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https://github.com/vubacktracking/movie-recommeder-system-content-based
Movie Recommender System Using Content-Based Method
https://github.com/vubacktracking/movie-recommeder-system-content-based
api content-based-recommendation movie-recommendation python recommender-system streamlit
Last synced: about 1 month ago
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Movie Recommender System Using Content-Based Method
- Host: GitHub
- URL: https://github.com/vubacktracking/movie-recommeder-system-content-based
- Owner: VuBacktracking
- Created: 2023-10-27T08:58:41.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-02T06:22:08.000Z (over 1 year ago)
- Last Synced: 2024-11-10T01:13:12.291Z (3 months ago)
- Topics: api, content-based-recommendation, movie-recommendation, python, recommender-system, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 13.9 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Movie Recommendation System using Content-Based
I used Content-Based Method in order to create a Movies Recommender System. This is a web application that can recommend various kinds of similar movies## Dataset has been used:
* [TMDB Dataset link](https://www.kaggle.com/tmdb/tmdb-movie-metadata?select=tmdb_5000_movies.csv)
## Demo:
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## How to run the app
### STEPS:Clone the repository
```bash
git clone https://github.com/VuBacktracking/Movie-Recommeder-System-Content-Based.git
```
### STEP 01- Create a conda environment after opening the repository```bash
# you can use the other version of python but at least 3.7.10
conda create -n movie python=3.7.10 -y
``````bash
conda activate movie
```### STEP 02- install the requirements
```bash
pip install -r requirements.txt
``````bash
#run this file to generate the modelsMovie RecSys.ipynb
```Now run,
```bash
streamlit run app.py
```*Note*: My [`frontend`](https://github.com/VuBacktracking/Movie-Recommeder-System-Content-Based/tree/main/frontend) folder was donwloaded from https://github.com/DenizD/Streamlit-Image-Carousel