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The rapid development of artificial intelligence cannot leave the support of high-quality datasets. Whether it is commercial applications or scientific research, datasets provide a continuous source of power for AI technology. Datasets aren’t only the input for algorithm training, but also the determining factor affecting the maturity of AI technology. By using real world data, researchers can train more robust AI model to handle various unpredictable scenario changes.

6 Most Popular Datasets for Practicing Machine Learning

The hardest part of building a new AI solution or product is not the AI or algorithms but the data collection and labeling. So the training datasets are essential for setting up machine learning models. Below is a list of 6 popular datasets suitable for improving machine learning skills.

 

1) Iris Data

This dataset is the most popular binary classification problem. The goal of this competition is to predict whether or not an Iris flower belongs to one of two species (Iris Setosa, Versicolour). Some examples are that iris setosas have shorter petals and wider sepals than versicolours. An example prediction might be that if the petal length is greater than three centimeters and the sepals are less than six centimeters, then it’s more likely that the flower belongs to Iris Setosa.

 

Examples of the variables in this dataset are:

Petal Length

Sepals Width

Petal Width

 

There are many tutorials to approach this dataset. One of the most popular is called “Using Scikit Learn on the Iris Flower Dataset”. It’s a very good tutorial for beginners because it shows you how to use scikit learn, which has prebuilt functions that allows you to easily train models.

 

The dataset can be downloaded from here: Iris Dataset 

 

2) Boston Housing Data

The Boston Housing dataset is another popular dataset on Kaggle. This dataset contains information about housing in the city of Boston. It has over 200,000 records and 18 variables. The goal of this dataset is to predict whether or not a house price is expensive. The dataset has three different classes (Expensive, Normal, and Cheap).

 

You can see examples of features like:

 

Number of bedrooms

Number of bathrooms

Average number of rooms

 

If you’re interested in the data science field, this dataset is a great one to try. It’s not too difficult while still being very interesting.

 

The dataset can be downloaded from here: Boston Housing Dataset

 

3) Trip Advisor Hotel Reviews

Hotels play a crucial role in traveling and with the increased access to information new pathways of selecting the best ones emerged. With this dataset, consisting of 20k reviews crawled from Tripadvisor, you can explore what makes a great hotel and maybe even use this model in your travels!

 

How to use:

Predict Review Rating

Topic Modeling on Reviews

Explore key aspects that make hotels good or bad

 

Link to: Trip Advisor Hotel Reviews

 

4) Breast Cancer Wisconsin

The Breast Cancer Wisconsin dataset is a great challenge for those who are more experienced in data science. This dataset contains information about breast cancer patients in the state of Wisconsin. The goal of the dataset is to predict whether or not a patient has cancer based on their characteristics. For example, you can see from the dataset that patients with a tumor size of less than 0.50 cm have a 98% chance of survival, while those with a tumor size greater than or equal to 0.80 cm have only a 15% chance of survival.

 

Examples of variables in this dataset are:

 

Tumor Size

Grade of Tumor

Lymph Nodes Involved

There are a few tutorials on how to approach this dataset. If you’re looking for a challenge, try predicting the survival rates for different tumor sizes.

 

The dataset can be downloaded from here: Breast Cancer Wisconsin Data

 

5) MNIST Handwritten Digits

The MNIST dataset is a toy set of handwritten digits. It consists of images of size 28x28 pixels and has 60,000 training examples and 10000 test cases. The goal of this dataset is to correctly classify all the digits in the training set and also in the test set. For this type of problem you will usually use Convolutional Neural Networks (CNNs).

 

There are a lot of tutorials on how to approach this type of problem, so I suggest you start with the basics and then move on to more advanced methods.

 

The dataset can be downloaded from here: MNIST Handwritten Digits

 

6) CIFAR-100

The CIFAR-100 dataset is a great dataset to practice your machine learning skills. This dataset contains 100 images of objects in six categories: airplane, car, cat, deer, dog, and ship. Each image is 32x32 pixels and has three color channels (red, green, blue). The goal of the data is to predict which of the six categories each image belongs in.

 

Examples of variables in this dataset are::

 

Pixels

Red channel

Green channel

Blue channel

There many tutorials on how to approach this challenge. If you’re looking for a challenge, try predicting the labels for images that have been distorted or transformed in some way.

 

The dataset can be downloaded from here: CIFAR-100

The future of AI is highly dependent on the support of data. With the development of technology and the expansion of application scenarios, high-quality datasets will become the key point to promoting AI performance. In this data-driven revolution, we will be able to better meet the opportunities and challenges of technology development if we constantly focus on data quality and strengthen data security management.