Difference between revisions of "Group08 Proposal"
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<font size = 6; color = #ffffff><span><b> Visualizing Future of Crowd Funding with Yu’e Bao</b></span></font> | <font size = 6; color = #ffffff><span><b> Visualizing Future of Crowd Funding with Yu’e Bao</b></span></font> | ||
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The mfd_day_share_interest.csv | The mfd_day_share_interest.csv | ||
The mfd_bank_shibor.csv | The mfd_bank_shibor.csv | ||
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| + | The full description of the data variables can be found here: | ||
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<h1><span lang=EN-GB>Visualization and Analysis</span></h1> | <h1><span lang=EN-GB>Visualization and Analysis</span></h1> | ||
| + | The main goal of the TIANCHI competition is to train a model to predict future cash flow of Yu’e Bao users to aid [https://www.antfin.com/index.htm?locale=en_US Ant Financial Services Group], Alibaba Group’s affiliate company operating Alipay, in its business of processing the cash inflow and outflow of its users. In contrast, our group choose to provide a alternate view of the data by implementing the below analysis and visualizations: | ||
=== Data Exploration and Visualization === | === Data Exploration and Visualization === | ||
| − | + | In this module, we aim to provide an interactive data explorer to visualize the data and in different ways. We will employ different visualization techniques, eg. treemaps, heatmaps, corrplots, to demonstrate the interaction and relationships between different combination of categorial and interval variables. | |
=== Objective 2: Survival Analysis and Visualization === | === Objective 2: Survival Analysis and Visualization === | ||
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dtwclust time series cluster.... | dtwclust time series cluster.... | ||
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| + | <h1><span lang=EN-GB>Challenges</span></h1> | ||
<h1><span lang=EN-GB>Libraries</span></h1> | <h1><span lang=EN-GB>Libraries</span></h1> | ||
Revision as of 20:27, 21 November 2018
Visualizing Future of Crowd Funding with Yu’e Bao
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Contents
40 Thieves Members
¥ Wong Yam Yip
¥ Wu Jinglong
¥ Song Chenxi
Abstract
Yu’e Bao (余额宝) is an investment product offered by Alipay (支付宝), a mobile and online payment platform established by China’s multinational conglomerate Alibaba Group. In June 2013, Alibaba Group launched Yu’e Bao, in collaboration with Tianhong Asset Management Co., Ltd., to form the first internet fund in China. Since then, Yu’e Bao has become the nation’s largest money market fund and, by Feb 2018, has US$251 billion under its management. In Chinese, Yu’e Bao represents “Leftover Treasure”. Alipay users can deposit their extra cash, for example, leftover from online shopping, into this investment product. The money will be invested via a money market fund with no minimum amount or exit charges, with interest paid on a daily basis. While major banks offer 0.35% annual interest on deposits, Yu’e Bao may offers user 6% interest with the convenience and freedom to deposit and withdraw anytime via Alipay mobile app. Thus, Yu’e Bao became extremely popular in China.
Using various data visualization methodologies, coupled with analysis of survival and time-series, this project aims to build an interactive tool on R Shiny framework, to unearth the underlying treasures of associations between Yu’e Bao’s user profile, behaviour, time and other financial factors.
Dataset
Source: Alibaba Cloud TIANCHI Competition: The Purchase and Redemption Forecasts - Challenge the Baseline. The timeframe of the dataset stretches over 14 months from 1 July 2013 to 31 Aug 2014, comprising of Yu'e Bao user information, transaction behaviour,
There are 4 table to this dataset:
- user_balance_table.csv -
- user_profile_table.csv
- mfd_day_share_interest.csv
- mfd_bank_shibor.csv
The user_balance_table.csv contains.....
The user_profile_table.csv The mfd_day_share_interest.csv The mfd_bank_shibor.csv
The full description of the data variables can be found here:
Visualization and Analysis
The main goal of the TIANCHI competition is to train a model to predict future cash flow of Yu’e Bao users to aid Ant Financial Services Group, Alibaba Group’s affiliate company operating Alipay, in its business of processing the cash inflow and outflow of its users. In contrast, our group choose to provide a alternate view of the data by implementing the below analysis and visualizations:
Data Exploration and Visualization
In this module, we aim to provide an interactive data explorer to visualize the data and in different ways. We will employ different visualization techniques, eg. treemaps, heatmaps, corrplots, to demonstrate the interaction and relationships between different combination of categorial and interval variables.
Objective 2: Survival Analysis and Visualization
Performing Survival analysis....
Objective 3: Time-Series Clustering and Visualization
dtwclust time series cluster....
Challenges
Libraries
The below R libraries will be considered for the project
- shiny
- shinydashboard
- shinyWidgets
- dashboardthemes
- tidyverse
- lubridate
- ggplot2
- plotly
- lattice
- xts
- treemap
- d3treeR
- survival
- ggfortify
- survminer
- dplyr
- TSclust
- dtwclust
- cluster
