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Kaggle has established itself as one of the most essential stepping stones for students and professionals interested in Data Science these days.
Kaggle features a wealth of online resources to aid in the learning of Data Science. Thousands of datasets, data science competitions, code submissions on datasets, community discussion, and even beginner-friendly courses are all available. In addition, the user receives a public user profile that monitors and displays all of the user's efforts and accomplishments.
The user profile displays who the user follows, who follows the user, any code the user has written, any datasets the user has created, and other information. There are a variety of rating methods as well. The kaggle profile is a great method to develop shareable online projects and showcase your skills. Your kaggle profile serves as a tool to display your Data Science skills, similar to how your HackerEarth or Code Chef profile shows your competitive coding skills.
To create a solid kaggle profile, one must work with data and create high-quality Python or R notebooks in the form of projects, all while telling a storey with the data.
On Kaggle Notebooks, users can add various data visualisations, write markdown, and train models. There is a lot that can be done with them. The best part of Kaggle Notebooks is that they don't require the user to install Python or R on their computer in order to use them. Almost any important library can be imported directly. TPUs are also available for free on Kaggle. Tensor Processing Units (TPUs) are deep learning-specific hardware accelerators. They're available in Tensorflow 2.1 via the Keras high-level API and, at a lower level, in models with a custom training loop.
Working with Datasets on Kaggle is thus quite simple and convenient, and all beginners should give it a go in order to gain some expertise and information.
Here are some datasets that any newbie can use to create amazing projects:
This is a dataset that holds a lot of promise. This dataset is useful for a recommendation engine, trend analysis, popularity prediction, and unsupervised clustering, as indicated in the tasks.
Discover Influential Artists in a Variety of Genres.
Create an artist recommendation engine that is based on content.
Predict the popularity of a song based on its other characteristics.
Examine music trends over the course of a century.
Different genres are grouped together based on auditory characteristics.
By browsing at an Airbnb map of New York City, you may compare pricing by region.
Predict the cost of an Airbnb rental in New York City.
Examine whether there is a price difference between room types.
This information is based on demographics of the population. The data includes information such as the student's food type, test preparation level, parental education level, and pupils' math, reading, and writing results.
Various types of regression and classification issues can be solved using the data. It can also be used to determine which elements contribute to improved exam results. Overall, it should be fun to work on.
Examine tweets about the new Pfizer and BioNTech vaccine to learn more about the vaccine's reception, subjects of discussion, and favourable and negative feelings.
The classic dataset for categorising dogs and cats. There are several Dog and Cat photos available for training models and making predictions. For students interested in Image Processing or Computer Vision, this dataset is a must-have. You also get to see a lot of lovely cat and dog pictures.
Is there anyone who doesn't enjoy Netflix? Netflix TV episodes and movies are included in this kaggle dataset. This dataset can be used to develop a high-quality Exploratory Data Analysis project.
This dataset can be used to determine what type of material is created in which country, identify comparable content based on the description, and perform a variety of other activities.
Downloading youtube video using python. Here we will be using pytube module for downaloding youtube videos.pytube is a lightweight, Pythonic, dependency-free, library for downloading YouTube Videos.
ML Ops (or MLOps) is a set of methods that combines Machine Learning, DevOps, and Data Engineering with the goal of reliably and quickly deploying and maintaining machine learning systems.
In this article we will be looking at Low Pass and High Pass Filter and how to apply them using C++ and OpenCV