click here to download project abstract/base paper of knn in machine learning
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ABSTRACT
In the field of Artificial Intelligence, machine learning offers automatic systems that learn and improve themselves from experience without explicit programming. This research work actively builds a movie recommender system using the K-Means Clustering and K-Nearest Neighbor algorithms.
The proposed work deals with the introduction of various concepts related to machine learning and recommendation system. In this work, we actively utilize various tools and techniques to build recommender systems. We delve into detailed descriptions of various algorithms, including K-Means Clustering, KNN, Collaborative Filtering, and Content-Based Filtering.
Further, after studying different types of machine learning algorithms, there is a clear picture of where to apply which algorithm in different areas of industries such as recommender systems, e-commerce, etc. This section actively illustrates how we utilize the implementations and workings of the proposed system to implement the movie recommender system.
The proposed system details various building blocks, such as architecture, process flow, pseudo code, implementation, and the working of the system in comprehensive detail. Finally, in this work for different cluster values, different values of Root Mean Squared Error are obtained.
In this proposed work as the no of clusters decreases, the value of RMSE also decreases. The best value of RMSE obtained is 1.081648. The results given by the proposed system are better than the existing technique on the basis of RMSE value.
