Kaggle is an excellent platform for beginner data scientists to work with actual data. Whether you're building your portfolio or honing your skills, choosing a suitable dataset is very important. Here's a selected list of seven beginner-friendly Kaggle datasets, each picked for its educational benefit and simplicity.

1. Titanic: Machine Learning from Disaster

Considered by many to be the most popular dataset for beginners, the Titanic dataset is a great introduction to classification problems. The objective is to predict passenger survival based on attributes such as age, class, and gender. You can find the dataset here.

Why it's good for beginners: It's straightforward and has good documentation, with many available tutorials. It's well-suited for learning basic data preparation, feature creation, and model construction.
Learning areas: Data cleaning, logistic regression, decision trees, and assessment of classification models.

PassengerId	Pclass	Sex	Age	Survived
1	3	male	22.0	0
2	1	female	38.0	1
3	3	female	26.0	1

2. Iris Dataset

The Iris dataset is a machine-learning classic frequently used in introductory guides. It contains 150 observations of iris flowers, with the aim of sorting them into one of three species based on sepal and petal measurements. You can access the dataset here.

Why it's good for beginners: Small and easy to handle with a clearly defined problem (multiclass classification), making it good for trying basic machine learning algorithms.
Learning areas: K-nearest neighbours (KNN), decision trees, and support vector machines (SVM).

SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm	Species
5.1	3.5	1.4	0.2	Iris-setosa
7.0	3.2	4.7	1.4	Iris-versicolor
6.3	3.3	6.0	2.5	Iris-virginica

3. House Prices: Advanced Regression Techniques

This dataset offers housing data from Ames, Iowa, and requires participants to predict house sale prices based on different attributes. The dataset can be found here.

Why it's good for beginners: Although a bit more complex, this dataset allows learners to practice with regression models and feature creation.
Learning areas: Linear regression, feature selection, and model assessment methods like RMSE.

Id	LotArea	OverallQual	YearBuilt	SalePrice
1	8450	7	2003	208500
2	9600	6	1976	181500
3	11250	7	2001	223500

4. Pima Indians Diabetes Database

This medical dataset includes attributes related to the health conditions of Pima Indian women. The objective is to predict if a patient has diabetes. You can download the dataset here.

Why it's good for beginners: The small dataset is well-suited for practising binary classification and model performance assessment.
Learning areas: Logistic regression, decision trees, random forests, and performance metrics such as accuracy, precision, and recall.

Pregnancies	Glucose	BloodPressure	Age	Outcome
6	148	72	50	1
1	85	66	31	0
8	183	64	32	1

5. MNIST Handwritten Digits

The MNIST dataset is a classic in computer vision, containing images of handwritten digits (0-9) that must be classified. You can find the dataset here.

Why it's great for beginners: It provides an introduction to working with image data, which requires different preparation techniques than tabular data.
Learning areas: Neural networks, convolutional neural networks (CNNs), image processing, and accuracy assessment.

Pixel1	Pixel2	Pixel3	...	Label
0	0	0	...	5
0	0	0	...	0
0	0	0	...	4

6. New York City Airbnb Open Data

This dataset includes Airbnb listings in New York City, with details such as price, number of reviews, and location. It offers a chance to perform exploratory data analysis (EDA) and use basic clustering methods. The dataset can be accessed here.

Why it's good for beginners: A large dataset with authentic data, good for practising data visualization, feature examination, and clustering algorithms.
Learning areas: Exploratory data analysis, visualizing trends, clustering, and regression analysis.

ListingID	Neighborhood	Price	Reviews	RoomType
2539	Williamsburg	150	200	Private room
2595	Harlem	125	300	Entire home
3647	Midtown	200	400	Shared room

7. Wine Quality Dataset

This dataset is made up of results from physicochemical tests on wines and their corresponding quality ratings. The aim is to predict wine quality using these attributes. You can find the dataset here.

Why it's good for beginners: It offers a good way to practice regression and classification methods with a fairly straightforward yet multi-attribute dataset.
Learning areas: Feature selection, regression models, decision trees, and SVM.

FixedAcidity	VolatileAcidity	CitricAcid	ResidualSugar	Quality
7.4	0.7	0.0	1.9	5
7.8	0.88	0.0	2.6	5
7.8	0.76	0.04	2.3	5

Conclusion

These datasets are excellent for beginner data scientists wanting to build confidence with actual data problems. Simpler datasets like the Titanic or Iris can help you understand basic ideas. In contrast, more detailed datasets like House Prices or the New York City Airbnb data will give you experience working with larger, more extensive data.

Top 7 Best Kaggle Datasets for Beginner Data Scientists