Where can I get the data for machine learning?

Popular sources for Machine Learning datasets

• Kaggle Datasets.
• UCI Machine Learning Repository.
• Datasets via AWS.
• Google’s Dataset Search Engine.
• Microsoft Datasets.
• Awesome Public Dataset Collection.
• Government Datasets.
• Computer Vision Datasets.

How do you gather data for ML project?

Preparing Your Dataset for Machine Learning: 10 Basic Techniques That Make Your Data Better

1. Articulate the problem early.
2. Establish data collection mechanisms.
3. Check your data quality.
4. Format data to make it consistent.
5. Reduce data.
6. Complete data cleaning.
7. Create new features out of existing ones.

How do you collect data sets?

This process consists of the following five steps.

1. Determine What Information You Want to Collect. The first thing you need to do is choose what details you want to collect.
2. Set a Timeframe for Data Collection.
3. Determine Your Data Collection Method.
4. Collect the Data.
5. Analyze the Data and Implement Your Findings.

What makes a good ML dataset?

What factors are to be Considered when Building a Machine Learning Training Dataset? You need to assess and have an answer ready for these basic questions around the quantity of data: The number of records to take from the databases. The size of the sample needed to yield expected performance outcomes.

How do I create a dataset for machine learning?

Steps for Preparing Good Training Datasets

1. Identify Your Goal. The initial step is to pinpoint the set of objectives that you want to achieve through a machine learning application.
2. Select Suitable Algorithms. different algorithms are suitable for training artificial neural networks.
3. Develop Your Dataset.

Where can I find data?

11 websites to find free, interesting datasets

• FiveThirtyEight.
• BuzzFeed News.
• Kaggle.
• Socrata.
• Awesome-Public-Datasets on Github.
• Google Public Datasets.
• UCI Machine Learning Repository.
• Data.gov.