Students and aspiring professionals interested in cutting-edge technologies are working on machine learning Python projects. These machine learning Python projects can supplement hands-on experience with machine learning as well as the popular Python programming language. However, they occasionally look for multiple datasets to use in the successful creation of these projects. These project databases are freely available on the internet, causing students to become overwhelmed. So, let’s take a look at some of the top ten datasets for machine learning Python projects in 2022 to gain in-depth knowledge as quickly as possible.
Top ten Python machine learning project datasets in 2022
Enron electronic mail
With approximately 0.5 million messages, Enron electronic mail is one of the top ten machine learning Python datasets. It was first made public and is widely used for pure language processing. This project dataset aids in the completion of multiple ML Python projects.
Chatbot intents
Chatbot Intents is a popular Python machine learning project dataset for classification, recognition, and chatbot development. The dataset is available as a JSON file with various tags selected from a list of patterns for ML Python projects.
Label-studio
Label-studio is an open-source data labeling tool for machine learning and Python projects. As project datasets, students and working professionals can label data in a variety of formats. It can be combined with ML models to provide label predictions and active learning.
Doccano
Doccano is a well-known open-source data labeling tool for machine learning Python projects. There are various types of labeling tasks with various data formats. This dataset has appealing features for sequence labeling, sequence-to-sequence tasks, text classification, and many other applications.
Kaggle
Kaggle is the most prominent machine learning Python project dataset for students to explore, analyze, and share high-quality data. It provides multiple categories of 10,000 datasets to complete projects and add value to resumes.
AWS
AWS datasets are well-known for covering storage costs for publicly available high-value cloud-optimized datasets. It enables project workers to democratize real-time data access by making it available for machine learning Python projects.
World Bank
World Bank datasets are popular for providing enough data to start a new ML Python project. It contributes high-quality statistical data to the development strategy. The Development Data Group is well-known for data coordination with a variety of financial and sector datasets.
UCI machine learning
UCI machine learning is also known as the UCI machine learning repository because it provides the machine learning community with approximately 622 datasets. Students can use this project dataset to complete a successful project and land a job with one of the world’s leading technology companies.
GTSRB
GTSRB, or German Traffic Sign Recognition Benchmark, is known for its 43 traffic sign classes and 39,209 training data for multiple projects. As a large multi-category classification benchmark for computer vision and ML problems, there are two datasets available.
Iris
Iris is one of the top ten ML Python projects datasets, and it contains three different types of irises: Setosa, Vericolor, and Virginica. It is a multivariate dataset with four different features, including length, width, and many others. It is appropriate for a typical test case with multiple statistical classifications.
