Machine learning and Python — why is it a good combination?

Idego Idego • Oct 04
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In the world of business that becomes more and more data-driven, it is important to stay updated on the topic of the most popular technologies. The group of languages that are most often used for machine learning includes Python. But, is it a good choice for those interested in implementing ML-based solutions in their companies?

Machine learning-based systems already partially replace people, performing tasks that previously could only be performed by humans. Multiple jobs can be automated using artificial intelligence. There are many programming languages leveraged for data science and AI development. Is Python better than any other out of that group? What is Python use cases that include machine learning? We explain why we think Python is a good choice of programming language for machine learning.

Machine learning in today’s business world — statistics on adoption and development

In the Refinitiv AI/ML Survey from 2020 we read that 72% of respondents state AI/ML is a core component of their business strategy and as much as 80% claim that they are making significant investments in AI/ML technologies/techniques. Machine learning is no longer a new phenomenon in various markets all over the world. It has been widely implemented and developed. 

Machine learning has multiple applications. According to a Statista report conducted in 2021, 57 percent of use cases of AI and ML involved improving customer experience. Acquiring new clients, increasing the loyalty of the current ones, and implementing business analytics are the most popular examples of processes supported by ML. Artificial intelligence and machine learning are also powering numerous cybersecurity solutions. 

There is no doubt that modern companies find machine learning vital for their growth. The question is which technologies are the best solution for ML development?

Machine learning using Python — why should you opt for this language? 

According to Google Trends, Python’s popularity among machine learning developers has been growing in recent years, and it remains one of the solutions that are most often chosen for machine learning. Why?

Simplicity and consistency

Understandably, developers choose to code with a programming language that is simple and easy to read. Python is concise and readable and, thus, projects implementing this technology prove to be quite reliable. That enables programmers to focus on ML-related problems rather than on the inefficiency of the language.

Extensive choice of ML libraries and frameworks 

Writing a code is a manual task that takes time. Fortunately, Python has a lot of libraries dedicated to machine learning projects and AI development. These include sci-kit-learn, Tensorflow, pylearn2, NumPy, SciPy, and many others. They are used for multiple purposes, depending on the type of project. 

Supported by many platforms and highly extensible

Python is supported by many platforms including macOS, Windows, and Linux. A system developed with it can be easily implemented on various devices with the most common operating systems. Python may also be easily integrated with components written in different languages like Java, .NET, or C/C++ libraries.

Active community 

Python has been placed fourth in the ranking of programming language popularity, created in 2020 by Stack Overflow. Handling a development project is always easier when developers are eager to share their knowledge and solutions. You probably will not have problems with finding a Python developer for your project. If you do not manage to find one with sufficient experience in machine learning, you may always decide to make use of the staff augmentation solution. 

Python use cases in machine learning

If you plan to start a machine learning project and you are wondering what kind of developers you need to look for, have a look at examples of Python applications.

Creating business insights for general business optimization

Machine learning algorithms focus on finding natural patterns in data. Such a feature allows you to gain useful business insights, which considerably facilitates the decision-making process. With predictive analytics powered by ML, you can come up with a better strategy for your organizations’ growth. 

Your ML experts could write several machine learning algorithms in Python:

  • Linear regression – it analyses continuous variables and establishes relationships between dependent and independent variables by fitting a regression line to predict the desired outcome.
  • Decision Trees – decision trees are used mainly for the classification of tasks, although they could be leveraged for solving regression problems too.
  • Logistic regression – this algorithm may be used for predictive analysis to forecast if some events are likely to occur.

Many Python machine learning algorithms could help you make more data-driven decisions for your business. 

Predictive analytics in healthcare for disease prognosis 

Patients may gain quite a lot of clinics continue to invest in machine learning and other modern technologies (like, for example, the Internet of Things). Artificial Intelligence revolutionizes the healthcare industry by improving diagnostics and helping to predict if a patient is at risk of getting sick. You just need to Google “ML-based healthcare software” and you will see numerous offers from companies like AIcure, offering machine learning-powered solutions for clinics and other healthcare institutions.

Machine learning can be applied not only in healthcare-related software but also in numerous commercial applications that analyze users’ everyday habits and instruct them on what to do to become healthier. 

Risk assessment in the finance sector

The financial market often involves considerable risks. Companies deal with hazardous investments, high expectations of the customers, cybercrime, and fraud. How can you reduce the risk? By leveraging artificial intelligence. 

First, AI is used for process automation that enables companies to save time and money. Machine learning and AI-based solutions may be used for assessing risks and the outcome of specific investments. Those technologies can be applied to ensure 24/7 observation of activity in company systems and spotting suspicious activity or data leaks.

An example of a successful online banking product using Python and machine learning is Venmo – an application for easy, online payments. As PayPal’s product, it uses fraud detection systems powered by machine learning.

See also: Machine learning in finance: how does machine learning transform financial operations?

Chatbot development for customer service improvement

More and more companies create chatbots. Such programs are rather simple and remain capable of taking a lot of tasks off your employee’s hands. A well-developed chatbot can sometimes help your customers as efficiently as a human consultant. It may answer basic questions, suggest products or services, assist the client and help them finalize the transaction. 

There are two types of chatbots:

  • declarative – focused on tasks,
  • conversational — more “smart”, “human-like”, capable of interacting with the customer.

Both those types of chatbots leverage machine learning and NLP (natural language processing), although declarative chatbots are more advanced.

Python has numerous libraries for chatbot development at its disposal, such as spaCy or ChatterBot, so if you are interested in chatbot development, you should look for a Python developer.

Recommendation systems for e-commerce

Recommendation systems are typical for e-commerce platforms. They simply observe internet users’ activity on the website and gather various information:

  • what the user bought last time,
  • what were they searching for in the shop,
  • what sort of product they viewed or added to the wish list.

Based on that and other information, the recommendation system decides which products or services the client could be interested in and then shows it to them with a discount or a special offer. LightFM is an example of such a recommendation system framework. It is a Python implementation of popular recommendation algorithms. 
See also: Interesting ideas for using AI in e-commerce

To sum up

Machine learning is a complex field of IT, but implementing ML-based solutions is not an easy task. Python has qualities that make it good for AI-related projects. If you do not have Python developers within your company, you can always outsource experienced talents by leveraging staff augmentation. Tell us about your project, and we’ll help you find the IT specialist you need.

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