This project uses Machine Learning methods to predict the fare of a cab travels, This project is a minimal example to learn MLops such as Docker, DVC and feature stores
To test this app locally, I would recommend you to create a virtual environment
conda create venv python=3.7
conda activate venv
git clone https://github.com/seanbenhur/cabfare_prediction.git
python app.py
python test.py=== data Datsets in CSV, serialized jobib files and parquet file
=== features Feature store repository
=== src
====== evaluate.py Code for evaluating the machine learning model
====== prepare_data.py Scripts for cleaning the data
====== train.py Script for training the LGBM model
=== model Folder containing the saved model in joblib format
=== outputs Contains the metrics saved as a JSON file
If you found any issues please feel free to contribute
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