Good beginner machine learning projects include predicting house prices, classifying spam messages, predicting student performance, customer churn prediction, clustering customers, recognising handwritten digits, sentiment analysis of reviews, forecasting simple sales trends, recommending books or films, and detecting anomalies. Focus on clean data, honest evaluation and clear explanation.
Before you start
You should be comfortable with Python basics and pandas. Each project below uses public datasets, available from sources such as Kaggle and university repositories. See Kaggle for beginners.
The projects
- House price prediction (regression): predict prices from features like size and location. Teaches regression, feature engineering and error metrics.
- Spam message classifier (classification): classify SMS or emails as spam. Teaches text features and precision versus recall.
- Student performance prediction: predict exam results from study habits and attendance data. Teaches ethical questions around predictions about people.
- Customer churn: predict which customers will leave a service. Teaches imbalanced classes and business value.
- Customer segmentation (clustering): group customers by behaviour. Teaches unsupervised learning and interpretation.
- Handwritten digit recognition: classify images of digits. A gentle first neural network.
- Sentiment analysis: classify reviews as positive or negative. Teaches natural language processing basics.
- Simple forecasting: forecast monthly sales or temperatures. Teaches time series and why random splits are wrong for time data.
- Recommendation system: suggest films or books from ratings. Teaches similarity and collaborative filtering.
- Anomaly detection: detect unusual transactions or sensor readings. Teaches evaluation without many labelled examples.
What makes a project portfolio-worthy
- A clear question and why it matters.
- Data cleaning and exploration, explained.
- A simple baseline model before complex ones.
- Honest evaluation on held-out data.
- Discussion of limitations and fairness.
- A readable notebook or report, and code on GitHub.
Common mistakes
- Testing on data the model trained on (data leakage).
- Chasing accuracy without understanding errors.
- Ignoring class imbalance.
- Copying notebooks without understanding them.
Our machine learning and data science course guides students through projects like these with one-to-one feedback.
Frequently asked questions
Which project should I do first?
House price prediction or spam classification. Both are well documented and teach core ideas.
Do I need a powerful computer?
Not for these projects. Free online notebook environments are enough.
How many projects do I need for a portfolio?
Three thorough, well-explained projects beat ten copied ones.
