So, what kind of dataset for machine learning is appropriate for machine learning?
In this article, we'll take a look at datasets for machine learning: what they are, how they look, and some public sources you can use to get started. We also discuss the types of problems each dataset is best suited for solving.
What Is a Dataset?
Datasets are collections of data that machine learning models train on. The goal is for these datasets to provide enough information so the model can learn how to generalize from this particular type of context, and make predictions about new entities in other situations where they might be applicable, such as customers' demands/sales patterns.
With tons of historical data at hand, businesses will have better insights into what could happen next, helping them make more informed decisions that will improve outcomes down the line.
What Does a Dataset for Machine Learning Look Like?
Businesses use datasets to track customer behavior, understand trends, and make predictions. Datasets can be small, containing only a few dozen data points, or they can be large, containing millions or even billions of relevant data points for analysis.
No matter their size, datasets contain valuable information that businesses can use to make intelligent decisions.
Thanks to technological advances, it is now easier than ever for businesses to collect and analyze datasets. As a result, datasets are playing an increasingly important role in various industries all over the world.
The machine learning algorithm must learn which leads are most likely to convert and why so that sales reps can focus their efforts on the leads with the highest chances of conversion.
After the model is trained, a client can ask the machine for predictions on new leads, and the sales reps can use this information to prioritize their time and resources. By using a lead scoring dataset, businesses can efficiently boost conversion rates.
Examples of Publicly Available Datasets for Machine Learning
Publicly available datasets for machine learning are labeled data points that can be used to train and test machine learning models. These datasets contain
and textual data
labeled with the correct classification so that the machine learning algorithm can learn from them.
Here are a few examples of public datasets for machine learning to get you started:
The Palmer Penguin dataset is an excellent resource for those looking to practice their classification and clustering skills. The dataset comprises two parts, each containing data on 344 penguins—a great choice for practicing a wide range of algorithms.
The Palmer Station Antarctica LTER has shared extensive documentation on this dataset. Whether you're looking to use traditional methods like decision trees or random forests, or try something more innovative like support vector machines, the Palmer Penguin dataset is an excellent starting point.
Fashion MNIST is a fantastic dataset for practicing image classification, with a training set of 60,000 images and a testing set of 10,000 clothes images. All images are size-normalized (28x28 pixels) and centered.
The BBC News Datasets contains 2225 high-quality articles from a reputable source, each labeled with one of five categories: tech, business, politics, entertainment, or sport. The labels are evenly distributed, so no category is significantly over-or under-represented.
These datasets for machine learning can be used for text classification and other NLP tasks like sentiment analysis.
Spam SMS Classifier
The Spam SMS Classifier Dataset is an excellent asset for solving spam detection and text classification problems. The dataset is heavily used in literature, and it is fantastic for beginners. It consists of a collection of SMS messages that have been classified as spam or non-spam.
The dataset is divided into two sets: a training set (5,000 messages), which is used to train the classifier, and a test set (1,000 messages), which is used to evaluate the performance of the classifier.
The goal of the classifier is to learn from the training set and correctly classify new SMS messages as either spam or non-spam. The classifier's performance is measured by its accuracy on the test set.
Datasets for Machine Learning: The Takeaway
It would help if you had high-quality datasets to train a machine learning algorithm and make it do what you want. You can collect datasets from different sources, such as online surveys or social media platforms. The more accurate and complete the dataset, the better the machine learning algorithm will perform.
We hope this article has helped you understand how to select a good dataset for your machine learning project and given you some ideas on where to find high-quality data.
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This blog post provides insights based on the current research and understanding of AI, machine learning and predictive analytics applications for companies. Businesses should use this information as a guide and seek professional advice when developing and implementing new strategies.
At Graphite Note, we are committed to providing our readers with accurate and up-to-date information. Our content is regularly reviewed and updated to reflect the latest advancements in the field of predictive analytics and AI.
Hrvoje Smolic, is the accomplished Founder and CEO of Graphite Note. He holds a Master's degree in Physics from the University of Zagreb. In 2010 Hrvoje founded Qualia, a company that created BusinessQ, an innovative SaaS data visualization software utilized by over 15,000 companies worldwide. Continuing his entrepreneurial journey, Hrvoje founded Graphite Note in 2020, a visionary company that seeks to redefine the business intelligence landscape by seamlessly integrating data analytics, predictive analytics algorithms, and effective human communication.
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