Project Structure

• LICENSE

• Makefile

  • Makefile with commands like make data or make train

• README.md

  • The top-level README for developers using this project

• Data

  • external: Data from third party sources.
  • interim: Intermediate data that has been transformed
  • processed: The final, canonical data sets for modeling
  • raw: The original, immutable data dump.

• docs

  • A default Sphinx project; see sphinx-doc.org for details

• models

  • Trained and serialized models, model predictions, or model summaries

• notebooks

  • Jupyter notebooks. Naming convention is a number (for ordering),the creator's initials, and a short - delimited description, e.g.1.0-jqp-initial-data-exploration.

• references

  • Data dictionaries, manuals, and all other explanatory materials.

• reports

  • Generated analysis as HTML, PDF, LaTeX, etc.
    • figures Generated graphics and figures to be used in reporting

• requirements.txt

  • The requirements file for reproducing the analysis environment, e.g.generated with pip freeze > requirements.txt

• setup.py

  • makes project pip installable (pip install -e .) so src can be imported

• src

  • Source code for use in this project.

    • init.py Makes src a Python module

    • data

      • Scripts to download or generate data └── make_dataset.py

    • features

      • Scripts to turn raw data into features for modeling └── build_features.py

    • models

      • Scripts to train models and then use trained models to make predictions ├── predict_model.py └── train_model.py

    • utils

      • Scripts for helping streamlit app ├── home.py └── prediction.py
      • static ├── home.css ├── prediction.css └── main.css

    • visualization

      • Scripts to create exploratory and results oriented visualizations └── visualize.py

• tox.ini

  • tox file with settings for running tox; see tox.readthedocs.io

• config.yaml

  • yaml file containing paths and parameters

• run.yaml

  • yaml file containing files path for executing

• run.py

  • python file used run.yaml to execute files

Usage

Open Terminal or Command Prompt

git clone https://github.com/mrqadeer/movie-recommender-system-e2e-project.git

Make sure you have git installed on your machine. Otherwise download Zip file and extract it. Navigate to project directory

cd movie-recommender-system-e2e-project

Installation dependencies For executing standalone streamlit_app.py

pip install -r requirements.txt

then

streamlit run streamlit_app.py

OR Alternatively, you can run the following command to train and predict models with the Streamlit app:

python run.py 

Note: This process may take some time depending on your machine's configuration. Additionally, it will download and install the necessary nltk packages.

This will also downlaod and install nltk packages

Thank you!