It also supports Python numerical and scientific libraries like NumPy and SciPy. It features various classification, regression, and clustering algorithms including support vector machines, random forests, gradient boosting, k-means, and DBSCAN. The library we will use to build the recommendation model is scikit-learn, which is a free machine learning library for Python. We will then use the cosine similarity scores to recommend other listings that are most similar to the input listing. We will use the cosine similarity to calculate the similarity between the listings’ descriptions. To build the recommendation model, we will use the descriptions of the listings to build a recommendation model that will recommend similar listings based on the user’s input ID. Streamlit is great for prototyping and creating apps quickly, and it can also be used to create production-ready apps. It allows you to create web apps with just a few lines of code and without having to know any front-end technologies like HTML, CSS, or JavaScript. Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science. Quick demo of the finalised APP (image by author) Explanation of the technologies used (Streamlit, recommendation models)
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