The world of machine learning is fascinating, and there are new developments happening all the time. If you’re interested in getting started with machine learning Python projects, you’ll need to have access to good data sets. In this article, we’ll share 10 of the best datasets for machine learning Python projects. We’ll cover a variety of topics, including natural language processing, image classification, and more.
Top 10 Project Datasets for Machine Learning Python in 2022
Enron Electronic Mail
The Enron Electronic Mail dataset is one of the best datasets for machine learning Python projects. This dataset contains over 500,000 emails from the now-defunct energy company, Enron. The emails are a great source of data for building machine learning models.
The Enron Electronic Mail dataset is available for free on the internet. It is also well-documented, making it easy to use for machine learning projects.
Overall, the Enron Electronic Mail dataset is an excellent choice for machine learning Python projects. It is a large dataset with plenty of data for building models. It is also available for free and is well-documented.
Chatbot Intents
1. Chatbot Intents: Chatbot intents are a great dataset for machine learning projects. They allow you to train your model on a large variety of different conversation scenarios. This dataset is available for download from the NLTK website.
2. Sentiment140: The Sentiment140 dataset is a popular dataset used for training machine learning models for sentiment analysis. It contains 1.6 million tweets, each with a label indicating whether the tweet is positive, negative, or neutral. The dataset is available for download from the Stanford University website.
3. MNIST: The MNIST dataset is a well-known dataset used for training machine learning models for image recognition. It contains 70,000 images of handwritten digits, each with a label indicating the digit that was written. The dataset is available for download from the University of Minnesota website.
Label-Studio
1. Label-Studio is a great dataset for machine learning Python projects because it is open source and provides a wide variety of data annotation tools.
2. It also has a very active community that provides support and advice on using the tool.
3. The tool is very user-friendly and easy to use, which makes it ideal for beginners.
4. Label-Studio is a great choice for machine learning projects because it offers a wide range of features and is very user-friendly.
Doccano
1. Doccano is a machine learning dataset that was created by the University of Tokyo. It contains over four hundred thousand documents, including news articles, blogs, and books. The dataset is available in both English and Japanese.
2. The dataset is divided into two sets: a training set and a test set. The training set contains eighty percent of the data, while the test set contains the remaining twenty percent.
3. Doccano is a great dataset for machine learning projects because it is large and diverse. The dataset can be used to train machine learning models to perform tasks such as text classification and sentiment analysis.
4. Doccano is an open-source dataset, which means that it can be freely accessed and used by anyone. This makes it a great resource for machine learning developers who are looking for datasets to use in their projects.
Kaggle
1. Kaggle is a website that hosts data science competitions. Competitions are hosted by companies, organizations, and individuals who need help solving a problem. Competitors work to find the best solution to the problem and submit their results to Kaggle. The results are then scored by Kaggle, and the winner is announced.
2. Kaggle also has a public dataset library. This library contains datasets that can be used for machine learning projects. The datasets are categorized by topic, and each dataset has a description and a link to download the data.
3. Kaggle is a great resource for machine learning projects because it provides access to both real-world data and competition data. The competition data can be used to test and validate machine learning models. The real-world data can be used to build practical applications.
AWS
1. AWS is a cloud-based platform that provides plenty of data storage options for machine learning projects.
2. Amazon S3 is a popular option for storing data sets used in machine learning projects. It is a simple storage service that offers high scalability and reliability.
3. Another option for storing data sets used in machine learning projects is Amazon DynamoDB. DynamoDB is a fast and flexible NoSQL database service that can be used for a variety of data-intensive applications.
4. Amazon Redshift is also a good option for storing data sets used in machine learning projects. Redshift is a petabyte-scale data warehouse service that offers fast performance and low pricing.
Overall, AWS provides plenty of options for storing data sets used in machine learning projects. Amazon S3, DynamoDB, and Redshift are all popular choices that offer high scalability and reliability.
World Bank
1. World Bank: The World Bank dataset is a collection of data about different countries around the world. It includes information about the economy, population, geography, and more. This dataset is often used in machine learning projects that involve predicting economic outcomes.
2. Wikipedia: The Wikipedia dataset is a collection of data from the online encyclopedia. It includes information about different topics, articles, and more. This dataset is often used in machine learning projects that involve text classification or Natural Language Processing (NLP).
3. Amazon Reviews: The Amazon Reviews dataset is a collection of data from customer reviews on the Amazon website. It includes information about different products, ratings, and more. This dataset is often used in machine learning projects that involvesentiment analysis or predictive modeling.
UCI Machine-Learning
1. UCI Machine-Learning is one of the most popular datasets used in machine learning Python projects. It contains a wide variety of data on a variety of topics, including medical data, stock market data, and more.
2. UCI Machine-Learning is easy to use and well-documented. This makes it a good choice for beginners who are just getting started with machine learning.
3. UCI Machine-Learning is also widely used by experts in the field. This is because it contains high-quality data that can be used to train sophisticated machine learning models.
GTSRB
1. GTSRB: The German Traffic Sign Recognition Benchmark is a dataset that consists of images of traffic signs taken in real-world conditions. This dataset is often used in machine learning projects that involve image classification.
2. MNIST: The MNIST dataset is a collection of images of handwritten digits. This dataset is often used in machine learning projects that involve image classification.
3. CIFAR-10: The CIFAR-10 dataset is a collection of images of various objects, such as animals and vehicles. This dataset is often used in machine learning projects that involve image classification.
Iris
The Iris dataset is one of the best datasets to use for machine learning Python projects. This dataset contains 150 records of iris flowers. Each record contains four features: sepal length, sepal width, petal length, and petal width. The target column contains the species of the iris flower.
This dataset is ideal for machine learning because it is small but still has a good amount of data. The four features are also easy to understand. This makes the Iris dataset a good choice for beginners.
The Iris dataset is also popular because it is well-known and well-studied. There are many resources available that can help you learn more about this dataset. This makes it a good choice for more experienced machine learning practitioners as well.
