# “DEEP LEARNING PROJECT for Digital marketing” starts today. I present probability of visiting the store here

At the beginning of this year,  I set up a new project of my company.  The project is called “Deep Learning project” because “Deep Learning” is used as a core calculation engine in the project. Now that I have set up the predictive system to predict customer response to a direct mailing campaign, I would like to start a sub-project called  “DEEP LEARNING PROJECT for Digital marketing”.  I think the results from the project can be applied across industries, such as healthcare, financial, retails, travels and hotels, food and beverage, entertainments and so on. First, I would like to explain how to obtain probability for each customer to visit the store in our project.

1. What is the progress of the project so far?

There are several progresses in the project.

• Developing the model to obtain the probability of visiting the store
• Developing the scoring process to assign the probability to each customer
• Implement the predictive system by using Excel as an interface

Let me explain our predictive system. We constructed the predictive system on the platform of  Microsoft Azure Machine Learning Studio. The beauty of the platform is Excel, which is used by everyone, can be used as an interface to input and output data. This is our interface of the predictive system with on-line Excel. Logistic regression in MS Azure Machine Learning is used as our predictive model.

The second row (highlighted) is the window to input customer data.

Once customer data are input, the probability for the customer to visit the store can be output. (See the red characters and number below). In this case (Sample data No.1) the customer is less likely to visit the store as Scored  Probabilities is very low (0.06)

On the other hand,  In the case (Sample data No.5) the customer is likely to visit the store as Scored Probabilities is relatively high (0.28). If you want to know how it works, could you see the video?

2. What is the next in our project?

Once we create the model and implement the predictive system, we are going to the next stage to reach more advanced topics

• More marketing cases with variety of data
• More accuracy by using many models including Deep Learning
• How to implement data-driven management

Our predictive system should be more flexible and accurate. In order to achieve that, we will perform many experiments going forward.

3. What data is used in the project?

There are several data to be used for digital marketing. I would like to use this data for our project.

When we are satisfied with the results of our predictions by this data,  next data can be used for our project.

Digital marketing is getting more important to many industries from retail to financial.   I will update the article about our project on a monthly basis. Why don’t you join us and enjoy it!  When you have your comments or opinions, please do not hesitate to send us!

If you want to receive update of the project or want to know the predictive system more, could you sing up here?

Microsoft, Excel and AZURE are either registered trademarks or trademarks of Microsoft Corporation in the United States and/or other countries.

Notice: TOSHI STATS SDN. BHD. and I do not accept any responsibility or liability for loss or damage occasioned to any person or property through using materials, instructions, methods, algorithm or ideas contained herein, or acting or refraining from acting as a result of such use. TOSHI STATS SDN. BHD. and I expressly disclaim all implied warranties, including merchantability or fitness for any particular purpose. There will be no duty on TOSHI STATS SDN. BHD. and me to correct any errors or defects in the codes and the software.

# “DEEP LEARNING PROJECT” starts now. I believe it works in digital marketing and economic analysis

As the new year starts,  I would like to set up a new project of my company.  This is beneficial not only for my company, but also readers of the article because this project will provide good examples of predictive analytics and implementation of new tools as well as platforms. The new project is called “Deep Learning project” because “Deep Learning” is used as a core calculation engine in the project.  Through the project,  I would like to create “predictive analytics environment”. Let me explain the details.

1.What is the goal of the project?

There are three goals of the project.

• Obtain knowledge and expertise of predictive analytics
• Obtain solutions for data-driven management
• Obtain basic knowledge of Deep Learning

As big data are available more and more, we need to know how to consume big data to get insight from them so that we can make better business decisions.  Predictive analytics is a key for data-driven management as it can make predictions “What comes next?” based on data. I hope you can obtain expertise of predictive analytics by reading my articles about the project. I believe it is good and important for us  as we are in the digital economy now and in future.

2.Why is “Deep Learning” used in the project?

Since the November last year, I tried “Deep Learning” many times to perform predictive analytics. I found that it is very accurate.  It is sometimes said that It requires too much time to solve problems. But in my case, I can solve many problems within 3 hours. I consider “Deep Learning” can solve the problems within a reasonable time. In the project I would like to develop the skills of tuning parameters in an effective manner as “Deep Learning” requires several parameters setting such as the number of hidden layers. I would like to focus on how number of layers, number of neurons,  activate functions, regularization, drop-out  can be set according to datasets. I think they are key to develop predictive models with good accuracy.  I have challenged MNIST hand-written digit classifications and our error rate has been improved to 1.9%. This is done by H2O, an awesome analytic tool, and MAC Air11 which is just a normal laptop PC.   I would like to set my cluster on AWS  in order to improve our error rate more. “Spark” is one of the candidates to set up a cluster. It is an open source.

3. What businesses can benefit from introducing “Deep Learning “?

“Deep Learning ” is very flexible. Therefore, it can be applied to many problems cross industries.  Healthcare, financial, retails, travels, food and beverage might be benefit from introducing “Deep Learning “.  Governments could benefit, too. In the project, I would like to focus these areas as follows.

• Digital marketing
• Economic analysis

I would like to create a database to store the data to be analyzed, first. Once it is created,  I perform predictive analytics on “Digital marketing” and “Economic analysis”.  Best practices will be shared with you to reach our goal “Obtain knowledge and expertise of predictive analytics” here.  Deep Learning is relatively new to apply both of the problems.  So I expect new insight will be obtained. For digital marketing,  I would like to focus on social media and measurement of effectiveness of digital marketing strategies.  “Natural language processing” has been developed recently at astonishing speed.  So I believe there could be a good way to analyze text data.  If you have any suggestions on predictive analytics in digital marketing,  could you let me know?  It is always welcome!

I use open source software to create an environment of predictive analytics. Therefore, it is very easy for you to create a similar environment on your system/cloud. I believe open source is a key to develop superior predictive models as everyone can participate in the project.  You do not need to pay any fee to introduce tools which are used in the project as they are open source. Ownership of the problems should be ours, rather than software vendors.  Why don’t you join us and enjoy it! If you want to receive update the project, could you sing up here?

Notice: TOSHI STATS SDN. BHD. and I do not accept any responsibility or liability for loss or damage occasioned to any person or property through using materials, instructions, methods, algorithm or ideas contained herein, or acting or refraining from acting as a result of such use. TOSHI STATS SDN. BHD. and I expressly disclaim all implied warranties, including merchantability or fitness for any particular purpose. There will be no duty on TOSHI STATS SDN. BHD. and me to correct any errors or defects in the codes and the software.

# These are small Christmas presents for you. Thanks for your support this year!

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I started the group of “big data and digital economy” in Linked in on 15th April this year. Now the participants are over 300 people!  This is beyond my initial expectation. So I would like to appreciate all of you for your support.

I prepare several small Chirstmas presents here. If you are interested in, please let me know. I will do my best!

1. Your theme of my weekly letter

As you know, I write the weekly letter “big data and digital economy” every week and publish it in Linkedin. If you are interested in specific themes,  I would like to research and write them as long as I can. Anything is OK if it is about digital economy.  Please let me know!

2.  Applications of data analysis in 2016

In 2016,  I would like to develop my applications using data analysis and make them public through the internet.  As long as data is “public”,  we can do any analysis on the data. Therefore,  if you would like to look at your own analysis based on public data,  could you let me know what you are interested in?    These are examples of applications provided by “shiny”,  very famous tool among data scientists.

http://shiny.rstudio.com/gallery/

3.   Announcement on the  project of R-programming platform

This is a project of my company in 2016.  To support for business personnel to learn R-programming,  I would like to set up the platform where participants can learn R-programming interactively with ease.  Contents are very important in order for participants to keep learning motivations. When you have specific themes which you want to learn,  could you let me know?  These themes may be included as programs in the platform going forward!    This is an introductory video of the platform.

http://www.toshistats.net/r-programming-platform/

Thanks for your support in 2015 and let us enjoy predictive analytics in 2016!