Top 15 Machine Learning Projects for Beginners In 2024

When it comes to the domain of computer science and IT, taking up projects regularly is one of the most beneficial ways of acquiring hands-on skills in your specific domain of interest. Similarly, whether you are a student of technology or an enthusiast of machine learning wanting to upskill yourself in this domain, taking up projects that put your knowledge and skills to practice can pave the way for working knowledge and expertise in ML technologies.

However, selecting the right topic for a project for yourself, whether for course completion or portfolio building, is a complex decision to make. If you are someone faced with a similar problem, then in this blog, we bring to you 15 popular project ideas that expose you to various nuances of machine learning in practice along with important information such as source codes.

What is Machine Learning (ML)?

The domain of machine learning is subsumed under that of artificial intelligence, and it revolves around the training of AI models to find accurate solutions to problems. This is done by using statistical models and complex algorithms that analyze vast training datasets fed into the model to identify trends and patterns and utilize them in future decision-making and problem-solving.

Machine learning approaches are commonly categorized into three categories–supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves the active provision of structured and labeled datasets into an ML model by the developer, while unsupervised learning includes autonomous learning by the ML model through analysis of datasets generated in real-time as users access the software. Unsupervised learning requires little to no intervention by the developers upon deployment of the model, unlike supervised learning models which require systematic feeding of data into the system.

Reinforcement learning mimics human learning wherein the accuracy and success of a decision/solution provided by the model are determined not by analyzing datasets fed into the system but by evaluating the outcome it produces.

Machine learning finds usage in a variety of industries and domains, including but not limited to:

• Healthcare and Health Management
• Medical Imaging and Predictive Diagnostics
• Financial and BFSI Sectors
• Fraud Detection and Spam Detection
• Email Spam Filtering
• Virtual Assistance
• Customer Services
• Marketing, Trading & Sales, etc.

Thus, the domain of machine learning is being increasingly used by enterprises and companies to strengthen their AI operations and models, thus advancing their functioning and efficiency.

Why are Projects in Machine Learning Important?

One might wonder, why take up machine learning projects at all? Machine learning projects are quite useful in honing your hands-on skills in managing and developing ML models, providing meaningful exposure to the programming languages, models, algorithms, etc. involved in the domain. Due to their instrumentality in enabling students to build their competency in ML, many courses have mandatory projects that are required to be completed for evaluative purposes.

 Here are some of the promising reasons for you to consider taking up machine learning projects:

• Such projects are often mandatory to complete as part of the course curriculum of certain IT and computer science courses you might be pursuing.
• Taking up projects and developing your very own ML models is beneficial for building your portfolio and gives you an edge over competitors in establishing your credibility.
• Such projects help you to build your working knowledge and skills in the domain of machine learning, enabling you to increase your expertise in the domain.
• Taking up your machine learning projects and exploring unique ideas for them enhances your creativity and innovation–two greatly desirable competencies among technology professionals in the industry.
• They can be of much benefit to your career prospects as well as help you learn by trial and error, allowing for greater adaptability in diverse ML-related roles.
• Lastly, taking up projects in ML regularly allows you to simultaneously explore your areas of interest in addition to building your skills in the domain, thus, providing a direction for your future career endeavors.

Additionally, pursuing ML projects, especially if you are collaborating with an expert or a peer on the project, can provide scope for exposure and networking with professional experts in the field, providing greater connectivity for future professional endeavors.

Top 15 Projects in Machine Learning

Here, we have elaborated upon a few of the most popularly taken-up projects in machine learning suitable to the needs of beginners, experts, and intermediaries in ML.

These project ideas can be explored independently for portfolio-building, upskilling, and so on as well as taken up as part of curriculum-mandated projects. Continue reading to explore more about the projects.

1) Project Ideas: Beginner Level

At the beginner level, one can focus on projects related to prediction applications, as they allow for exposure to the basics of AI and machine learning development, including supervised and unsupervised learning, regression, classification, etc. These are suited to the skill needs of beginners as they provide a foundational working knowledge of machine learning.

(i) Health Prediction Application

A health prediction application is a project in which one has to train an ML model to analyze the health data of clients/patients (usually as a CSV dataset) and arrive at a prediction of their likelihood of health outcomes and predictions of developing health conditions and illnesses in the future. The model is trained to analyze several parameters from the dataset including gender, age, genetic history, comorbidities, etc. to predict their health conditions and likelihood of diseases. This model can be developed in Python.

More details about the same have been provided below :

• Language: Python
• Dataset: CSV File
• Source Code: Disease & Health Prediction Application

(ii) Stock Price Predictor Application 

This project deals with the development of an ML model that can analyze vast datasets of market data and shareholding to predict whether a particular trade is likely to be profitable or not. This project includes training the model based on datasets including market data, market stability, product/service data, etc. Commonly used Python libraries for this project include Matplotlib, Numpy, Seaborn, etc. This is an easy project to undertake with easily accessible detailed datasets.

More details about this project have been provided below :

• Language: Python
• Dataset: CSV File
• Source Code: Stock Price Predictor Application

(iii) Sales Forecast Application

A sales forecast application uses an ML algorithm that can analyze a dataset based on various factors such as days of the week, market demand of a product, price of a product, past sales records, etc. to forecast the future sale of a product from an enterprise. This project includes supervised learning using linear regression models and can be easily prepared in Python.

More details about the dataset type and source code have been provided below :

• Language: Python
• Dataset: CSV File
• Source Code: Sales Forecast Application Source Code

(iv) Cryptocurrency Price Prediction Application 

Machine learning projects can also be created to predict the prices of cryptocurrency in the future. This project includes feeding the model with datasets about historical data about the prices of the cryptocurrency through the years along with pertinent data related to the market. Commonly used libraries for this project include Numpy, Matplotlib, Pandas, etc. Python is used as a coding language for the development of the model.

Find more details about a cryptocurrency price prediction model along with the source code (specifically for Dogecoin) below :

• Language: Python
• Dataset: CSV File
• Source Code: Cryptocurrency Price Prediction Application

(v) Sentiment Analysis in Twitter

Sentiment analysis models make use of natural language processing technologies as well as computations to assess the overall sentiment of a given textual data in social media platforms like Twitter, usually in the form of “negative”, “positive” and “neutral” analysis. This project requires an NLTK dataset and can be created in Python. It requires the installation of Tweepy, which is the Python client for the official Twitter API.

Find more relevant details for this project given below :

• Language: Python
• Dataset: NLTK Library
• Source Code: Sentiment Analysis Software in Twitter

2) Project Ideas: Intermediate Difficulty

These projects are well-suited for students and learners who have already been exposed to machine learning principles and hands-on development in some respect and are familiar with the basic tools, programming languages, and the development of algorithms needed to create a useful ML project. Students of computer science, final-year students, and technology enthusiasts skilled in programming can consider taking these courses.

(i) Email Spam Detection Program

This is one of the most useful and widely used applications of machine learning, as virtually all email service providers make use of spam detectors and filters to prevent the spamming of user inboxes with unnecessary and often promotional emails. Thus, this is a useful project to build one’s skills in. An e-mail spam detection program makes use of text classification to identify and label emails as spam or “ham” (not spam) and accordingly filter mails displayed in one’s inbox. This project can be prepared using the TensorFlow API.

Find more details about the program given below :

• Language: Python
• Dataset: TensorFlow API
• Source Code: email Spam Detection Software

(ii) Loan Approval Prediction Program

A loan approval prediction program predicts the likelihood of approval of one’s loan application based on the analysis of vast user data about their age, credit score, income, number of dependents, approval of loans consequent payment of loans, and so on. Commonly used libraries for developing this model include Matplotlib, Seaborn, Pandas, etc. The project can be undertaken in Python.

More details along with the source code for preparing this ML project have been provided below :

• Language: Python

• Dataset: CSV File 

• Source Code: Loan Approval Prediction Program

(iii) Credit Card Fraud Detection Software

A credit card fraud detection model is one of the most important applications of machine learning in the banking and finance sectors. This project involves the creation of a model that can effectively detect potential credit card fraud based on the transaction histories of customers as well as patterns of buying. This model is more suited for learners having some proficiency in ML because it needs to address certain key challenges including being able to process vast data quickly and accurately, identifying fraud in unbalanced data, combating the adaptive techniques used by scammers to prevent detection, and so on.

More details about the project have been provided below :

• Language: Python
• Dataset: CSV File
• Source Code: Credit Card Fraud Detection Software

(iv) Kaggle Breast Cancer Wisconsin Diagnosis using Logistic Regression

While simplistic in its analysis, a Kaggle breast cancer diagnosis model is one of the significant machine learning projects one can consider undertaking. This project involves creating an ML model that utilizes logistic regression to predict whether a patient’s tumor is benign or malignant. The project can be developed in Python with a CSV file containing patient data for training.

Explore more details about the project along with the source code given below :

• Language: Python
• Dataset: CSV File
• Source Code: Kaggle Breast Cancer Wisconsin Diagnosis Program

(v) Facial Recognition Application

 A facial recognition software analyses multiple features of human faces including dimensions, complexion, colors of eyes, hair, hair color, etc. to identify them based on datasets consisting of users’ facial data. Common uses of facial recognition software include biometric surveillance and security, counting several faces, etc. While facial recognition can include the exact matching of facial features with the identity of users, one can also undertake projects that include models for only the calculation of the number of faces in images or real-time in videos.

Find more details about the creation of such projects along with a source code given below :

• Language: Python
• Dataset: AFW (Annotated Faces in the Wild)
• Source Code: Facial Recognition Application

3) Project Ideas: Advanced Level

Mentioned below are a few of the advanced ML projects that include multiple components and processes of development. These projects are appropriate for students and professionals well-acquainted with machine learning basics and wishing to put together such skills to develop a holistic and advanced project.

Find more details about the projects given below :

(i) License Plate Recognition with OpenCV and Tesseract OCR

License plate recognition software is used for surveillance and traffic regulation and maintenance by states and traffic departments. This project makes use of Optical Character Recognition or OCR technologies to identify the textual content in license plates. An Open CV file is suitable for the creation of this project in Python.

Find more details about the same given below :

• Language: Python
• Dataset: Open CV File
• Source Code: License Plate Recognition with OpenCV and Tesseract OCR

(ii) Autocorrect Feature Using NLP

Dependent upon the principles and technologies of natural language processing (NLP), an autocorrect tool involves machine learning to identify incorrect spellings and provide suggestions for corrections for the same. This software is used in virtually all domains that include textual communication–social media platforms, writing applications, messaging applications, and so on. The feature requires the use of an NLTK file as a detailed dataset and can be created in Python.

The source code and related details of the project have been provided below :

• Language: Python
• Dataset: NLTK File
• Source Code: Source Code: Autocorrect Feature Using NLP

(iii) Hate Speech Detection Software/Algorithm

This is an important application of machine learning, as a large number of communication platforms such as social media applications, messaging applications, and customer interaction platforms make use of hate speech detection software to remove such threatening content from their platforms and uphold their cyber-civility policies to safeguard users’ safety. This model includes the use of NLP features and NLTK datasets to identify potentially hateful, aggressive, offensive, or discriminatory content on a platform and consequently remove the same if policy guidelines are violated by the content.

More details along with the source code of this project have been provided below :

• Language: Python
• Dataset: NLTK File
• Source Code: Hate Speech Detection Software/Algorithm

(iv) Intrusion Detection System Using Machine Learning Algorithms

Utilized majorly for cybersecurity and data privacy, intrusion detection software aims to identify potential unauthorized connections and fraud connections to an information system to block them and safeguard a computer network and user data. Intrusive connections can be identified as unauthorized users trying to connect, service deniers, etc. An HTML dataset in Python can be effectively used to create this intrusion detection system with ease.

A source code for the same has been provided below :

• Language: Python
• Dataset: HTML Dataset 
• Source Code: Intrusion Detection System

(v) Personalised Fashion Recommendations Software (H&M)

A personalized fashion recommendation and sales prediction software can be prepared by integrating ML models that allow the application to provide personalized suggestions for fashion to the users and also predict their likelihood of buying specific products in a given period. This model depends on training datasets of user information including their preferences, buying history on the platform, demographic characteristics, and so on. CSV files of user data along with images can be used to train this ML model.

Further details about the project are provided herein :

• Language: Python
• Dataset: CSV Files and Images
• Source Code: Personalised Fashion Recommendations Software

Tips for Taking Up & Managing Machine Learning Projects

While the development of an ML project is certainly a challenging and engaging endeavor, the selection of a project topic is a tough decision to make. Some aspects, such as the precise requirements and expectations from the project (especially if it is a course-mandated project), one’s own interests and strength areas, proficiency needed to complete the project, alignment with career goals, etc. are important aspects to consider.

To guide the decision, herein we have mentioned a few tips that might help select the right project topic for yourself. 

1) Identify Your Major Area of Interest: Knowing one’s areas of interest within ML can help in narrowing down the project topics that you want to undertake. Taking up a project that aligns with your interests ensures a greater likelihood of completion as well as builds your skills in that domain.

2) Narrow Your Interest Area to a Specific Topic: Among domains interesting to you, the next major task is to identify potential topics within the domain of interest that can be viably taken up by you. Identifying topics within your broader area of interest can be based upon consideration of aspects like the difficulty level of the project topics, available time for completion, etc.

3) Conduct a Review of Literature and Research Your Topic: The identification of potential topics in machine learning as well as the finalisation of the topic can be helped much by a thorough review of available literature. Researching your topic of interest, and identifying potentially helpful sources of reference for model development as well as the conducted projects in that area help you to strengthen your project and prevent the mere replication of an idea already explored in the industry.

4) Plan & Design your Overall Project: Ample time and efforts spent on the overall design of a project can set an excellent and successful project apart. Ensure that you devote considerable time to preliminary planning and designing the overall project before diving deep into developing it.

5) Test the Project: Testing the project on a pilot basis can help a lot in identifying potential bugs and pragmatic difficulties faced in using it at a larger level. Testing should be an essential part of your project design.

6) Evaluate the Project’s Performance & Functionality:  Make sure you evaluate the utility, performance, and actual functionality of the ML project you create. Continuous evaluation throughout the various stages of development can help to ensure the success of your project. Evaluating the potential biases in your dataset is also highly crucial to ensure the accuracy of predictions made by your ML model.

7) Get Potential User Reviews & Feedback from Subject Matter Experts: You can involve subject matter experts and peers in the evaluation of your project as such feedback can prove highly insightful in increasing the accuracy and utility of your project. It can help in the identification of weak areas that might go unnoticed by you as a developer, especially if you are a beginner in ML. You can also rely on user reviews and feedback from your pilot test of the model.

Keeping such tips in mind, one can select ML projects that can provide a significant boost to their careers and journey of skill-building in the domain of machine learning.

Conclusion

There are numerous projects that you can consider taking up as a student of machine learning or as a professional looking to upskill themselves in machine learning. Through such projects in machine learning, one can upskill and hone their existing competencies to escalate their career in this domain. Such projects enable hands-on skill building as well as building a strong portfolio for career prospects and a bright career scope.

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