Top 7 Machine Learning Projects For Beginners?

Machine learning projects employ machine learning algorithms and techniques to create models that can make predictions or judgments based on input data. These projects frequently include building a machine learning model on a big dataset, followed by utilizing the taught model to make predictions or choices on fresh, previously unknown data. Machine learning projects can be classified into three types: supervised learning, unsupervised learning, and reinforcement learning. The model is trained on labeled data in supervised learning, and the proper output is delivered for each example in the training set. In unsupervised learning, the model is not given with labeled training samples and must learn to find patterns and correlations in the data on its own. Reinforcement learning entails teaching an agent how to behave in a given environment in order to maximize a reward. In this post, we will look at the top seven machine−learning projects for beginners.

1. Titanic Survival

If you're new to ML projects, this Kaggle beginner's project is the perfect choice for you. One of the most well−known maritime disasters in history, the Titanic accident, is the subject of this. Based on information about the passengers' age, gender, socioeconomic status, and other characteristics, you only need to guess which people survived the Titanic catastrophe.

For instance, a wealthy person from the first class is far more likely to have survived than a person from the third class. For this project, you can utilize the Titanic datasetfrom Kaggle. To add to the intrigue, this dataset includes genuine information about those who perished and survived the Titanic accident.

2. Sales forecasting with Walmart

While perfectly forecasting future sales may be impossible, organizations can come close using machine learning. Walmart, for example, provides datasetsfor 98 goods across 45 stores, allowing developers to get information on weekly sales by location and category.

A project of this size aims to improve data−driven decisions in channel optimization and inventory planning.

3. Stock Price Prediction

Stock price projections, like sales forecasting, are based on databases from previous prices, volatility indexes, and fundamental factors. With a project like this, beginners can start small and utilize stock−market statistics to make forecasts over the following few months.

It's an excellent approach to practice making predictions based on large datasets. To begin, obtain a stock market dataset from here.

4. Wine Quality Prediction

Shopping for new and unfamiliar wines may be a hit−or−miss experience. There is no guaranteed method to determine whether a wine is of good quality unless you are an expert who considers aspects such as age and price. 

The Wine Quality Data Setis a fun machine−learning project that provides such information to assist forecast quality. This project provides ML novices with hands-on experience with data visualization, data exploration, and classification models.

5. Iris Flower Classification

The Iris Flowers datasetis well-known and one of the oldest and easiest machine learning projects to train on for beginners. With this assignment, students must grasp the fundamentals of manipulating mathematical quantities and data. Data points include the length and breadth of sepals and petals. Successful research sorted irises into one of three species using machine learning.

6. Loan Price Prediction

It is quite difficult to obtain a bank loan. Obtaining a loan needs a complicated combination of elements, not the least of which is a consistent income! As a result, the goal of this ML project is to develop a model that will categorize how much loan a user can acquire depending on numerous characteristics such as marital status, income, education, work prospects, number of dependents, and so on. 

The Loan prediction dataset contains information on all of these parameters, which can then be used to build an ML model that shows the amount of loan that can be accepted.

7. Movies Recommendation

Almost everyone nowadays utilizes technology to watch movies and TV episodes online. While deciding what to watch next might be difficult, recommendations are frequently made based on a viewer's past and preferences.

This is accomplished by machine learning and can be a fun and simple project for novices to do. New programmers can practice by writing code in Python or R and using data from the Movielens Dataset.


The machine learning projects for beginners will assist you in demonstrating machine learning experience using supervised and unsupervised approaches. All of these ML Project Ideas are excellent choices if you are new to Machine Learning or if you already know the basics but need more experience. Check out all of these projects, and after you're through, you can try even more projects on Kaggle and participate in competitions. Who knows, you could even win the first prize!