A common question for beginners in machine learning is, “What projects can I work on?” Our experts at ProjectPro suggest diving into a variety of ML project ideas across different industries. These projects help you apply the skills you’ve learned while solving real-world problems.
We’ve put together a list of exciting machine learning projects with source code, perfect for professionals just starting their careers. These projects are designed to give you a feel for the challenges you might face as a machine learning engineer, deep learning engineer, or data scientist, making them great additions to your portfolio.
Top 10 Machine Learning Projects with Source Code for 2025
If you’re just starting out in machine learning or you’re a final-year student, it’s important to choose projects that not only interest you but also keep you motivated.
Start by looking for datasets that match your passions while keeping a good balance of complexity and size. To build your portfolio, come up with a list of potential machine learning project ideas, pick the most exciting ones, and dive in to boost your resume.
Our experts at ProjectPro recommend starting with data cleaning projects, then gradually moving on to analytics, machine learning, and deep learning. To help you out, we’ve put together a list of projects categorized by difficulty, perfect for aspiring machine learning engineers who are eager to work on AI and ML but need some exciting ideas to get started.
Also Read: What is the Best AI Code Editor for Developers?
Best Machine Learning Projects for Beginners
If you’re new to machine learning or a final-year student, working on real projects is key to deepening your knowledge. Here are some cool and beginner-friendly machine learning projects to help you get hands-on experience.
1) Home Value Prediction
Imagine you’re buying or renting a house, and you want to know how much it’s worth. Zillow, created in 2006, provides a tool called “Zestimate” that estimates home prices based on factors like income, crime rates, and the number of schools nearby.
Project Idea: Use Zillow’s economic dataset to build a model that predicts house prices using XGBoost. You’ll answer questions like which states have the highest rent, what houses are worth per square foot, and much more.
Industry: Real Estate
2) Sales Prediction
For beginners, it’s great to dive into various machine learning topics. Here’s one focused on sales data for grocery stores.
Project Idea: Use the BigMart sales dataset to predict next year’s sales for 1559 products across 10 outlets. You’ll explore how different factors influence sales and how machine learning can boost business outcomes.
Industry: Retail
3) Music Recommendation System
Recommendation systems are everywhere. Think about how Netflix or Spotify suggest movies or songs based on what you’ve liked before.
Project Idea: Use a music streaming service’s dataset to build a recommendation system that suggests songs or artists based on a user’s listening history. Try applying classification algorithms and even deep learning techniques like neural networks.
Industry: Entertainment
4) Iris Flowers Classification
This is one of the simplest ML projects. It’s often called the “Hello World” of machine learning.
Project Idea: Using the Iris dataset, classify flowers into three species based on their petal and sepal measurements. This is a great starting point for beginners to practice handling data.
Industry: Medicine
5) Stock Prices Predictor
For aspiring financial data scientists, predicting stock prices is an exciting challenge.
Project Idea: Use time series forecasting models like moving average, exponential smoothing, or ARIMA to predict future stock prices based on historical data.
Industry: Finance
6) Wine Quality Prediction
Wine quality is determined by factors like alcohol content and acidity.
Project Idea: Build a model to predict wine quality using its chemical properties. Visualize the data and fine-tune the model to improve its accuracy.
Industry: Viticulture
7) Movie Recommender System
With more people streaming movies, creating a personalized recommendation system has become essential.
Project Idea: Use the Movielens dataset to build a system that recommends movies based on user preferences. You can even create a word cloud of movie titles to get started.
Industry: Entertainment
8) House Pricing Prediction
Want to predict house prices in Boston? This dataset has information about housing and various attributes like crime rates and school quality.
Project Idea: Apply regression models to predict house prices in Boston based on the dataset’s attributes. This is a good beginner project to learn basic machine learning concepts.
Industry: Real Estate
Also Read: How to Use GitHub Copilot for Coding Projects?
9) Sentiment Analysis
Social media is full of data that can help analyze public opinions.
Project Idea: Using Twitter data, you can build a model that analyzes the sentiment behind tweets—whether they’re positive or negative. This is a great way to learn about text data and classifiers.
Industry: Multiple
10) Interest Rate Prediction
Predicting interest rates involves understanding trends and factors affecting the economy.
Project Idea: Use sentiment analysis to predict which houses will attract the most interest based on rental listings’ viewer reactions. This helps owners understand demand and improve their listings.
Industry: Real Estate
These projects are perfect for gaining real-world experience and improving your machine learning skills. Pick the one that excites you most and start building!
How do I Start a Machine Learning Project?
No project succeeds without a solid plan, and machine learning projects are no different. Building your first machine learning project is simpler than it seems, as long as you have a clear plan in place.
To get started with any ML project, you need a comprehensive approach—from defining the project to deploying the model and managing it in production. Here’s a simple breakdown of the key steps in a machine learning project to help ensure success:
1) Step One: Scoping the Machine Learning Project
Before diving in, it’s crucial to understand the business needs of the project. The first thing to do is choose the right problem or use case that the ML model will solve. Picking the right project and evaluating its potential return on investment (ROI) is vital for success.
2) Step Two: Data
Data is the heart of any ML model, and you can’t build a model without it. The data stage is a four-step process:
- Data Requirements: Understand what kind of data you need, how it should be formatted, where it’s coming from, and whether it meets compliance standards.
- Data Collection: Work with database admins, data architects, or developers to gather the data from internal sources or third-party vendors.
- Exploratory Data Analysis (EDA): Check the data to ensure it meets the requirements, is in good shape, and free from errors.
- Data Preparation: Clean and transform the data so it’s ready for the machine learning algorithms. This step involves fixing errors, creating new features, and ensuring the data is in the right format for the model.
3) Step Three: Building the Model
This step could take anywhere from a few days to several months, depending on the complexity of the project. Here, you’ll choose the appropriate machine learning algorithm and start training your model on the data. It’s essential to understand how to measure accuracy and error to know if the model is performing well. After training, you’ll test the model on new data to check its performance and prevent overfitting. It’s important to ensure the model works well not just on historical data, but also on new, unseen data.
4) Step Four: Deploying the Model into Production
Once your model is built, it’s time to deploy it so that new data can flow into the system and help it learn more. But deployment alone isn’t enough—you must also monitor its performance. Regularly retrain the model using fresh production data to keep it accurate. This process, called model tuning, ensures that the model remains reliable and free from biases or errors.
Also Read: What are the Top Coding Languages for AI Projects?
How do you Put Machine Learning Projects on Your Resume?
Real-world experience is the best way to prepare for success, especially in machine learning. As a beginner, the more hands-on experience you get by working on real ML projects, the better you’ll be at landing one of the hottest jobs out there.
After completing thorough data science training, the next step to securing a top role as a machine learning engineer or data scientist is to create an impressive portfolio. This portfolio will show potential employers that you can apply ML techniques in real-world situations. Here’s how to make your machine learning resume stand out:
- List your machine learning projects after your work experience section.
- Number the projects in order and include the project title.
- For each project, add a brief description of the dataset and the problem it solves.
- Highlight the ML tools and technologies you used for each project.
- Link each project in your resume or portfolio to GitHub, your website, or your blog, so employers can get a deeper understanding of your work.
And here’s a bonus tip from Peter Vaclav, Data and AI Leader at RGA, on how to add the right skills to your resume.
