[EXE] Movie Recommendation Project

[ishan-kshirsagar0-7]

Learning Goals

Basic data wrangling, data manipulation, and basic Machine Learning concepts.

Exercise Statement

Movie Recommender is a Machine Learning / Data Manipulation based project, made using Python. It uses libraries like Pandas, Numpy, NLTK, SciKit Learn, etc. It has two types of Recommenders : A "Simple Movie Recommender", which suggests top movies based on the genre inputted by the user, and also parameters like year of its release, popularity and IMDB's Weighted Rating. The second one is "Content Based Recommender", which suggests movies similar to the movie inputted by the user, and other few parameters such as Cast, Crew, Keywords, Director, etc as well.

Prerequisites

Data preprocessing, Machine Learning, Data Visualization

Data source/summary:

https://www.kaggle.com/code/rounakbanik/movie-recommender-systems/data This is a MovieLens dataset that I found on Kaggle. The Simple Movie Recommender notebook uses a Full Version of this dataset meanwhile the Content Based Recommender uses the Compact Version of the dataset.

Solution

I have the solution, will be happy to create pull request to include the aforementioned exercise statement.

[DLesmes]

The full version of this exercise will be so great to learn 👍🏾

[ishan-kshirsagar0-7]

I actually have two Jupyter Notebooks ready for the same

On Fri, 14 Oct 2022 at 10:29 PM Diego Lesmes @.***> wrote:

The full version of this exercise will be so great to learn 👍🏾

— Reply to this email directly, view it on GitHub https://github.com/gimseng/99-ML-Learning-Projects/issues/193#issuecomment-1279250499, or unsubscribe https://github.com/notifications/unsubscribe-auth/AT6BBFQFYSHCIWAONBZBDJTWDGGOPANCNFSM6AAAAAARAECVWE . You are receiving this because you authored the thread.Message ID: @.***>