A machine learning based recommendation system using different filtering techniques.

There is a considerable volume of data made accessible to people of the modern digital age, thanks to the vast array of innovations and the enormous amount of content available over the Internet. Consequently, a disorder called "information overload" develops a mental confusion due to more information. As a result, it is quite difficult for an individual to find for and access information to make informed decisions. The amount of information that contains each unstructured and structured data and its information, has big heavily in recent days. Recommendation systems (RS) are becoming more widespread, as they are being used everywhere in the E-commerce sector and analysing each qualified and validated data to have the best recommendation. To process distributed data in this project, a large framework known as jupyter notebook is used. Unlike previous mapping features, jupyter can accommodate redundant algorithms, dynamic algorithms, and time intervals that are stripped down. Because of the rise of the internet, a helpful technology known as counselled method has evolved into a cost-effective application for forming consumer suggestions. Today, many collective recommender programmes have succeeded in fields such as film, music, and internet applications. However, there are several options to make them more useful RS. This paper implements a combined similarity-based substitute for an item-based collective filtering technique, as well as a solution to the most common cold start problem. A series of tests show that the new approach can make better recommendations than the traditional item-based collective filtering method. [ABSTRACT FROM AUTHOR]