ANLY482 AY2016-17 T2 Group 2 Project Overview Methodology
| 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 3 datasets: musical data, concert data and customer profile data. The datasets consists of transaction records from both phone booking and internet booking channels as well as customer details records. 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.
| 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.
| Association Rule Mining |
Association rule mining is a rule-based method to discover interesting relations in the dataset. We will conduct analysis on the ticketing transactions to determine purchasing patterns, which are known as rules, between customers and the different ticketing channels. These rules can then be used by Kaiso as the basis for marketing strategies for their products.
| Correlation Analysis |
We will perform correlation analysis and observe the interactions of various variables, which we have identified from EDA, with the bet amount. From the correlation coefficient, we will be able to determine the strengths of these relationships and find out does these relationships correlate to the purchasing patterns for both ticketing channels.
| Dashboard |
Following the analysis that was carried out, a dashboard will be built to aid in the visualization of the findings. The dashboard will showcase the important variables and its interactions with customer purchasing behaviour. This will be an easy way for the customer engaging teams to use and understand specific behaviours of their customers.
| 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.
