Movie-Recommendation-System

In this implementation, when the user searches for a movie we will recommend the top 5 similar movies using our movie recommendation system.

About

We will focus on providing a basic recommendation system in this notebook by recommending items that are most similar to a specific item, in this case, movies. Keep in mind that this is not a complete recommendation system; to put it another way, it simply shows you which movies/items are most similar to the one you selected.

Approach:

• Here I have used two recommendation system
  • Correlation:
  • Cosine simalarity :
    • Similarity is the cosine of the angle between the 2 vectors of the item vectors of A and B
    • Closer the vectors, smaller will be the angle and larger the cosine

Dependencies

 scipy
 Pandas
 Scikit-Learn
 Numpy
 python 3.9

Usage

Just run jupyter notebook in terminal and it will run in your browser.

Install Jupyter here i've you haven't.

Steps to run this notebook in your system:

• Clone or download the repo.
• Open command prompt in the downloaded folder.

Data set:

• The MovieLens DataSet
• Download from MovieLens 100K dataset can be downloaded from http://grouplens.org/datasets/movielens/100k/