Data preparation is the key step of data workflow<\/strong> to make a machine learning<\/strong> model capable of combining data captured from many different sources and providing meaningful business insights.<\/span><\/p>\n

Getting good at data preparation is a challenge to those working with data. Here\u2019re some of the best practices to prepare the data effectively.<\/p>\n

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• It\u2019s easy to dive into preparing data without thinking about the source of the data and the reliability of that source. Often, the quality, format, and accessibility of the data source play a big role in the analytics. Data sourcing can be broken down into three parts namely defining the data required for a business task, identifying potential sources of the data, and confirming the data source.<\/li>\n
• Data profiling plays a crucial role in data preparation. It\u2019s used to decide if a data source is even worth including in a machine learning<\/strong><\/li>\n
• In this age of abundant data, preparing large datasets can be time-consuming and cumbersome. So, it\u2019s recommended to start with a random sample of the data for data preparation. Creating data preparation rules on a valid sample of the data will greatly expedite the time-to-insight.<\/li>\n
• Based on the knowledge of the business analytics goal, different data cleansing strategies should be experimented upon to get the relevant data. Here again, a statistically-valid, small sample should be used to start with the experimentation.<\/li>\n<\/ul>\n

  Data preparation may seem to be messy but it\u2019s ultimately a valuable and rewarding exercise. Guided by solid data governance principles and armed with profiling tools, sampling techniques, visualization etc, data workers can develop effective data preparation approaches.<\/p>\n

  4.3- Exploratory data analysis (EDA)<\/em><\/h4>\n

  <\/p>\n

  Exploratory data analysis<\/strong><\/em> is a crucial step in any data analysis process. In this data workflow<\/strong> step, the contents of a dataset are understood and summarized by data workers, typically with a specific question. This is done by taking a broad look at trends, patterns, unexpected results, outliers and so on in the existing data. Here, quantitative and visual methods are used to highlight the story that the data is telling.<\/p>\n

  Let\u2019s have a look at how exploratory data analysis helps data workers.<\/p>\n

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  • Identifying errors that have been made at the time of data collection and missing data areas.<\/li>\n
  • Identifying the most influential variables in a dataset.<\/li>\n
  • Mapping out the underlying structure of that data.<\/li>\n
  • Listing and highlighting outliers and anomalies.<\/li>\n
  • Estimating parameters, defining margins of error, or determining confidence intervals.<\/span><\/li>\n
  • Testing previously proposed hypotheses.<\/li>\n<\/ul>\n

    The key purpose of EDA is to examine the dataset while eliminating any assumption about what it may contain. By eliminating assumptions, data workers can identify potential causes and patterns for observed behaviors. Any of the two types of assumptions are made about raw datasets by the analysts \u2013 business assumptions and technical assumptions.<\/p>\n

    Business assumptions can often remain unrecognized and can impact the business problem without the researcher being aware of them consciously. Technical assumptions can be like no data is anyway corrupted in a dataset or no data is missing from it, which have to be correct so that the insights gained from statistical analysis prove to be true later.<\/p>\n

    5- Other stages in a machine learning model<\/em><\/strong><\/h3>\n

    The<\/em> main goal of using the above data workflow<\/strong> steps is to train the highest performing model possible, with the help of the pre-processed data.<\/span> The types of methods used to cater to this purpose include supervised learning and unsupervised learning.<\/p>\n

    In the former, the machine learning<\/strong> model is provided with data that is labeled. In unsupervised learning, the model is provided with uncategorized, unlabeled data and the algorithms of the system act on that data without prior training.<\/p>\n

    Next comes the evaluation stage, which is an integral part of a machine learning<\/strong><\/em><\/a> model development process. It helps data workers find the best model that represents the data and evaluate how well that model will perform in the future.<\/p>\n

    Final takeaway<\/em><\/strong><\/h3>\n

    <\/p>\n

    So<\/em>, we learned about the data workflows<\/strong> for a machine learning<\/strong> model and discussed various steps in order to understand the topic better. It\u2019s important to remember that a machine learning<\/strong> model is only as good as the data it\u2019s provided with and the ability of the algorithms to consume it.<\/p>\n

    In data science<\/strong><\/em><\/a>, one of the most important skills is the ability to assess machine learning<\/strong>.<\/span> In the field of data science<\/strong><\/em><\/a>, there isn\u2019t any shortage of techniques to perform a wide range of high-end tasks. However, what it probably does lack is how to solve non-standard business problems and this is where machine learning<\/strong> techniques fit in the picture perfectly.<\/p>\n