Data mining datasets

  • Data mining techniques

    How do you prepare your data?

    1. Collect data.
      2. Collecting data is the process of assembling all the data you need for ML.
    2. Clean data.
      3. Cleaning data corrects errors and fills in missing data as a step to ensure data quality.
    3. Label data
    4. Validate and visualize

  • How data mining is done?

    The data mining process includes projects such as data cleaning and exploratory analysis, but it is not just those practices.
    Data mining specialists clean and prepare the data, create models, test those models against hypotheses, and publish those models for analytics or business intelligence projects..

  • How to mine data from database?

    This involves exploring the data using various techniques such as clustering, classification, regression analysis, association rule mining, and anomaly detection.
    Data mining has a wide range of applications across various industries, including marketing, finance, healthcare, and telecommunications..

  • Types of data mining

    The first step in data mining is almost always data collection.
    Today's organizations can collect records, logs, website visitors' data, application data, sales data, and more every day..

  • What are datasets in data mining?

    Through data mining, data scientists assist in the analysis of gathered data.
    A dataset is a set of numbers or values that pertain to a specific topic.
    A dataset is, for example, each student's test scores in a certain class..

  • What is a data set example?

    A data set is a collection of numbers or values that relate to a particular subject.
    For example, the test scores of each student in a particular class is a data set.
    The number of fish eaten by each dolphin at an aquarium is a data set..

  • What is data mining database?

    Data mining is most commonly defined as the process of using computers and automation to search large sets of data for patterns and trends, turning those findings into business insights and predictions..

  • Where I can get data for data mining?

    Kaggle.
    Kaggle is one of the most popular communities for data scientists, and the site's user-published datasets are great for self-guided machine learning or analysis projects.
    You'll find a wide range of data from movie reviews to customer sales data and, fortunately, most have some of the preprocessing done..

  • Where to get data for data science projects

    Data mining is most commonly defined as the process of using computers and automation to search large sets of data for patterns and trends, turning those findings into business insights and predictions..

Datasets for Data Mining
  • Particle physics data set.
  • Physiological data set.
  • Brain-Computer Interface data set.
  • Prediction of Gene/Protein Localization data set.
  • Prediction of Molecular Bioactivity for Drug Design: Binding to Thrombin dataset.
  • The 4 Universities dataset.
  • Internet advertisements dataset.
Data mining uses algorithms and various other techniques to convert large collections of data into useful output. The most popular types of data mining techniques include: Association rules, also referred to as market basket analysis, search for relationships between variables.

How many data mining datasets are there?

There are 6 data mining datasets available on data.world.
Find open data about data mining contributed by thousands of users and organizations across the world.
Advanced Clustering Analysis Challenge -- Inspired by the Opportunity Project Use Cases .

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What is a data mining process?

The data mining process involves a number of steps from data collection to visualization to extract valuable information from large data sets.
As mentioned above, data mining techniques are used to generate descriptions and predictions about a target data set.

How Are Datasets created?

Different datasets are created in different ways. In this post, you’ll find links to sources with all kinds of datasets

Public Data Sets For Data Visualization Projects

A typical data visualization project might be something along the lines of “I want to make an infographic about how income varies across the different

Public Data Sets For Data Processing Projects

Sometimes you just want to work with a large data set. The end result doesn’t matter as much as the process of reading in and analyzing the data

Public Data Sets For Machine Learning Projects

When you’re working on a machine learning project, you want to be able to predict a column from the other columns in a data set

Public Data Sets For Data Cleaning Projects

Sometimes, it can be very satisfying to take a data set spread across multiple files, clean them up, condense them into one

Bonus: Streaming Data

It’s very common when you’re building a data science project to download a data set and then process it. However

Bonus: Personal Data

The internet is full of cool data sets you can work with. But for something truly unique, what about analyzing your own personal data

Next Steps

In this post, we covered good places to find data sets for any type of data science project

Manipulating raw data into a form that can be readily analysed

Data preparation is the act of manipulating raw data into a form that can readily and accurately be analysed, e.g. for business purposes.

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