Know Concepts and Resources You Require to Develop Machine Learning Enabled Mobile Apps

Freak Sense October 16, 2019

706 9 minutes read

The term machine learning was coined by Arthur Samuel in 1959. But people kept overlooking the usefulness of machine learning until 2008 when Google launched PlayStore.

With the advent of Google PlayStore, an era of mobile apps had started and soon app owners and app development companies figured out that they need to introduce cutting edge technology to satisfy growing user demand and their high expectations.

To achieve so, they ended up opting for machine learning. Since it is a very versatile technology, app owners and app development companies have been applying it to all types of apps and derive many benefits.

Currently, almost all popular apps are working on the machine learning methodology. It enables app owners to offer personalized users experience and predict the user engagement rate.

However, machine learning is a very complex concept. To actualize it, app developers and data scientists have to take care of many parameters. The most common and complex parameters are machine learning models, machine learning algorithms and datasets.

In this blog, we are going to discuss how by putting all these parameters together, one can easily develop the machine learning-enabled mobile app.

We will study each parameter separately and conclude the blog with a very comprehensive example showing how using a machine learning algorithm and dataset, an app predicts the cyber attack.

But before all of this, let’s understand the basics of machine learning and the usefulness of it in mobile apps.

What is machine learning?

Wikipedia says “Machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without human interaction.”

This seems tangled. Isn’t is. So, let’s understand it word by word.

In the definition, if you have noticed the major two components are machine learning algorithms and statistical models. Here, the statistical model is nothing but the business logic or code which contains the datasets along with the business logic of machine learning algorithms.

Meaning, the statistical model which we can also consider as a machine learning model contains its own business logic as well as the logic of the machine learning algorithm which is responsible for processing datasets.

In other words, machine learning algorithms and datasets are training the machine learning model to cope up with real problems.

Here is a real-life example of machine learning.

In most of all supermarkets, they keep diapers and milk next to each other. Do you know why? Because the machine learning model has found out that people who buy diapers, tend to buy milk too. Here is how a machine learning model predicts such a scenario.

All supermarkets store the data of users and sales. This data is called the dataset.

Now, when a dataset is given to a machine learning model which consists of a machine learning algorithm, data gets proceeded and certain rules are made after evaluating every attribute of the dataset. These rules are generally in ‘if..then’ format, I.e if a user buys diapers, then he will buy milk too.

We have just talked about one application of machine learning. There are many more applications of machine learning.

The usefulness of machine learning in mobile apps

The following are the top reasons why app development companies are relying on machine learning technology to develop futuristic mobile apps.

1. Personalized user experience

A mobile app collects a lot of data from users. This data can be used to identify the interest of the users by applying machine learning algorithms on it.

Once the machine learning module finds out the interest of a user, it shares this data with the content delivery engine. So now content delivery engine only shows those contents to a user that he is interested in.

This is how the user’s engagement rate is taken care of.

2. Chatbots

Chatbots are revolutionizing customer service. A chatbot is nothing but a machine learning program that takes queries of the users as input, analyzes it, compares it with rules and gives the most satisfying answer to users.

Thanks to chatbots, many organizations have been witnessing exceptional growth in a number of users. And since machine learning models evolve with time and data, chatbot requires no future update and maintenance.

In fact, it automatically improves its capabilities to process the data and understand the type of user query with every incoming data.

3. Sales prediction

This is perhaps the most useful application of machine learning. The machine learning model along with the machine learning algorithm takes historic data and a few real-time data into account and figures out the possible effect of many factors on the sales. This is a very common feature, you can see it in almost all admin panels of the apps.

4. Cybersecurity

There are two facts about cyber attacks – cyber-attacks are always performed through the internet and cyber-attacks are always identified after its occurrence. Well, we can’t change the fact 1, but we can surely change the fact 2.

The machine learning module makes rules to identify the cyber attack. It then compares those rules with every incoming package and alerts the admin or drops the package if it finds any malicious package.

We will understand how a machine learning module can find genius and malicious packages and predicts the cyberattack later in this blog. For now, let me reveal the list of top apps working on machine learning.

How to apply machine learning on mobile apps?

Though applying machine learning on mobile apps is a complex operation, you can easily do it if you follow the steps. Machine learning technology is a fusion of machine learning algorithms, machine learning models and datasets. Meaning, you only need to take care of these three components.

Find the purpose of applying machine learning on a mobile app. Because purpose reveals the most suitable machine learning algorithm.
Select the most suitable machine learning algorithm which satisfies your purpose in the best possible way. For instance, if your purpose behind applying machine learning on mobile apps is classification, it is better to opt for the Naive Bayes Classifier Algorithm. And if your purpose behind applying machine learning on mobile apps is predication, opt for the Genetic Algorithm.
Once you select the algorithm, get the most relevant dataset and write the business logic of algorithm and business logic for UI and output. Here, you can use your own collected data as a dataset or you can also download verified datasets from online sources. But whatever you select, the dataset should have enough data. Because it is the fuel for the algorithm. If your selected dataset has more data, the machine learning module will make more rules.
And lastly, develop an app module which takes rules and problem statement as the input and solves the problem using the rules. Developing such kind of module for the small app is very easy. All you need to do is write a code that compares the rules with the data of the problem statement.

So, now when you know that without machine learning algorithms and datasets, you can never ever develop machine learning-enabled apps, let’s discuss top machine learning algorithms and datasets.

Top 3 most useful machine learning algorithms

Before we talk about machine learning algorithms, I would like to mention one thing about them; they all are customizable. You can customize them according to your needs.

1. Linear Regression Algorithm

Linear regression algorithm is too useful when you have to develop a module in the admin panel which predicts the effect on demand if you make a change in price.

This algorithm studies the historic data and real-time data and calculates the dependency of one attribute on other attributes. In other words, it determines the change in the value of one attribute with the change of values of other attributes.

2. K means clustering algorithm

K means clustering is the really popular classification algorithm and it is being used heavily in e-commerce sites and mobile apps.

It basically scans the product inventory and puts relevant products in relevant categories or clusters, I.e ‘Apple’ into the ‘Apple iPhone’ category and ‘apple’ into the ‘Fruits’ category.

Without k means clustering algorithm, the search engine of the e-commerce app can not identify the difference between Apple iPhone and apple fruit!

3. Naive Bayes Classifier Algorithm

This is another very useful classification algorithm. However, unlike K means clustering algorithm, it is being used to classify the screen content of the app or web page.

This algorithm understands the type of content and places it in the relevant category. If you are developing a news app or apps like Pinterest in which you need to have a module that puts images and written content in relevant categories, the Naive Bayes Classifier algorithm does the job so nicely.

Top 5 most useful and verified datasets for developing machine learning-enabled mobile apps

The Following are the verified sources of the datasets which you can employ to develop a machine learning-enabled mobile app.

1. UK government data

This is the most detailed and verified dataset from the British government itself. From their data portal, you can download open datasets of crime, education, transportation, and health.

2. Walmart sales data

If you are developing an e-commerce app or app for the grocery store, you can derive many benefits from the Walmart sales dataset. This dataset stores historic sales data of 45 Walmart stores.

3. Airbnb

For hotel booking apps, Airbnb open data justifies your need in the best possible ways. This dataset accommodates data of its properties, locations reviews, and many more.

4. QuandI

QuandI is the premium source for financial and economical data. It has the data of the world’s top hedge funds, asset managers and investment banks which is really useful to develop FinTech app.

5. NYC taxi data

If you want to develop a taxi app which works on machine learning, NYC taxi dataset is a very resourceful dataset. It stores the trip data of NYC taxis from the year 2013.

A tutorial: how to develop an app which predicts the cyber attack using machine learning?

To make this learning really rational, I will understand each concept of this project separately. So, let’s proceed.

1. Intrusion Detection App (IDA)

IDA is the app that predicts the cyber attack. It scans every incoming package, compares it with rules and alerts admin if there is any malicious package. IDA is made out of two major components – rule engine and rule comparison engine.

2. Rule engine

The rule engine contains the code of the machine learning algorithm. In our case, we have chosen a Genetic Algorithm. This Genetic Algorithm processes the dataset and makes rules. So, now let’s discuss the Genetic Algorithm and KDD Cup dataset.

3. KDD Cup Dataset

KDD Cup dataset stores the values of different attributes of internet packages and the types of attacks. The following are the attributes of the dataset.

4. Genetic algorithm

A genetic algorithm is the most useful algorithm when it comes to making rules out of data. It takes the KDD dataset as input and generates a duplicate dataset with random values.

This duplicate dataset is now compared with the KDD dataset and based on the availability of the same values in both datasets, it calculates the fitness function of each row of the KDD dataset. (Fitness function is nothing but a value which shows how near a number is to the optimal solution.)

Once the genetic algorithm gets the fitness function of all rows, it discards the rows which are having the value of fitness function below a set limit. The rest of the rows undergo two operations – crossover and mutation. In the crossover, the algorithm interchanges the values of two rows and in mutation, it interchanges the values of one single row.

Then, it takes the output of the mutation operation as the input for the second round. In the second round also, it creates a duplicate dataset, compares it with the KDD Cup dataset, finds the fitness function and does crossover & mutation.

In fact, the algorithm keeps doing this until it gets only rows with the highest ever fitness function. These rows are considered as the solution to the problem.

The values of each attribute of each row are now converted into rules and rules are generally in ‘if …then’ format, I.e if ‘duration’ = 3, ‘Protocol type’= 2, ‘service’= 4 ….. then ‘attack type’= Dos.

Genetic algorithm shares these rules with rule comparison engine which compares the attributes of all incoming packages with rules and if it finds any unusual similarity between rules and incoming packages, it alerts the admin with the attack type details.