- When a user enters a movie title into the input box, this recommendation system swiftly generates suggestions for other movies that may align with the user's preferences.
- This repository contains all the codes and resources used to build and utilize the recommendation system.
- Data Exploration: Comprehensive analysis to understand the MovieLens 25M dataset's structure and distribution.
- Search Engine: Building a search engine to find a specific movie title in the dataset.
- Recommendation Engine: Creating a recommendation engine to suggest specific movies based on user preferences and movie ratings.
- Reading in movie data with pandas.
- Cleaning movie titles with regex.
- Creating a TF-IDF matrix. (Time Frequency-Inverse Document Frequency)
- Creating a search function.
- Building an interactive search box with Jupyter.
- Reading in movie ratings data.
- Finding users who liked the same movie.
- Finding how much all users like movies.
- Creating a recommendation score.
- Building a recommendation function.
- Creating an interactive recommendation widget.
You can find the code for this project here:
- Jupyter Notebook / Google Colab
- Python 3.10.12
- Python packages
- Pandas -
pip install pandas
- Numpy -
pip install numpy
- Scikit-learn -
pip install scikit-learn
- Regex -
pip install regex
- Pandas -
You can download the dataset files used in this project here: