Movies Recommendation engine

Recommendation System is a Artificial intelligence (AI) based system that seeks to predict or filter preferences according to the user’s choices. Recommender systems are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general.

Recommender systems produce a list of recommendations in any of the two ways –

• Collaborative filtering: Collaborative filtering approaches build a model from user’s past behaviour (i.e. items purchased or searched by the user) as well as similar decisions made by other users. This model is then used to predict items (or ratings for items) that user may have an interest in.
• Content-based filtering: Content-based filtering approaches uses a series of discrete characteristics of an item in order to recommend additional items with similar properties. Content-based filtering methods are totally based on a description of the item and a profile of the user’s preferences. It recommends items based on user’s past preferences.

  In this project, you learn how to:

• Select a machine learning algorithm
• Prepare and load your data
• Build and train a model
• Evaluate a model
• Deploy and consume a model

 

 

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Dataset and Features

We use the Movie Lens dataset available on Kaggle 1 , covering over 45,000 movies, 26 million ratings from over 270,000 users. The data is separated into two sets: the first set consists of a list of movies with their overall ratings and features such as budget, revenue, cast, etc. After removing duplicates in the data, we have 45,433 different movies. Table 1 is the top 10 most popular movies by their weighted score, calculated using the IMDB weighting 2 . We randomly split this dataset into an 80% training set and 20% test set for content-based filtering.

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libraries that we will need for this program

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

from sklearn.feature_extraction.text import countvectorizer

from sklearn.matriices.pairwise import cosine_similarity