Movie Recommender System

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  Screenshot of app in use

Inspiration

-Search Recommendation Systems (ex:youtube)

-Data Collection and Filtering

What it does

-Uses a k-NearestNeighbors model to recommend titles based on a title input

-There is also an option to select a randomized movie

How I built it

-Used beautifulsoup library to download data locally

-Used sklearn library to generate suggestions

-Using Streamlit to deploy the app and build a user interface

Challenges I ran into

-Figuring out how to implement the sklearn library

-Resolving issues with filtering DataFrames

Accomplishments that I am proud of

-I learned how to use Streamlit

-I created an app and met the minimum project requirements within a short timeframe

-It works decently

What we learned

-It is not good to procrastinate

-Data cleaning and analyzing the data is important to utilize it in a way that makes sense

What's next for Movie Recommender System

-Have better data filtering

-Add more data points

-Allow for a movie watch list and recommend based off of those instead of just a single movie

Built With

• beautiful-soup
• pandas
• python
• sklearn
• streamlit