Collecting data for Model building

In the previous blog, we understood the Machine learning development cycle . There we saw data collection as the first process in the lifecycle. In this blog, we will explore the importance of data collection, the types of data needed for ML model building, and methods to get data. Data collection is a crucial step in building a successful machine learning (ML) model. The quality and quantity of data used to train an ML model directly affect its accuracy and generalizability.

Why is data collection important?

Data collection is the foundation of ML model building. The accuracy and generalizability of an ML model depend on the quality and quantity of the data used to train it. High-quality data enables ML models to learn patterns and relationships accurately, resulting in better predictions and decisions. Collecting the right data is essential to avoid biases and ensure that the model performs well in different scenarios.

Types of data needed for ML model building

1. Structured data - Structured data is organized in a tabular format and is easy to analyze. This type of data is commonly found in spreadsheets, databases, and transactional systems. Structured data is well-suited for regression and classification problems.

2. Unstructured data - Unstructured data refers to data that does not have a pre-defined data model or format. Examples of unstructured data include text, images, audio, and video. Unstructured data requires special processing to extract meaningful information, making it more challenging to work with than structured data.

3. Semi-structured data - Semi-structured data has some organizational structure, but not as rigid as structured data. Examples of semi-structured data include JSON, XML, and CSV files. Semi-structured data can be easier to work with than unstructured data as it has some level of organization.

Methods to get the data

1. Web scraping - Web scraping involves extracting data from websites using automated tools. Web scraping can be useful for collecting data from social media platforms, news websites, and e-commerce sites. Web scraping can be a powerful method for data collection but requires expertise in programming and web development.

2. Public datasets - Public datasets are pre-collected and publicly available for research and analysis. These datasets can be a great source of information for ML model building. Some examples of public datasets include the MNIST database for handwritten digits and the ImageNet dataset for object recognition.

3. Surveys - Surveys can be a useful way to collect data directly from users or customers. Surveys can provide valuable insights into user behavior and preferences. Online survey tools such as SurveyMonkey and Google Forms can make it easy to design and distribute surveys.

4. Data marketplaces - Data marketplaces are platforms that connect data providers with data consumers. These marketplaces offer a range of data sources, including public datasets, commercial data, and user-generated content. Some examples of data marketplaces include DataMarket and Kaggle.

Conclusion

Data collection is a critical step in building an ML model. The quality and quantity of data used to train an ML model directly affect its accuracy and generalizability. Structured, unstructured, and semi-structured data can be used for ML model building. Methods to get data include web scraping, public datasets, surveys, and data marketplaces. Careful consideration of the data sources and collection methods is necessary to ensure that the data used for ML model building is accurate, unbiased, and suitable for the intended application. Hope you got value out of this article. Subscribe to the newsletter to get more such blogs.

Thanks :)