5 Interesting Machine Learning Datasets You Should Explore Today
If you’re an aspiring Machine Learning (ML) practitioner, having a complete array of carefully selected datasets to work with is essential. It’s not surprising that machine learning enthusiasts are always looking for new and exciting data sets to explore. In this article, we’ll explore 5 fascinating machine learning datasets that you can leverage to enhance your skills.
1. The Fashion MNIST Dataset
Fashion MNIST is among the most popular datasets available in the machine learning community. It comprises 70,000 grayscale images of 28×28 pixels each. The images feature ten distinct types of clothing, each with 7,000 samples. If you want to work with computer vision models and convolutional neural networks, this dataset is an excellent starting point. The dataset is available for download on Kaggle and can be utilized with TensorFlow, PyTorch, and other popular machine learning frameworks.
2. The Boston Housing Dataset
The Boston Housing dataset is a standard machine learning dataset that has been used extensively for regression learning. It comprises 14 attributes that are used to predict the median value of owner-occupied homes depending on the Boston Suburbs. The dataset is available on the UCI Machine Learning Repository with 506 samples.
3. The Wine Quality Dataset
The Wine Quality dataset is a great example of a classification problem. It comprises data on red and white wines, including attributes such as residual sugar, pH, and acidity, among others. Unlike other datasets, this dataset has meaningful attributes, making it an excellent choice if you want to learn classification techniques. It has 1,599 samples for red wines and 4,898 samples for white wines. You can obtain the dataset from the UCI Machine Learning Repository.
4. The World Happiness Report Dataset
Unlike the standard numerical datasets discussed earlier, the World Happiness Report dataset is a great example of a non-numerical dataset. It comprises data from various global surveys measuring variables such as income, freedom, and social support. As such, it provides a unique opportunity to learn feature extraction and data preprocessing skills in machine learning. The dataset is available on Kaggle, and you can use it for both regression and classification problems.
5. The Airbnb Dataset
Airbnb is a popular vacation rental website that offers an exciting dataset for the machine learning community. It comprises data on Airbnb listings in New York, including variables such as room type, neighborhood, and price. This dataset is ideal if you’re interested in working on real-world datasets and want to experiment with data cleaning techniques. You can obtain the dataset from Inside Airbnb, and it has over 48,000 samples.
Conclusion
In summary, there are numerous publicly available datasets that you can explore to hone your skills in machine learning. The examples listed above offer a great starting point for anyone who wants to learn about the field. However, remember that the usefulness of a dataset will depend on how it aligns with your learning goals. Therefore, ensure you choose datasets that are meaningful to you and can assist in solving real-world business problems.
