This project was part of my Machine and Deep Learning course at Ecole Polytechnique Paris
How to run the code:
- If you are using google collab: Make sure that the following lines are uncommented
from google.colab import drive drive.mount('/content/drive')
To locate the "train.csv" file locate it in your google drive and copy the path in pd.read_csv
-If you are using Jupyter Notebook: Make sure that the following lines are commented
from google.colab import drive drive.mount('/content/drive')
Make sure "train.csv" and "evaluation.csv" are in the same directory where you are working
After loading the data, you can choose which model to run (we submitted 3)
-Pipeline architecture: (Cell number 3) (If trained on the whole data it takes a LOT of time, at least on CPU) -Neural Network: (Cell number 5 to 10) (If trained on the whole data it takes a LOT of time, at least on CPU) -XGBoost: (Cell number 11) (Can be trained on the whole data)
Make Sure to download all the required packages and versions.