This project aims to use modern and effective techniques like KNN which groups together the dataset and providing the comprehensive and generic approach for recommending wine to the customers on the basis of certain features. This will help the shop owners to already know about the demand of the wine and accordingly the stock will be updated. This will facilitate in increasing the profit of the shop owners.you can get the dataset from uci repository.
• k- Nearest Neighbour (k-NN) classification technique:
k-NN is a non- parametric method used for classification. In this classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. It is the simplest algorithm among all the machine learning algorithms.
• Support Vector Machine (SVM) classification Technique:
Support Vector Machine (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However, it is mostly used in classification problems. In this algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate. Then, we perform classification by finding the hyper-plane that differentiate the two classes very well