Web scraping has been described as “the process of harvesting authentic, actionable and valid data from large databases. Generally, web scraping derives patterns and trends that exist in the data. It is important to understand that the trends and patterns may be collected and simplified as a model for decision making.
The models mined have wide applications to specific business scenarios. Such scenarios include:
- Sales Forecasting. This incorporates the prediction of sales volume and the profits that can be accrued.
- Mailing targeted to specific customers. This is meant for updating and contacting customers by sending those messages and offers.
- Product determination for selling. By web-scraping, you are likely to understand and know what kind of products you want to sell.
The building of a business model is part of a greater process that starts from the definition of a problem and the problem will be solved. This process of web scraping may be defined by the use of the following important basic steps.
This step incorporates and analyzes of business requirements, scope of the problem, the definition of the metrics that the model is being evaluated on, and the definition of the final objectives of the web scraping project. To understand this step, it is important to answer these questions;
- What are you looking for?
- What kind of data set are you after and trying to predict?
- What are the types of relationships that you are trying to find?
- How is the data you are after distributed?
- If you are dealing with columns and tables, how are they interrelated?
If the data obtained from web scraping does not offer support to the needs of users, you need to pivot your project by looking for a different redefinition.
The main reason for this step in the web scraping process is to consolidate and clean the data which is identified in the problem definition. It is important to realize that it may be scattered across a company website and likely to be stored in a number of different formats.
It is also likely to have some inconsistencies that contain flawed entries. For instance, data may attribute that a customer bought a product before the customer was actually born.
It is therefore important to note that before the building of models, it is ideal to fix such problems before starting to build such web scraping models.
In normal operations of the web scraping process, it is important to explore the data which has been scraped. It is important that you understand the data so as to make appropriate decisions when creating the models. This step of the web scraping process includes the calculation of maximum and minimum values and looking for data distribution.
In web scraping, before building your model, you need to randomly separate the data that is prepared into testing data sets and use each for separate training. The training data set is used in building the model and also testing the accuracy of the model by the creation of prediction queries.
Exploration and Validation of Models
In web scraping after building the models, it is important to explore the models which you have built and then test their effectiveness.
It is not fair to deploy a model into the production arena without first testing how the model will behave and perform. In this stage, you are likely to come up with different models and then deciding on the model that performs best.
If from all the web scraping models you have built, there is none that functions as wanted, you need to go back to the previous stage.
Deployment and Updating Models
This is the last stage in the web scraping model building process. After obtaining the web scraping models that can exist in the production environment, you have the liberty to perform many tasks, depending on your needs.
It is important to note that the creation of a web scraping model is an iterative and dynamic process. After the exploration of data, you may find it necessary to look for extra data from other sources in case the data harvested is not sufficient.
The updating of your web scraping model is of eminent importance and should be part of the deployment strategy. It is important to realize that as more data comes into the organization, you need to reprocess the models.
This is likely to improve the effectiveness of your web scraping process.