How do you choose the best data mining technique for your analytics project?

Often, the combination of both approaches (ML and Data mining) may be the most effective solution to extract valuable insights from the dataset, however, the choice is dependent on the project goals (predictive vs. exploratory), data type (structured vs. unstructured), data availability (labeled vs. unlabeled), interpretability, and resource constraints.

From my experiences, what you should do is this. Start by clearly defining your problem and understanding your data. Select a technique that fits your data type and objectives, such as clustering, classification, or regression. Finally, test and refine the chosen method based on its performance metrics to ensure it effectively meets your project's goals.

While it's true that data mining and machine learning share similarities, their distinction lies in how they're applied within analytics projects. Data mining is more exploratory in nature, focusing on extracting patterns or hidden relationships within vast datasets, often without a predefined outcome. On the other hand, machine learning brings in the ability to automate and enhance this process by learning from the data and making predictions. The choice between the two often hinges on whether the goal is discovery (data mining) or prediction and optimization (machine learning). It’s important to understand that integrating both can significantly improve the depth of insights and efficiency, depending on the project’s objectives.

In my experience, one tends to forget the actual purpose of data mining by focusing too much on the data and their inability to link it to the business objectives. While machine learning is an extension of data mining, without an understanding of the business and the data, the extension is just a waste of time. Yes, you can run the data and make forecasts, but then you will still have to do a debrief. Although ML can save thousands of human hours when analyzing complex data sets.

In my experience data as a product is something organisations should focus on. Getting a high quality data is a debatable topic because in machine learning some of the records are dropped due to presence of invalid or garbage data but sometimes the impact of those records is overseen. I believe data quality plays very important role to trust data driven decisions.

Absolutely, data needs to be wrangled and cleaned before using it to fit any model. Also, it is very important that the data is free from any bias so that the predictions made by the model are accurate.

A critical part of this process is to make sure you aren't making these determinations in a monologue with yourself, but rather a dialogue with the business. Make sure you are familiar with the business case and context of the question, either through interviewing stakeholders/customers or having domain experience yourself. Before concluding that particular approaches, dimensions, or datasets are in or out of scope, you need to ensure you have a clear problem statement (or statements) in place.

First, ask yourself: Is my query addressing an 'actionable' business question? Second, remain realistic in your expectations of what your data can answer, as well as efforts required. Too many times I've seen these two facets of data work utterly ignored. There are plenty of methods for messing about with your data, and they all have their places. But if you're not chasing 'actionable' results or you're blowing time and resources on too granular a prize, you're not where you should be.

While it's great to have competency in these and other tools, I would stress that tools are simply a means to an end: delivering business value through data analysis and insights generation. I have seen hugely impactful solutions delivered using Excel spreadsheets and a Word document; conversely, I've seen analytics projects lose trust and fail to delivery useful insight even when backed by hundreds of lines of code and elaborate dashboards. If you apply the most cutting-edge tools to the wrong business questions, you are going to end up with disappointed stakeholders, no matter how impressive your technical skills.

The resource and tool consideration is pivotal in analytics, as it directly impacts the feasibility and sophistication of the project. Leveraging open-source tools like Python or R can significantly reduce costs while providing robust capabilities. However, it's essential to balance the allure of advanced techniques with the practicality of project constraints. Investing in skill development or selecting scalable tools can offer long-term benefits, enabling teams to adapt to evolving data challenges and maintain a competitive edge in the analytics landscape.