Classification is the challenge in machine learning that involves detecting whether an object belongs to a certain category based on a previously trained model. As an aspiring data scientist, the most effective approach to improve the skills would be to practise. Personal projects are critical to your career development and help get one step closer to realising your data science ambitions. The knowledge, abilities, and confidence will improve as a result of projects. Including projects in the resume will make it much easier to land a data science job.
However, selecting data science project ideas is a difficult task. This article contains a list of data science projects using classification to help professionals practise and improve their data science skills. The classification serves a variety of purposes. To gain a practical understanding of it, one must work in real-time. Therefore, let us proceed to the classification projects that will enable us to get real-world experience.
Fake News Detection
According to a study conducted by MIT (Massachusetts Institute of Technology), fake news spreads six times quicker than legitimate news. Nowadays, with social media consuming so much of our lives, it is critical to discern fake news from legitimate news. With the advent of social media platforms, fake news is spreading at a breakneck pace. This research aims to develop a machine learning model that can distinguish between true and fraudulent news using a text classification approach.
The complete description of the project, including the source code, can be seen here.
Sample fake news detection dataset.
Gender classification
Gender classification is garnering increasing attention, as it offers detailed information about men’s and women’s social activities. Gender classification is concerned with determining an individual’s gender based on the features that distinguish masculinity from femininity. For instance, a computer system with gender identification capabilities has a wide range of applications in basic and applied research domains such as human-computer interaction, security and surveillance, demography research, business development, mobile applications, and video games. Advances in the science of gender classification have resulted in a plethora of potential applications.
Sample gender classification dataset and the project code can be found here.
Fake currency detection
Detecting counterfeit currency is a significant issue for both individuals and corporations. Counterfeiters are continually developing new ways and techniques for manufacturing counterfeit banknotes that are virtually indistinguishable from genuine currency – at the very least in terms of the human eye. Fake currency detection is a machine learning challenge that requires binary categorisation. If we have sufficient data on genuine and counterfeit banknotes, we can use it to train a model that can categorise new banknotes as genuine or counterfeit.
The project code can be found here.
Language classification
Language classification is the process of classifying related languages. Diachronically, languages are classified into language families. In other words, languages are classified based on their development and evolution across time, with languages descended from a common ancestor being classified as a single language family.
Sample language classification dataset and project code can be found here.
Customer churn prediction
Customer churn is a term that refers to the process of identifying all potential customers or clients who will discontinue their relationship with the business. It is critical for any organisation because it is used to forecast the organisation’s growth as well as future customer trends. This project aims to categorise customers based on their likelihood of remaining with the company or to terminate their relationship.
Sample customer churn prediction dataset and project code can be found here.
MNIST dataset image classification
MNIST is an acronym for Modified National Institute of Standards and Technology. It is a collection of over 60,000 training photos and 10,000 testing images of handwritten digits. This task aims to classify the image of a handwritten numeral ranging from 0 to 9. This dataset is perfect for those who are just getting started with image categorisation. This dataset is frequently referred to as the ‘hello world’ of machine learning and deep learning in terms of object recognition.
Sample MNIST dataset and project code can be found here.
Skin cancer classification
Skin cancer is one of the most prevalent types of cancer worldwide. Even when people have skin cancer symptoms, many do not seek medical attention, which is a bad indicator because skin cancer can be cured in its early stages. This is where a machine learning algorithm comes into play when it comes to skin cancer classification. The algorithm for machine learning is based on Convolutional Neural Networks (CNN).
Sample skin cancer classification dataset and project code can be found here.
Heart disease prediction
Predicting and diagnosing cardiac diseases is the most difficult task in the medical business, as it is dependent on aspects such as the physical examination, the patient’s symptoms, and signals. Body cholesterol levels, smoking habits and obesity, family history of illnesses, blood pressure, and job environment – all contribute to heart diseases. Machine learning algorithms are critical for the accurate prediction of cardiac diseases. Hence, the prediction of heart diseases uses machine learning and the logistic regression technique.
Sample heart disease prediction dataset and project code can be found here.
The list consists of various machine learning classification projects. Only via practice and interaction with machine learning tools and algorithms can one gain real-world exposure to machine learning. With the proper tools and skills, no data science endeavour is too challenging. Projects are an excellent method to hone your abilities and advance toward mastery. One can practise machine learning or data science algorithms using a variety of machine learning datasets.
