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The objective of this project is to build a kNN-based recommender system in order to predict the top 5 movie based on a given movie, in this case "The Post". As there is no need for classification or regression, the nearestNeighbors model and neighrbors() method are used to find the 5 most closely related films.

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kNN-based Recommender System

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A recommender system, or a recommendation system, is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item. It is widely used to make recommendations based on a user's past behaviours and preferences. Recommender systems are often based on the kNN (k-nearest neighbor) algorithm, which makes recommendations based on shared similar features. It can be used to solve both classification and regression problems.

Project Description

The objective of this project is to build a kNN-based recommender system in order to predict the top 5 movie based on a given movie, in this case "The Post". As there is no need for classification or regression, the nearestNeighbors model and neighrbors() method are used to find the 5 most closely related films. This project does not include model performance testing due to the small size of the dataset.

Steps

  1. Data Exploration
  2. Building a kNN-based Recommender System
  3. Getting Recommendations

Requirements

Python. Python is an interpreted, high-level and general-purpose programming language.

Integrated Development Environment (IDE). Any IDE that can be used to view, edit, and run Python code, such as:

Packages

Install the following packages in Python prior to running the code.

import pandas as pd
import numpy as np
from sklearn.neighbors import NearestNeighbors

Launch

Download the Python File CA06_kNN_based_Recommender_Engine and open it in the IDE.

Authors

Silvia Ji - GitHub

License

This project is licensed under the MIT License.

Acknowledgements

The project template and dataset provided by Arin Brahma at Loyola Marymount University.

About

The objective of this project is to build a kNN-based recommender system in order to predict the top 5 movie based on a given movie, in this case "The Post". As there is no need for classification or regression, the nearestNeighbors model and neighrbors() method are used to find the 5 most closely related films.

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