Movie-Recommender System

A movie recommender system is a system that seeks to predict or filter preferences according to the user's choices. The created Web-App using Python is a similar system, which suggests movies based on the user's liked movies.

The model has been trained using a dataset of 5,000 movies! Find the dataset here 🔗

Download the similarity model file (pkl) from here 🔗

Check out the demo of the application on Youtube here 🔗

Use the application live here 🔗

Tech Used

Language: Python (3.9)

Front-End: Streamlit

Please Note that this technology is being used for the latest version. Further improvements in the project may result in changes in the technology used. It will be updated above as well.

Installation / Working

Requirements

beautifulsoup4==4.10.0
ipython==7.30.1
matplotlib==3.7.1
matplotlib-inline==0.1.6
numpy==1.21.5
packaging==21.3
pandas==1.3.5
pandocfilters==1.5.0
parso==0.8.3
pickleshare==0.7.5
Pillow==8.4.0
pipreqs==0.4.11
python-dateutil==2.8.2
requests==2.26.0
soupsieve==2.3.1
scikit-learn==1.2.2
streamlit==1.3.1
testpath==0.5.0
urllib3==1.26.7
xgboost==1.7.4
validators==0.18.2
virtualenv==20.13.0

Clone this repository or Download the files into your local system.

• Extract the ZIP file (if you directly download from Github Web)
• Download the similarity model file from here 🔗
• Make sure all the files are in the same folder/directory
• Open your Command Prompt (CMD) in the same directory
• Type the following command (for web app) :

streamlit run app.py

• Make sure you have streamlit installed on your local device, if not installed, type the following to install (windows) :

pip install streamlit 

Working Demo

The demo working of this web app can be found here 🔗 | Do like it, if you watch it :)

🚀 Thanks

Thanks for looking into the project and being here. Feel free to share your reviews/suggestions/remarks! :)

If you found it useful, leave a ⭐ here!

License

MIT

Ending Credits

• Made using Python and Streamlit
• @Author : Sagar Bapodara