This repository contains a Data Mining mini project on content based movie recommender system using Neural Networks as an assignment for a data science undergraduate module at SLIIT.
About the Dataset
The main dataset is from MovieLens contains 27,000,000 ratings and 1,100,000 tag applications applied to 58,000 movies by 280,000 users. Includes tag genome data with 14 million relevance scores across 1,100 tags. Last updated 9/2018.
Document - https://mysliit.sharepoint.com/:w:/s/FDMMiniProject22/EcBvys2HgtBMmZbxeu9GtkEB18BtzlEBWy3Su4Tih4YDJw?e=0dymJ1
APP link :- https://darklaneanjana-fdm-mini-project-streamlit-app-p6gs5a.streamlitapp.com
Dataset :- https://grouplens.org/datasets/movielens/latest
This section shows the list of major frameworks that we have used to built our project .
- Language : Python 3.10
- App framework : Streamlit
- DNN Engine : TensorFlow
In order to be fully funtional and uprunning the following should be followed
The following requirements should be fullfilled
- Python 3.10 install
- Creating a Python virtual environment to install python modules
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Clone the repository
git clone https://github.com/Darklaneanjana/FDM_Mini_Project.git
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Install all the python modules
pip install -r requirements.txt
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run Movie_Recommender.ipynb(optional)
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Run the server
streamlit run streamlit_app.py
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Clone the project
git clone https://github.com/Darklaneanjana/FDM_Mini_Project.git - Create your Feature Branch
git checkout -b feature/AmazingFeature - Commit your Changes
git commit -m 'Add some AmazingFeature' - Push to the Branch
git push origin feature/AmazingFeature - Open a Pull Request
This is a project done for the Fundamentals of Data Mining module of BSc (Hons) in Information Technology Specialising in Data Science, in Sri Lanka Institute of Information Technology