Automation is the need of the hour. The very goal of its existence is the reduction of human errors from the day-to-day processes and to increase the value of human labor in the process. Today’s automation tools are generally powered by machine learning and artificially intelligent entities that require a lot of relevant data to train. In the case of ML and AI the collection and preprocessing of these data is extremely important as the validity of output depends on the relevance and authenticity of inputs. While collecting data there can be a plethora of inconveniences a collector can face.

  • Noises and irrelevant information can be present, affecting the training and analysis in unforeseen ways.
  • Certain pieces of information can be missing from a data set.
  • Biases can be present in a data set.

The preprocessing methods and approaches concentrate on alleviating these ordeals so that the analysis is convenient and accurate. This collection and data preprocessing in machine learning determine the accuracy and relevance of an ML-based automation entity.

Data collection

We live in a time when data is abundant more than anything. Just a couple of years ago we did not have the capabilities for efficient handling and storing of data. But with the advancements achieved in computer sciences and allied disciplines we can utilize the data in any manner we fancy.

  • The cheapest and easiest way of collecting data is by accessing freely available public datasets. A machine learning tool developer can get their hands on any data set that might help them develop a specific model. Thanks to the abundance, almost all kinds of data sets are freely available and a search is usually never disappointed.
  • The other inexpensive method is scraping by extensive browsing. This manual collection method is dependent on searching the internet for relevant sources of data and receding it in the desired form.
  • Apart from these two methods, a developer can get access to data by purchasing the same from a reputed organization selling the same with all the ethical limitations in mind.

Data processing

Data processing is done for making a data set readable for an automation entity. A machine learning or artificial intelligence tool is usually dependent on codes that help them recognize certain formats. Thus the data set they are provided to work with must be of a favorable format. In addition to that, a preprocessing session concentrates on the removal of noises from a data set and accounts for all the missing values that can affect the outcome of a machine learning tool.

  • Imputation of data

Data imputation can be done in a plethora of circumstances. The process concerns missing values or features from a dataset. These missing values are often substituted in a manner unacceptable given the required accuracy of the analysis. Thus data imputation is performed. Imputation might include avoiding entire arrays based on the presence of any value at all. Or by simply adding a calculated value to a blank cell.

  • Reduction of dimensionality

Reduction of dimensionality is performed for easy visualization of data. Most of the time, the process involves the preservation of only the most relevant dimensions of a data set. The reduction is performed based on importance and in a calculated manner.

  • Scaling of features

Features can be complicated to handle when it comes to a data set with wide-ranging features. Features with huge values can dominate the lower values. And scaling is the only solution to the problem.

Standardization is often done to scale the low values and segregate them in classes or by representing them through another dimension.

Classification using weight variables is another option when it comes to setting different sets at par with each other. Assigning weight however is not easy and requires a lot of mathematical prowess.

Balancing the data

Data imbalance can occur when a feature is circumstantially undervalued. Upscaling and downscaling of the values are always an option but they lack the promise of perfection. The process requires undersampling and oversampling in accordance with the needs and performing the necessary adjustments with human attention.


Machine learning now is a force to be reckoned with. And the responsibilities and ML entity is expected to perform are of paramount importance. Thus perfection in preprocessing and ethical data collection is crucial. As the collection and preprocessing alone can determine the outcome of a process. And in turn, the future of humanity.