10 Kaggle Datasets:Practice and improve your Data Science Skills 

10 Kaggle Datasets:Practice and improve your Data Science Skills 

9 mins read1.2K Views Comment
Updated on Jan 4, 2023 11:07 IST

In this article we have covered 10 Kaggle for Datasets for practicing and improving our Data Science Skills with their explanations. It also covers Skills Required by Data Scientists to shine in Kaggle.


Kaggle is a platform for Data Scientists and machine learning experts. It provides them with hands-on expertise in the complex data science field. The communities role is to provide exposure for Data Scientists to upskill themselves and grow in their field. They conduct competitions to help Data Scientists compete with each other and enhance their skills. When you are a Data Scientist who wants to achieve that greatness in your career, this article will help you learn more about Kaggle, Kaggle datasets, and how you can use those to improve your data science skills. In this article we have covered 10 Kaggle for Datasets for practicing and improving our Data Science Skills.

Table of Contents

What is Kaggle? 

Kaggle is a crowd-sourcing platform that helps you attract, nurture, train, and challenge fellow Data Scientists all over the globe to solve machine learning, predictive analytics, and data science problems. 

Kaggle will solve your problems by giving you a space where you have fellow enthusiasts engaging and starting a healthy competition to solve their real-life problems. The experience

you gain with Kaggle will be immensely valuable for preparing your new identity in the growing field of data science. 

Skills Required by Data Scientists to shine in Kaggle

Here are some skills which you should master when you want to shine in the Kaggle community: 

Machine Learning

Data is growing more and more as the years go by. You will find yourself accessing one or more machine learning algorithms to ensure data stability in your company. So you must get access to those algorithms like random forest, SVM, k-neighbour, regression, and many more. Kaggle is a platform that can help you learn about all the best algorithms which machine learning offers and make yourself an expert. 

Statistics & Mathematics

As a Data Scientist, you will need a basic understanding of the mathematical and statistical terminologies. During exploratory data analysis, understanding these fronts will help you get the best outcome. Kaggle is the best place for you to learn about these terminologies. 

Business Acumen & Critical Thinking

When you have a thorough understanding of the problems your business face, you can use those to formulate a solution for solving that problem. Data Scientists must have critical thinking skills to shine in their role. With Kaggle, you can make that happen. 

Storytelling & Communication

Communication, Storytelling, and Data-driven Decision Making use visualization tools to gain strength. Kaggle will help you gain these skills and translate your insights into business-friendly language. 

Programming Skills

The responsibility of a Data Scientist is to work within the organization for data manipulation and wrangling. You should have significant skills like Data analysis and preparation to become the best, and Kaggle has it all. 

How to Select a Data Science Course?
How to Select a Data Science Course?
If you are interested to find data science course but are confused how to choose right course for yourself then this blog will guide you
  All About Train Test Split
  All About Train Test Split
Train test split technique is used to estimate the performance of machine learning algorithms which are used to make predictions on data not used to train the model. In this...read more

Also read: Difference between Regression and Classification Algorithms

Also Read: How tech giants are using your data?

Also read:What is machine learning?

Also read :Machine learning courses

Benefits of Kaggle

Here are the benefits of Kaggle: 

● Using Kaggle will help you find a good teammate or competitor to get you on the right track and give you healthy competition. 

● Kaggle has a job portal where you can apply for jobs easily. 

● Kaggle is well-known among the Data Science community, so they will recognize your achievements among your industry peers, which can help build your career strongly.

● Kaggle provides different courses to help you brush up on your skills and refine your knowledge. 

● You can get monetary prizes awarded for competitions, and some recruitments will also happen with those competitions. 

Tips for Kaggle Data Science

Here are some tips that will help you in getting the best experience from using the Kaggle Community: 

● Set Incremental Targets: Incremental targets are the best when you want to gain more knowledge. Each target you face will motivate you to reach your ambition with positivity and determination. With Kaggle, you can have that growth by choosing incremental targets to move to a bigger end game. 

● Forums: Forums in the Kaggle community where you can ask your doubts and rectify them with your peers. 

● Reviewing the most voted Kernels: When you are a participant, you can submit ‘Kernels’. They are short scripts that help explore the concepts, demonstrate the methods, or find and share a solution. 

● Joining forces to test your limits: When you have healthy competition among your peers, you can improve your skills and be on track with your technical knowledge. 

● Stepping stone: The Kaggle community is a stepping stone toward success for many people. So use the platform wisely and improve yourself to get a better future. 

Kaggle Datasets

The datasets that users in the Kaggle platform publish are Kaggle datasets. When you become a member of this platform, you can explore and build models using these datasets. All the Kaggle datasets are free to use by everyone in the community. There are also courses, certifications, and competitions that you can use to improve your skills. 

Top 10 Kaggle Datasets for helping you improve your data science skills: 

Here are the top 10 Kaggle Datasets of all time for helping you improve your data science skills: 

1. Titanic Dataset

The Titanic Dataset contains original data from the Titanic competition. It is ideal when you need a dataset for the binary logistic regression approach. This Kaggle dataset consists of passenger information like ID, name, sex, fare, and other similar information. 

It involves the user creating a machine learning model which will help you predict which passengers survived the Titanic Shipwreck and which didn’t. There are also many tutorials available to help you find the best approach to use this dataset.

2. Breast Cancer Wisconsin

Breast Cancer Wisconsin is the dataset that more experienced data scientists use. This Kaggle Dataset contains information about breast cancer patients in Wisconsin. The main goal of this dataset is to predict whether or not a patient has breast cancer, depending on their characteristics. For example, when you see patients with tumor size less than 0.5 cm, the survival rate is high and vice versa. 

3. MNIST Handwritten Digits

The MNIST Handwritten Digits is a Kaggle dataset with a toy set of handwritten digits. It contains images of size 28 X 28 pixels and has 60,000 training examples and over 10,000 test cases. 

The main goal of this dataset is to classify all the digits in the training and testing dataset correctly. For this type, you will use Convolutional Neural Networks. There are tutorials related to this on the Kaggle community to provide you with a better understanding. 

4. CIFAR-100 

The CIFAR-100 is a Kaggle Dataset where you can practice your machine learning skills. This dataset consists of 100 images of objects in six categories: car, deer, cat, dog, airplane, and ship. 

Every image is 32 X 32 pixels and has three different color channels Green, Blue, and Red. The main goal of this dataset is to predict which of the six categories each image belongs in. You can understand more about this when you see the tutorials available in the community. 

5. European Soccer Dataset

The European Soccer Dataset is a Kaggle dataset that helps you with data analysis and machine learning. It contains data for 25,000+ matches, 10,000+ players, and 11 European countries with their lead championships. 

It also contains players and teams, their contributions, team lineup with squad formation, and detailed match events like goal types, corners, fouls, and possessions. You can find the comprehensive data of all the games in this dataset. 

6. Credit Card Fraud Detection

Credit Card Fraud Detection is a Kaggle dataset that will help companies detect fraudulent credit card transactions. This dataset consists of European credit cardholders’ transactions until the current year. The dataset contains details of approximately 2,84,807 transactions, including 492+ frauds, which happened some days. Recently, it released a simulator of transaction data for a practical handbook on machine learning for detecting credit card fraud. 

7. Medical Cost Personal Dataset

Medical Cost Personal Dataset is a Kaggle dataset that can forecast insurance using the regression model. This dataset has age, sex, body mass, children, smoker, region and

charges, and other related factors of a person’s details. You can also use this dataset from GitHub. 

8. Machine Learning & Data Science Survey

Kaggle conducted an industry-wide survey some years back to establish a comprehensive overview of the data science and machine learning arena. This survey received over 16k+ responses. 

The gathered information around these fields of innovation has improved data science to be the significant technological development over the years. You can find the Machine Learning & Data Science Survey Kaggle dataset in the community with the tutorials. 

9. Annotated Corpus for Named Entity Recognition

This Kaggle dataset is extracted from the Groningen Meaning Bank corpus. It is tagged, annotated, and built for training the classifier to predict labeled entities like name, location, and other related details. 

This dataset will give you a greater understanding of feature engineering. It will also help solve business problems like picking entities from electronic medical records. You can find the related tutorial in the Kaggle community. 

10. Mobile Price Classification

The Mobile Price Classification dataset is a Kaggle dataset with different features and various data distribution patterns. You can find categorical features, binary data, and even numerical continuous data. There are a lot of data patterns for ensuring that one can work with heavy data using mathematical computations and statistics. 

Other Important Kaggle Datasets

Here are some important Kaggle datasets for your understanding: 

● Netflix Movies & TV Shows 

● Trip Advisor Hotel Reviews 

● Melbourne Housing Market 

● Churn Modeling 

● Kepler Exoplanet Search Results 

● Heart Failure Prediction Dataset 

● COVID-19 data from John Hopkins University 

● Binance Coin Cryptocurrency data 

● 2022 Ukraine Russia war 

Wrapping Up

The Kaggle Community is a subsidiary of Google LLC. It is a great community to collaborate and grow with your data science and machine learning peers. You can visit their community to understand more about the dataset tutorials and expertise in your career path.


How do you practice data science in Kaggle?

Learn and understand the basics of exploring data. u25cf Train your machine learning model. Tackle the competitions which arise on the way. Compete with your peers to increase your learning curve.

How many Kaggle datasets are there?

In Kaggle, you can find all the data and code to work on your data science skills. You can find over 50,000+ public datasets and 4,00,000+ public notebooks, which you can use whenever you need.

What algorithms are most successful on Kaggle?

The most successful algorithms on Kaggle are Random Forest, Neural Networks, and GBM.

What are the types of datasets?

Here are the types of datasets: Correlation dataset, Multivariate dataset, Numerical dataset,Categorical dataset,Bivariate dataset

Can I use SQL in Kaggle?

SQL is one of the easiest programming languages. It will give you access to data in these datasets to make them easier to understand.

About the Author

This is a collection of insightful articles from domain experts in the fields of Cloud Computing, DevOps, AWS, Data Science, Machine Learning, AI, and Natural Language Processing. The range of topics caters to upski... Read Full Bio