Top Reliable Datasets for All Your Data Science Projects

Start working on your data science projects with these datasets.

Completing data science projects is an easy way to finesse your portfolios. If you want to scale up in your career as a data scientist, your employers would want to know the kind of data problems you can solve, and that depends on the datasets you use. Kaggle has myriad datasets and it can get overwhelming to choose the right one to test a new machine learning concept. While this is not an exhaustive list, Analytics Insight has prepared a list of good and reliable data sets that can be used for several types of data science projects.

1. Multipurpose Datasets

• Kaggle Titanic Survival Prediction Competition: This dataset can be used to test out all the basic and advanced machine learning algorithms for binary classification.
• Fashion MNIST on Kaggle: This dataset is for performing multi-class image classification for different categories like apparel, shoes, bags, jewelry, etc.
• Credit Card Approval on Kaggle: This dataset is useful for binary classification tasks regarding good and bad credit card scores, to find out what percentage of people can be tagged as defaulters for credit card loans.
• Rock Paper Scissors on Kaggle: With this dataset, you can perform image classification for these three categories.

2. Regression Datasets

• Boston House Prices on Kaggle: This dataset can help you solve regression problems.
• WHO Life Expectancy on Kaggle: Use this dataset to test your EDA skills.
• California Housing Prices on Kaggle: Similar to Boston House Prices, this can help you work on regression problems.

3. Classification Problems

• Heart Disease UCI: This contains data that will help you predict the presence of heart disease in a patient based on several factors.
• Heart Attack Analysis: Use this dataset for binary classification, to predict the chances of a heart attack.
• Campus Recruitment: With this dataset, you can predict if a student gets placed in a company based on multiple aspects.

4. Image Classification

• Pneumonia Data: This dataset on Kaggle will help you classify the type of Pneumonia from patient X-Rays.
• Face Mask Classification: Perform multiclass classification into three types, with mask, without the mask, and incorrect wearing of the mask with this dataset.
• Intel Image Classification: This Intel dataset is great for classifying neutral picturesque sceneries into 6 categories.

5. Optical Character Recognition and Recommender Systems

• MovieLens: This is an easy dataset for a recommender system. Use this to predict which movie is the right recommendation for the given situation.
• Goodreads Books: This dataset on Kaggle has all the information you need about books through many columns for building a book recommender engineer.
• Netflix Data: This dataset has a vast collection of movies and TV show details till 2019, use this for the best recommender system project.
• Handwriting Recognition: Apt for optical character recognition, this dataset has approximately 400,000 handwritten names.

6. Natural Language Processing

• Amazon Reviews: This is a popular dataset for performing sentiment analysis.
• COVID-19 Open Research Challenge: Topical and critical, this dataset has many COVID-19 research articles for text summarization, semantic search, and Q&A systems.
• Arxiv Dataset: This is a collection of arxiv research papers for creating text generation systems. It can also be used for abstractive summarization and Q&A systems.

All the above-mentioned datasets are available on Kaggle. For more information about data science-related websites and platforms, click here.