Making Data Science Mainstream

Data Science has come a long way. We are more dependent on it than ever. The application of such is quite literally endless. From simple email spam detection to complex image and video processing, the brilliant minds of the community have come up with life-changing solutions and applications. However, the biggest catch of Data Science is the time and affords poured before ending up with a legit applicable result.

A historic Comparison

Now let me take you back 15-20 years. When the internet first emerged, emails, websites and virtual messages were not mainstream. Rather enthusiastic people or the so-called IT people were able to set up everything before one could start emailing and all. There was a barrier to entry and which was the computer knowledge and internet knowledge. However, with time these processes became much much and much simpler. Today we don't need any IT support to send an email or connect to some online platform. It is seamless and effortless and very natural and instinctive that almost anyone from any age can do them.

What If?

Today we are in a similar situation for the Data Science case. We have amazing platforms and algorithms but you need some barrier to entry. You need programming knowledge, computer knowledge, and knowledge regarding statistics and data mining. What if it’s not the case. What if everyone and anyone could do something with their data in hand. What if anyone could perform some predictive analytics and obtain predictions about their data and events regardless of their knowledge about data science.

One could argue that’s impossible at this time of the Data Science era. One could argue that even if someone has all the required knowledge one still has to perform some data cleaning, feature engineering, model training, testing, and all those mumbo jumbos before one could end up with some useable results. All these mean one thing only, Time. The time constraint is the biggest.

A Welcome Solution

Well as I said Data Science has come a long way. Today I will introduce you to the Enhencer that is here to lead us to the mainstream data science usage era.

Enhencer is an Auto Machine Learning Platform. It has two modes of usage.

1. Fully Automated
2. Fully customizable drag and drop

Currently fully automated works for certain predefined cases, Churn Predictions, and Product Recommendations. Lemme show you how it works and how it is here to make this mainstream.

Every business has customers and at some point, customers choose not to continue to buy their products and services, hence the Customer Churn. We all know how long do these studies take to end up with some predictions regarding customer churns. Well here is the next-gen version;

Sign up and log in to Enhencer. Create a project and choose one of the sources to upload your data. You will need Sales, Customer, Products (Optional), Visitor (optional) data. Click explore and trust me you are done. Enhencer will do the following for you;

1. Create features; Feature Engineering
2. Train Machine Learning Models
3. Test those models and choose the best performing one
4. Provide you with the predictions like the image below

It provides you the churn probabilities for all your customers. Higher the probabilities higher the likelihood that you are about to lose that customer. It also provides the reasons present in the data for why these customers have higher probability hence a higher likelihood of churn. All the user has to do is upload the data and obtain the predictions. After that, the users/business can take actions according to their company policy to retain these customers.

The second method is also very intuitive and easy as well. What if you have some knowledge about data science but you do not know the programming behind it. Well, Enhencer got you covered as well.

After you upload the data to the platform you can choose to do all these manually with more control and precision over your methods.

See on the right side there are a bunch of modeling options and their settings. Just click on one of the algorithms and it will train that model for the data. Say you are skeptical about the decision tree then feel free to use the algorithms by simply clicking them.

Say you created all the models available then go ahead and compare them to see which one performs the best. See the picture below;

Happy with a model performance, then choose it and predict new customers with your model of choice.

You are also given the choice to do the predictions inside Enhencer or integrate with your own platform of choice and export the results and model.

It is that easy. You don't need to write any codes, don't need any extra work to set up all those data science systems. Simply log in upload your data and click around to obtain predictions. It takes only a few minutes to actually do all these steps.

That's how it should be. You don't worry about how to set every system before emailing someone, rather worry about what you should write in your email. Similarly, you should not worry about how to perform such predictive analytics rather what actions to take after you obtain the predictions and be creative with your marketing actions based on predictions. That's how Enhencer is going to take Data Science to mainstream users.