- Implementation of linear systems such as traditional and recursive LU matrix decomposition.
- "resolverLU" program that describes a matrix given to LU form and prints the result
- Matrix inverse calculation using LU and Frobenius methods.
The goal is to enhance user experience by providing personalized suggestions that are relevant and useful on an individual basis. In many cases, recommendation systems utilize machine learning algorithms to analyze data and make predictions about what each person may be interested in. These algorithms can take into account factors such as previous purchases, items liked or clicked on, and items popular among other users with similar interests.
- Suppose this dataset belongs to a store that offers different bottles of wine for sale and also has a large customer base. The owner of the wine store wants to reduce the complexity of the dataset and wishes to build a wine recommendation system for their customers. The goal of this problem is to identify which customer segment each wine belongs to in order to recommend the wine to the appropriate customer. In other words, we need to create a wine recommendation system with the aim of optimizing sales and increasing the profits of the wine store.
We are going to use Predictive and ML models for categorizing 3 types of wines using linear systems, PCA, and more to reach our goal.
