensure that values throughout both dataset are consistent before we can perform any analysis. | Lastly, we will perform data normalization and transformation. Some fields in the phone purchasing dataset and internet purchasing dataset have different scales and values even though they represent the same information. Also, due to system changes in Kaiso's IT infrastructure, there are some differences in the way the data is stored and named. Therefore, we will perform data normalization and transformation to ensure that values throughout both dataset are consistent before we can perform any analysis. | ||
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| style="padding:0.3em; font-family:helvetica; font-size:100%; border-bottom:2px solid #626262; border-left:2px #66FF99; text-align:left;" width="20%" | <font color="#000000" size="3em"><strong>Dashboard</strong><br></font> | | style="padding:0.3em; font-family:helvetica; font-size:100%; border-bottom:2px solid #626262; border-left:2px #66FF99; text-align:left;" width="20%" | <font color="#000000" size="3em"><strong>Dashboard</strong><br></font> | ||
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| − | Following the analysis | + | Following the analysis, an analytical dashboard will be built to visualize our findings. The dashboard will display the key variables of the data and how they affect the customer purchasing behaviour. The customer engaging teams would be able to utilize the dashboard to display and better understand the differences between the customer behaviours before and after the launch of the new system. |
| + | The dashboard will use a framework that allows Singapore Pools to update their dashboard by uploading their dataset every time they have a new dataset. Design, statistics and visualization will be our main considerations when building the dashboard so that they can easily unveil the differences that they are looking for. | ||
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Revision as of 12:23, 14 January 2017
| Background | Data Source | Methodology |
|---|
Tools Used
Based on the client requirements for the project, the programming language that we will be using is Python and R. Python and R has a mature and growing ecosystem of open-source tools for mathematics and data analysis. Jupyter Notebook is the best IDE for data analytics.
Methodology
| Data Collection |
Kaiso will provide us with 5 datasets on musical and concert data. The datasets consists of transaction records from both phone booking and internet booking channels. Apart from the data provided, we will also look into collecting external data that may affect our analysis such as the dates of public holidays.
| Literature Review |
To gain more domain knowledge, we will seek to read up on research papers, articles and news related to our area of topic which is ticketing analytics. Furthermore, we aim to focus our reading on online ticketing because we will be using it as our basis when we perform our analysis. In addition, this will provide us with sufficient theoretical knowledge to conduct these analyses.
| Exploratory Data Analysis (EDA) |
In the initial stage of this project, we will examine the dataset to have a better understanding of the various aspects of the dataset. This will also help us in the next stage of data preparation by identifying outliers and anomalies. Furthermore, we can perform normalization and transformation on the data if they are not consistent. We will also use EDA to help us identify important variables for subsequent steps such as correlation analysis.
Some of the analysis which we will look at are the frequencies of transactions for account holders in relation to the different bet types and the popular time of transaction, type of transaction and amount of transaction.
| Data Preparation |
Before performing any further data analysis, the first step is to prepare the data. We will clean the data to handle outliers and missing values. In addition, we will perform data normalization and transformation on the given dataset.
For outliers, we will first determine if the values are due to human or system error. If it is due to human or system error, we can safely remove that transaction from our analysis. Otherwise, we will conduct separate analysis of these outliers values.
For missing values, we will determine the number of missing values. If the number is significant, we will use prediction techniques to predict these values based on the data set. Otherwise, we will remove these transactions from our analysis so that it will not affect our findings.
Lastly, we will perform data normalization and transformation. Some fields in the phone purchasing dataset and internet purchasing dataset have different scales and values even though they represent the same information. Also, due to system changes in Kaiso's IT infrastructure, there are some differences in the way the data is stored and named. Therefore, we will perform data normalization and transformation to ensure that values throughout both dataset are consistent before we can perform any analysis.
| Dashboard |
Following the analysis, an analytical dashboard will be built to visualize our findings. The dashboard will display the key variables of the data and how they affect the customer purchasing behaviour. The customer engaging teams would be able to utilize the dashboard to display and better understand the differences between the customer behaviours before and after the launch of the new system. The dashboard will use a framework that allows Singapore Pools to update their dashboard by uploading their dataset every time they have a new dataset. Design, statistics and visualization will be our main considerations when building the dashboard so that they can easily unveil the differences that they are looking for.
| Recommendations & Insights |
From our analysis and dashboard, we seek to assist Kaiso in understanding the characteristics of their customers. We will be proposing business strategies and recommendations to them based on the insights that we have uncovered.
