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Top 10 Machine Learning Projects for Beginners in 2024

23rd Dec, 2023
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    Top 10 Machine Learning Projects for Beginners in 2024

    In the world of machine learning, where data-driven solutions have the power to transform industries and empower individuals, if you're new to this exciting field and eager to embark on your machine-learning journey, you're in the right place. In this blog, we'll explore a curated selection of beginner-friendly machine learning projects that will not only help you grasp the fundamentals but also inspire your passion for this ever-evolving technology. There is probably no one who hasn’t heard of Artificial IntelligenceAI was once compared to the discovery of fire, a discovery that changed the human race forever. Similar to fire, AI has permeated every part of our lives and is changing it for the better.  

    Machine learning is a branch of AI; it's all about creating an algorithm, analyzing data, learning from data, processing data, and identifying and applying patterns to data with minimal intervention by humans.

    What is Machine Learning, and why are ML projects interesting? 

    Moving towards the definition of Machine Learning, “Machine Learning is the application or branch of Artificial Intelligence (AI) that is the ability to learn from data, train data, identify patterns, and improve overall user experience. It focuses on developing the computer program which can easily analyze the data.” 

    Machine learning and its projects are fascinating because they involve real-time data analysis, management, and learning data. It helps to solve real-time and human-related problems. It can be said that Machine Learning program is a program that writes another program and then writes another; this process is continuous and never-ending. 

    As a programmer, you are probably fascinated by its wide category of problem statements and state of the art solutions. It involves image classification, image detection, image recognition, voice recognition, and many other study fields. While dealing with the problem statement, you need to understand the problem, recognize its algorithm, develop the most suitable set of techniques, and apply it to large sets of data with different problems with a little bit of tweaking 

    When you go for a more practical approach, everything out there becomes more interesting and easier to learnAs a beginner, you should start with some basic projects so that you can brush up on your skills and get in-depth knowledge of the required algorithm.

    Key Features of Machine Learning Project

    Here are the key features that all machine learning projects should have:

    • It exposes you to a large variety of real-world problems of the business. 
    • It helps you perform automated data visualization. 
    • It provides the best automation tools for processing. 
    • It provides user engagement and better relationships. 
    • It provides accurate and precise data analytics. 
    • More business intelligence and exposure. 
    • The easiest way to predict for decision-making and business insights.   

    Top 10 Machine Learning Projects Ideas for Beginners in 2024

    Any ML project should be interesting, true-to-life, and meaningful. When you try to understand the basics of any technology, you must work on it hands-on to understand and take a deep dive into the subject. Here, we will try to cover machine learning projects, which can be a great starting point for you to learn about machine learning or which can be added to your portfolio of projects to make your resume stand out.  

    1. Sales Forecasting with Walmart

    Walmart is an American multinational retail corporation with hypermarkets, discount department stores, and grocery stores in its chainKaggle organizes a challenge for sales forecasting in which aspiring data scientists can participate. You can find the sample data set on GitHub or from their official site. 

    Sales forecasting data increases day by day and minute by minute, and this is a good place to apply machine learning and data analysis. It is very helpful in practicing data visualization, analysis, and exploratory analysis. 

    Data sources you can refer to: 

    • Walmart sales forecasting: the dataset available from the “Walmart store sales forecasting” project that was available on Kaggle. It contains weekly sales data for more than 40+ stores and 99 departments over a 3-year period. 
    • Kaggle Walmart sales forecasting: Kaggle organizes a challenge where you can participate and help them to organize their dataset and files and apply machine learning to the required dataset. 

    2. Stock price predictions

    The stock market exchange is a candy shop for data scientists who are interested in the finance sector. There are numerous data sets that you can choose from and perform analysis on. 

    You can apply predictions on the prices, fundamentals, value investing, and future forecasting and arbitraging. 

    Data sources you can refer to: 

    • Financial and economic data: Here, you can find the free as well as premium data for financial and economic analyses. It provides bulk amounts of data from the federal reserve. 
    • Data from US Companies: It has 5+ years of data from the US companies, which contain more than 5000+ records and value edit services. 

    3. Human Activity Recognition with Smartphones Data

    It is a classification problem where the sequence of accelerometer data has been recorded by the specialized harnesses or smart phones into known well-defined movements. For more information on the project and to develop more insights, you can visit the tutorial and then move onto the project. Human Activity Recognition is where you find what the person is doing and trace their activity and perform analysis and exploration of the data set. 

    The data source you can refer to: 

    • Human Activity recognition: This will provide you with insights into affordable wearable equipment and portable computing devices. It includes the UCI machine learning repository and dataset. 
    • Kaggle Human Activity Recognition: This contains the record of 30+ study participants, their daily activities and living standards. 

    4. Investigation on Enron data 

    It was the largest corporate meltdown in history. In the year 2000, they were called out for fraud. But luckily, for us, their database, which contains 500 thousand emails between employee, senior executive, and customers is still availableData scientists have been using that data for education and research purposes for years. 

    The data source you can use: 

    • Enron Email dataset: This set of data was managed and prepared by the organization known as Cognitive Assistant that Learns and Organizes (CALO), which contains 150 users’ data, maintained in different folders. 
    • An off-balance sheet of Enron: it is an assets liability that does not appear on the company’s balance sheet. This sheet contains typically those datasets which do not contain any direct obligation relating to most operating and significant values. 

    5. Chatbot Intents Dataset

    this is a basic machine learning project which you can undertake to develop a better understanding of the libraries and natural language processing. It contains the JSON file structure, which will respond to your chat with a defined pattern and syntax. 

    This is a useful machine learning project for beginners with source code in Python.   

    The data source you can refer to: 

    • JSON Dataset link:this JSON dataset file contains tags like goodbye, greeting, good morning, pharmacy search, and nearby hospital search, etc. 
    • Python source code: Chatbots help in business organizations and also in customer communication. Chatbots come under Natural language Processing, which involves Natural Language Understanding and Natural Language Generation. 

    6. Flickr 30K Dataset

    Flickr is a platform that provides an opportunity to upload, organize, and share your photos and videos. Flickr contains a 30k dataset; it has become a standard benchmark for sentence-based image processing. 

    It contains about 158k captions and 244k coreference chains. This is used to create a more accurate model. 

    The data source you can refer to: 

    • Flickr image source by Kaggle: this paper contains records from Flickr, which has 30k image dataset, captions, and co-references 

    7. Emojify

    It helps in creating your emoji with the help of Python. This performs a mapping operation between facial expressions and emojis. You are required to create a neural network to recognize the facial expression and map it down into the expression. 

    An emoji or avatar indicates a non-verbal cue; these cues are increasing as a part of our chatting and messaging world. It is used to describe your emotion, behavior and mood in your conversation.  

    The data source you can refer to:  

    • Emojify dataset: This dataset contains less amount of classification, which is the best fit for a beginner; try it if you are at the initial stage of Machine Learning, then move on to the next dataset.  
    • ML Project by Kaggle: it is used to solve the sentimental classification probleand has loads of data. You can visit Kaggle to work on the challenge.  

    8. Mall customer dataset

    The mall customer dataset contains all the entries about the customers visiting the mall, their names, ages, gender, recommendations, a product they buy, issues they face etc. Using the data's different characteristics, we can gain insights into the data and divide the data into different attributes and group them into different groups, based on their behavior. 

    The data source you can refer to: 

    • Customer dataset: this datasheet contains several sets of data and metadata you can go through to understand more about the dataset. 
    • Source code: trying to do the project in real-timeVisit the source code for all your references. The code is segmented according to the customer with the Machine Learning model's help.   

    9. Boston Housing

    The most famous and used dataset is the Boston housing dataset; many machine learning tutorials take this dataset as an example dataset. This is used for pattern recognition; it contains 500+ observations with 14 attributes or distribution variables. 

    The common logic behind this project is to predict the new house's cost using the regression model of machine learning. 

    The data source you can refer to: 

    • Boston Housing Dataset: The dataset is the natural dataset, which is being collected by the US service and housing management system. 

    10. MNIST Digit Classification

    MNIST stands for Modified National Institute of Standards and Technology; it is the dataset of 60+ thousand grayscale images of handwriting. In this project, you'll be able to recognize the handwriting digits using simple Python and machine learning algorithms. This is very useful in computer vision. 

    As this dataset contains flat and relational data, this data is the best fit for beginners to learn more about the algorithmic strategy.  

    The data source you can refer to: 

    • Digital handwriting recognition: here, you can easily find the prerequisites for project development. The Machine Learning model is trained using a Convolutional Neural Network, best known as CNN's. This data set is the best fit for users dealing with less memory space.  
    • Handwriting recognition: this drive contains the complete source code of the project.

    Projects Based on Machine Learning 

    These small-scale projects will help you create your base and develop an understanding of the fundamentals of machine learning. Before moving towards big datasets, one should be familiar with working with a small dataset and creating a graph and learning curve.  

    1. Wine Quality Test Project 

    Here, you have to understand the chemical composition of the mixture and how the wine is made, and then you have to apply the machine learning model on the data to obtain the quality of the wine. 

    The data source you can refer to:  

    • Wine quality: This dataset is composed of the different qualities of wine and their chemical composition. There are 2 datasets that contain red and white wine data samples from the north of Portugal.  

    2. Fake News Detection

    Social media has contributed to the proliferation of fake news. It is really very hard to understand the quality and correctness of the content present in social media. According to surveys3 out of 5 messages on social media are fake. Using this model, you can understand the ambiguity of the news present in our world.  

    Fake news is like wildfire and spreads uncontrollably  

    The data source you can refer to:  

    • Fake news dataset: find out the data present in social media, which is fake and predict data or information that is the legitimate source. 

    3. Kinetics project 

    This project identifies human actions and reactions by observing their behavior during activities. This dataset contains 3 different datasets, each of which is kinetic with a different collection of URLs and high-quality images and videos. 

    The data source you can refer to:  

    • Kinetics Dataset: This contains about 650,000 video clips with 400-600-700 different classes of human action divided into subclasses, with different data set versions.

    Key Points to Remember Before Moving Toward the Machine Learning Project 

    • To understand machine learning's basic concepts, you can opt for many free or paid courses available online. 
    • After developing the concepts, move towards developing the basic level projects. 
    • Once you develop aunderstanding of basic projects and gain complete knowledge about the algorithm and its workflow, move towards intermediate projects. 
    • Then move to advanced level projects, where you can develop systems based on machine learning algorithms and techniques.


    Machine Learning automates analytical modeling and building decisions. You can opt for different free or premium courses, which help you understand the space and create your projects. The aforementioned are the collection of top machine learning projects available online, which are easy to use and develop. The project contains complete guidelines you can refer to. This will help you to learn new algorithms and master your machine-learning skills. If you want to gain expertise, dive into the concept and figure out how the module works. Machine learning is the future, and if you have set yourself up for a career in this space, then building a solid resume with a project portfolio is the right way to go about it.  

    Frequently Asked Questions (FAQs)

    1How do I start a ML project?
    • Define your problem: Clearly outline the problem you want to solve with machine learning and specify your goals and success criteria.
    • Gather data: Collect and preprocess relevant data for your project, ensuring it's clean, labeled, and suitable for your chosen ML algorithms.
    • Choose algorithms and experiment: Select appropriate machine learning algorithms, build and train models, and iterate through experimentation to improve performance.
    2How many ML projects fail?

    According to Gartner, 85% of Machine Learning (ML) projects fail. The failure rate of machine learning projects can vary widely depending on factors such as project complexity, data quality, team expertise, and project management.

    3Does ML have a future?

    Yes, machine learning has a promising future. It continues to advance rapidly, driving innovation across industries such as healthcare, finance, and autonomous vehicles. As data availability and computing power increase, ML's potential for solving complex problems and making data-driven decisions is expected to grow significantly in the coming years.


    Abhresh Sugandhi


    Abhresh is specialized as a corporate trainer, He has a decade of experience in technical training blended with virtual webinars and instructor-led session created courses, tutorials, and articles for organizations. He is also the founder of Nikasio.com, which offers multiple services in technical training, project consulting, content development, etc.

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