11 Machine Learning Projects For Beginners

We are in a critical period for machine learning, with job postings for machine learning engineers rising 53% between 2020 and 2023. Thanks to the exciting work being done and the high salaries in this and adjacent careers, more people are looking to become experts in this field.

However, using machine learning to solve problems in the real world comes with many challenges. Applying theoretical concepts to real problems requires additional skills, most of which are best taught through experience. Working on machine learning projects early on will help.

This article takes you through 11 of the best machine learning projects for beginners today. These projects offer a solid introduction to different concepts, help hone your problem-solving skills, and get you working on real-life applications of machine learning.

Machine learning projects demand a lot of time and effort, especially for beginners or experienced ML engineers dealing with complex, multi-faceted problems. However, the upsides of taking on these projects include:

• Developing a Deeper Understanding of ML Algorithms: ****A solid understanding of ML algorithms helps you deliver better insights and solutions with better explainability.
• Working on Practical Applications: Machine learning projects for beginners offer a pathway from theory to using ML to solve real-world problems.
• Getting Familiar with ML Tools: Working on novice machine learning projects will introduce you to the ML tools available today and help you understand when and how they are used.
• Developing Problem-Solving Skills: Problem-solving skills are best developed by solving actual problems. Working on projects is an opportunity to do just that.
• Learning to Implement Complete Projects: There are many steps to implementing a machine learning project. These beginner machine learning projects will help you develop the habit of completing projects from start to finish and show you how to refine the final results into a portfolio piece.

To help you get started on working on projects, we have curated a list of 11 machine learning projects that are well-suited to beginners. These have a relatively low difficulty level but should still be sufficiently challenging for a newbie to machine learning.

1. Used Car Price Prediction

The goal of this project is to predict the price of used cars based on specific characteristics, e.g., condition, mileage, make, etc. This is a common beginner ML project because solving it requires the application of basic concepts, e.g., EDA, feature engineering, feature selection, linear regression, etc.

Dataset and Approach to Solution

Different datasets can be used for this project, including this one on Kaggle. Linear regression is often used, but it’s a good idea to test other models as well, such as random forest and XGBoost, and compare their performances.

To solve this problem, you’ll start with data wrangling and exploratory data analysis. This will help you understand the data and deal with missing values and other issues. Feature engineering is then done to ensure the columns of raw data are transformed into features suited to supervised learning before feature selection eliminates all but the best. Finally, the model(s) are built, and their predictive powers are compared. Check out this similar project on rent price predictions.

2. Sales Prediction

Sales prediction may be a beginner machine learning project, but it has many practical applications. These models are used by grocery chains, individual stores, manufacturers, etc. They can also be used by utility companies to estimate future consumption rates. These models help companies with budgeting and ensure they have enough but not too much inventory or capacity.

Dataset and Approach

This take-home assignment on Interview Query comes with its own dataset and instructions explaining the deliverables and how the final model will be evaluated. As in the first project, you start with a series of data preprocessing steps to ensure it is the right quality for training models. This problem can be solved using linear regression. This is an excellent opportunity to learn how to implement linear regression using different libraries, e.g., scikit-learn, PyTorch, etc.

3. Iris Flower Classification

The iris flower classification is a classic machine learning project where the goal is to use characteristics such as petal length and sepal width to predict which of three classes an individual iris flower belongs to. This is a basic example of how machine learning can be used to solve a classification problem.

Dataset and Approach

The dataset for this problem is available on Kaggle but is also a built-in dataset in scikit-learn. The standard approach used for this project is to load the data from sklearn, perform some exploratory data analysis and visualizations, and train and evaluate the model. Model training can be done using sklearn, PyTorch, or other libraries.

4. Churn Prediction

Customer acquisition is more expensive than customer retention. This is why businesses invest a lot to understand when and why they are likely to lose customers. If a company knows, for example, that a customer might cancel a subscription, they can take steps to keep them from leaving.

Dataset and Approach

For this project, you can use this classic Telco Customer Churn dataset on Kaggle. The dataset contains demographic information such as age and gender, specific services a customer subscribed to, contract type, and whether or not they churned. This is a binary classification problem since predictions can only be one of two.

After the exploratory analysis and preprocessing, you’ll need to identify features likely to be good predictors. Different classifiers may yield significantly different results, so trying several is a good idea. Check out these solutions where the ridge and Naïve Bayes classifiers are used.

5. Image Classification Using Neural Networks (MNIST Fashion)

This is another classic deep-learning image classification problem. This project differs from the iris flower classification because it uses a larger dataset with 10 distinct classes. The goal of this machine learning project for beginners is to accurately detect the type of clothing an image shows. This project is often used to benchmark machine learning algorithms.

Dataset and Approach

The MNIST Fashion dataset consists of 70,000 grayscale images: 60,000 for training and 10,000 for testing. To solve this, you can use a convolutional neural network (CNN), as shown in this post, where the Keras deep learning library is used.

6. Breast Cancer Detection

This breast cancer detection project is well suited to beginners who hope to apply machine learning in the medical field. The goal is to predict the likelihood of breast cancer in a patient with a solid mass in their breast.

Dataset and Approach

This dataset from the Diagnostic Wisconsin Breast Cancer database is often used for this project, but there are other datasets available, including some with images. In this dataset, ten features, including radius, texture, compactness, and symmetry, have already been computed from the images.

This is a classification problem since the mass can either be benign or malignant (cancerous). Solving it requires identifying features that make the best predictors and some exploratory analysis. Categorical data will also need encoding before different classification algorithms are tested. In this example, 7 different algorithms are tested and compared.

7. Cryptocurrency Sentiment Analysis (NLP)

Sentiment analysis is used to detect whether a group has positive, neutral, or negative feelings toward something. This information is useful for companies that want to make their products more appealing to their target audiences. Sentiment analysis makes use of natural language processing (NLP), which involves the use of machine learning to analyze human languages.

Dataset and Approach

The dataset for this project can be found in this take-home assignment on Interview Query. The goal is to see if there is a correlation between historical sentiments on a coin and its price. Check out this post to understand how NLP is used for sentiment analysis. You can also discover more sentiment analysis projects and datasets in this IQ post.

8. Predict Credit Card Approval

Predicting whether a credit card application will be approved or not is another popular machine learning project for novices. This project has potential applications in the real world because credit card issuers receive many applications and have to decide whether an applicant is a high default risk.

Dataset and Approach

The UCI credit approval dataset is an excellent dataset for beginners to start with. This is a binary classification problem since each application can either be approved or denied. Try to understand the features and relationships in the raw data and handle issues such as missing data before using the data for training. You can also test different classifiers to see which ones provide more accurate predictions, as seen here.

9. Music Recommendation System

Recommender systems are some of the most successful real-world implementations of machine learning. These models are used in e-commerce and by content streaming sites. Spotify, for example, uses these systems to recommend audio content that its listeners are likely to enjoy.

Dataset and Approach

This dataset on Kaggle contains data from over 100,000 songs across different genres. The features include acousticness, tempo, danceability, and duration. The goal is to build a recommendation system, but the dataset also provides a good opportunity to practice building classification models to put the songs into different genres. In this implementation, cosine similarity is used to identify songs similar to the one entered. You can also try building this e-commerce recommender system on Interview Query.

10. Plane Ticket Price Optimization

In the competitive air travel industry, having tickets listed at the right price at the right time is essential to maintaining a competitive advantage without sacrificing profitability. One beginner machine learning project that can help is price optimization. This is a system that dynamically recommends the best price for a ticket based on factors such as time and demand.

Dataset and Approach

One such problem, along with the dataset, is available as a micro-challenge on Kaggle. The goal is to write a pricing function that can pick the best ticket price based on the number of days left before the flight and the number of remaining seats. There’s also a limit to the number of tickets you can sell at a particular price.

11. Automated Number Plate Recognition

Automatic number plate recognition has many applications, including law enforcement and ticketing systems. Beginners may find some implementations of this project more challenging.

Dataset and Approach

To “read” a number plate from an image, the image must first be preprocessed to enhance readability. The number plate must also be isolated from the rest of the image before the characters are extracted using an OCR. In this project, the OCR should be trained using deep learning. This implementation will guide you in your own project.

Embarking on a machine learning project may be daunting at first, but there are plenty of simple projects that have real-world applications. As a novice, working on these projects will introduce you to the world of problem-solving using machine learning and help you hone the skills needed to tackle more complex challenges. These projects are also excellent practice for the types of questions you may be asked when interviewing for certain positions.

Interview Query offers a wide range of resources to help you prepare for any machine learning role. Many of our take-home challenges have a machine learning component, and you can also access common interview questions related to machine learning. Further, we have curated other lists of machine learning and AI projects to give you ideas on problems you could tackle to improve your skills, including this list of image recognition projects. Our modeling and ML learning path can also help you refresh your machine learning fundamentals before an interview.

Getting started on problem-solving with machine learning is challenging, but we trust our machine learning projects for beginners will ease you into it.