This project was coded in a modular format with utility scripts for ease of mainenance, understandability and reusability. For the ability to execute them in one shot, all the utility scripts were copied to final.ipynb
.
- Assuming the dataset is present in Kaggle environment
/kaggle/input
*
- Upload notebook in
final.ipynb
to Kaggle - Execute all cells sequentially
- Prediction file with timestamp can be found in
/kaggle/output
directory
- Update
PROJECT_HOME
variable to point root of project - Download and store the train, test data in the directory
$PROJECT_HOME/input/fall2021-inf8245e-machine-learning
asx_train.pkl, y_train.pkl, x_test.pkl
- Notebook is present in
$PROJECT_HOME/code/final.ipynb
- Execute all cells sequentially
- Prediction file with timestamp can be found in
$PROJECT_HOME/output
directory