The point is small – to learn how to draw in a multidimensional space Doesn’t work? Well let’s approach the problem from the other side. Can a computer calculate the distance between a pair of points? Easily. This means that he will be able to select areas of closely spacd points. Now let’s take some of the most. Crowdd of these areas and calculate the center of mass of each of them. These will be our types – the average most typical that do not exist in nature who? And here you already have to roll up your sleeves and manually mark each cluster – students pensioners mortgage holders etc.

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This approach is calld clustering a cluster is in fact just an area of ​​clumping of points in space. By dividing the bulk of customers into clusters with its help and identifying the characteristics of a typical representative of each cluster we can consciously develop products focusd on customers from a particular cluster – attractive to them and beneficial Azerbaijan Email List to us. So we have products focusd on our customers. Great! How will we offer each client from each cluster all the products for his cluster that he does not yet have? Every n days until he buys? So we run the risk of being too annoying Or on the contrary offer each product to the client only once? This is also not optimal – today he does not ned a loan but in six months he will ned it By that time he will have long forgotten about our offer or think that it is no longer relevant.

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If only we could learn how to offer the client exactly the product that he needs and at the exact moment when he needd it This brings us to another common machine learning task – classification. Classification What is classification in principle it is clear intuitively. But let’s take an example anyway. Everyone knows such a task as OCR – optical BLB Directory character recognition that is the recognition of handwritten letters and numbers. Suppose we are engagd in the automatic entry into a computer database of applications written by hand on letterheads with spaces for letters and numbers.