Getting Started on Predictive Analytics

简介: You won’t be able to see the future with predictive analytics, but you will be able to forecast likely trends and patterns.

6e06294dbfcb4956edf52e3310e5b481fd6d07f0_jpeg

You won’t be able to see the future with predictive analytics, but you will be able to forecast likely trends and patterns. Essentially, it’s similar to weather forecasting, where the basic premise is to use past data to guide our thoughts for future outcomes. Here are three ways to begin scratching the surface of predictive analytics.





  • Determine your objectives - As with any project or major undertaking, you must have a clear picture in your mind of what you want to achieve. The nature of predictive analytics can be very open, and as a result, possibilities of what you can achieve may be extensive. Avoid jumping straight into rafts of data. Instead, plot down your overall desired outcome in natural language. You’ll then be able to work out how that objective gets measured with which pieces of data.



  • Structure your data - Any form of data analysis must begin with organizing data. With data coming from all sorts of sources and in different formats, it’s impossible to begin without having everything structured first. You will want to try to ensure that you have consistent parameters and answer options. This will give you the platform upon which to proceed with analysis.


  • Experiment and mine - Statistical analysis will help as you mine the data. This is the time to be a bit more creative with how you view data. There are going to be so many parameters and variables that patterns will reveal themselves as you begin to pair up different ones against each other. By experimenting with the relationships, you will discover new causes and effects that will form a part of your forecasting.


Giving yourself an edge in the marketplace


By following the steps above, you will be able to start using predictive analytics to forecast important developments, such as changes in the performance of your competitors, predicting risk or the changing preferences of your clients.


For example, U.S. retailer Macy's is using predictive analytics to better target consumers and develop more tailored digital marketing campaigns. After developing 20 predictive models and deploying better targeted e-mails, the retailer saw an 8-12 percent increase in online sales.


With the explosion of data available to most businesses today, there is little excuse not to leverage that data to power predictive insights that can help your business survive and even thrive in an increasingly demanding and competitive marketplace.

目录
相关文章
|
存储 Cloud Native NoSQL
【Paper Reading】Cloud-Native Transactions and Analytics in SingleStore
HTAP & 云原生是如今数据库技术演进的两大热点方向。HTAP 代表既有传统的 HANA Delta RowStore+Main ColumnStore,Oracle In-MemoryColumnStore 等方案,也有像 TiDB,Snowflake Unistore这样新的技术架构;云原生代表则是以 S3 为低成本主存的 Snowflake,Redshift RA3,提供灵活弹性和Serverless 能力。SingleStore 则是首次把两者结合起来,基于计算存储分离的云原生架构,用一份存储提供低成本高性能的 HTAP 能力。
【Paper Reading】Cloud-Native Transactions and Analytics in SingleStore
|
机器学习/深度学习 存储 数据采集
DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled论文解读
我们提出了一个事件抽取框架,目的是从文档级财经新闻中抽取事件和事件提及。到目前为止,基于监督学习范式的方法在公共数据集中获得了最高的性能(如ACE 2005、KBP 2015)。这些方法严重依赖于人工标注的训练数据。
186 0
|
机器学习/深度学习 存储 传感器
Automated defect inspection system for metal surfaces based on deep learning and data augmentation
简述:卷积变分自动编码器(CVAE)生成特定的图像,再使用基于深度CNN的缺陷分类算法进行分类。在生成足够的数据来训练基于深度学习的分类模型之后,使用生成的数据来训练分类模型。
181 0
《Fighting Cybercrime A Joint Task Force of Real-Time Data and Human Analytics》电子版地址
Fighting Cybercrime: A Joint Task Force of Real-Time Data and Human Analytics
99 0
《Fighting Cybercrime A Joint Task Force of Real-Time Data and Human Analytics》电子版地址
|
机器学习/深度学习 数据采集 人工智能
Re10:读论文 Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous gr
Re10:读论文 Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous gr
Re10:读论文 Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous gr
|
SQL 存储 算法
The MemSQL Query Optimizer: A modern optimizer for real-time analytics in a distributed database
今天我们要介绍的MemSQL就采用这样一种新的形态(Oracle也变为了这种方式 ):即在做transformation时,要基于cost确定其是否可应用。 当然,本篇paper不止讲解了CBQT,还包括一些MemSQL优化器其他方面的介绍,包括一个有意思的heurstic based bushy join的方案。
425 0
The MemSQL Query Optimizer: A modern optimizer for real-time analytics in a distributed database
|
机器学习/深度学习 数据可视化 数据挖掘
Paper:《Graph Neural Networks: A Review of Methods and Applications》解读(一)
Paper:《Graph Neural Networks: A Review of Methods and Applications》
|
机器学习/深度学习 人工智能 编解码
Paper:《Graph Neural Networks: A Review of Methods and Applications》解读(二)
Paper:《Graph Neural Networks: A Review of Methods and Applications》
|
机器学习/深度学习 新零售 自然语言处理
KDD 2020 <A Dual Heterogeneous Graph Attention Network to Improve Long-Tail Performance for Shop Search in E-Commerce> 论文解读
店铺搜索是淘宝搜索的一个组成部分,目前淘宝有近千万的店铺,7日活跃店铺也达到百万级别。店铺搜索场景拥有日均千万级别UV,引导上亿的GVM。
KDD 2020 <A Dual Heterogeneous Graph Attention Network to Improve Long-Tail Performance for Shop Search in E-Commerce> 论文解读
|
JavaScript 前端开发
Guidelines for Function Compute Development - Troubleshoot Timeout Issues
Endless codes and endless bugs When you write code, you may inadvertently introduce some hidden bugs, even if you test a large proportion of the codes to the maximum extent possible.
1661 0