The best way to make the most of Data Mining

1. Get your problem statement first. People might think that you start with the data. No. You start with a problem. Is your problem retaining the customer and do you want to understand at which point they are abandoning the cart? Or do you want to understand whether organic hits are too low? Such problem statements give you a clear idea about what to look for in your data. It is ambitious to start with your data, and then try to find what problems it can help you solve. But this reverse process might backfire and you might end up not finding either the solution or the problem. To make sure that your data mining project is a success, it is best to take on projects which will affect the business.
2. This way you can do a trial run once your results are out and then keep making minor adjustments to models. And predictive engines to best suit the problem statement. Also starting with the data without a problem statement results in a higher amount of time spent only on data exploration, without focusing on a business problem that you can solve. Using a single data source is not a great idea if you want your data mining project to have minimal errors. Instead, you should use data from many sources, so that you can cover more ground, and so that you can use data from one source to confirm another. Say you are studying customer behavior when adding items to the cart. It is important to cover people from different places, economic backgrounds, ages, sex, and more. Leaving out any single group may make the study skewed and give you a biased model. Hence, you might need to get data from different eCommerce sites.
   
3. When companies want to start using data, they usually look inside to use data that are already stored in internal systems and lying unused. While using this data to work on a project might seem appealing, using only internal data will bind you to a very small dataset. Recommended that you get data from external, verified sources that you can incorporate into your project to improve your model.
   
4. A sampling strategy is a must. You need to make sure that you have separate training and testing sets, and both sets need to randomize so that your model doesn’t get biased. Always have an extra holdout set for backup. When you keep training your model on new data, you need to test it on the holdout set to make sure that it has not gotten biased or skewed.
   
5. Time spent on a wide variety of tasks before building your final model. Data needs cleaning, many algorithms need testing to find which one works best with the data present. Throwing data from different sources together and then testing many models. This can help you in identifying the best model. It may take time but is important to make sure that the future predictions made using the data mining project are close to real values. Skipping these parts may mean you are missing out on important insights. Hidden in your data that might enable you to make better decisions on the future steps in your project.
   
6. Make sure that your model gets trained on the go. While you can build a model and let it be, data mining projects are usually live systems, where the model keeps learning from newer data feeds. This helps keep the model updated with new data and avoids biasing.
   
7. Building an ambitious data mining project would not make much sense. Unless you can showcase your findings to the business team or the world outside. For this, you need to convert the extracted usable information into a readable and easy-to-understand format. Also, data mining projects should not end up only as R&D projects that get taken down after months of inactivity. They should immediately deploy on live systems. This can benefit the business and you can understand it’s shortcomings and keep improving.

1. Pattern recognition is one of the earliest and most used techniques. Do people from urban households spend more on electronics? In that case, you might need to make sure electronic gadgets stocked in urban warehouses. Such patterns and their resulting inferences need analysis and application so that companies can increase their profits while becoming more efficient. You can also find other patterns hidden in the data that you can use to reduce your costs. For example, there can be a specific time of the day when your website might see a spike in traffic. If you find this pattern in the data, you can increase your server capacity during that time and reduce it for the rest of the day. This way you would save a lot of money.
   
2. Classification another common algorithmic solution used on massive datasets. Usually, used to group sets of data. For example, if you have a dataset with a million user-data, and you want to sort them based on how often they transact online. You would classify them under- low, medium, and high.
   
3. Another algorithm that is usually used in recommender engines (be it on Amazon or Netflix) is association. Using it, similar products shown to us, when we are browsing an item. Also if we are on the checkout stage of a product, other products that are “usually bought together”. All these are the results of association algorithms that read human data on the internet and find repeating patterns.
   
4. The algorithm that we usually associate with data mining- prediction, is also one that is easiest to get wrong. It’s also the most used algorithm by business teams, who want to make predictions of customer behaviors or the company financials in the upcoming months.

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