A recommender system to recommend foods with the recipe based on the food you have already tried.
├── LICENSE
├── 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.
│
├── 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
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
Clone this repo in your local machine. Create a virtual environment to install the packages like this,
> virtualenv venv
After it successfully creates virtual environment then activate it.
> source venv/bin/activate # for linux
> .\venv\Scripts\actiavet # for windows
Now install all the require packages from requirements.txt file like this,
> pip install -r requirements.txt
This repo already contains the data in data/raw/data.csv which you can directly use. Or you can scrape the data directly from the source using the script.The orinal way to generate a dataset is to collect a data from source and store it in the sqlite database & then export the dataset into .csv file.
To scrape the data from source, run this command.
> python -m src.data.scrapper
To create the dataset as .csv file run this command,
> python -m src.data.make_dataset
To train the model you can start the training by using this command,
> python -m src.models.train_model
All the in between steps like pre-processing,feature engineering, etc will be performed automatically. All the generated models will be saved in models directory.
The recommender model will be created by using the similarity matrix method.
Don't have any visualization right now but you can add your own if you want.
All the operations will be logged in the debug.log file which will be generated automatically once you start running the code.