Kaggle competition: Mercari Price Suggestion Challenge
<SOS> metric: RMSLE Kaggle data source: mercari-price-suggestion-challenge<train_model>
%%bash
python -m pip install py7zr
mkdir data
model_path_list = glob.glob('/kaggle/input/cassava-leaf-disease-tpu-tensorflow-training/*.h5')
 model_path_list.sort()
print('Models to predict:')
 print(*model_path_list, sep='
' )<train_model>
for f in ["train.tsv.7z", "test.tsv.7z"]:
archive = py7zr.SevenZipFile(".. /input/mercari-price-suggestion-challenge/{}".format(f), mode='r')
 archive.extractall(path="./data")
 archive.close()
with zipfile.ZipFile(".. /input/mercari-price-suggestion-challenge/test_stg2.tsv.zip", 'r')as zip_ref:
zip_ref.extractall("./data" )
model_path_list_2 = glob.glob('/kaggle/input/cassava-leaf-disease-training-with-tpu-v2-pods/*.h5')
 model_path_list_2.sort()
print('Models to predict:')
 print(*model_path_list_2, sep='
' )<choose_model_class>
%%bash
git clone https://github.com/joren015/MercariProject.git
cp -r MercariProject/*./
python -m pip install -r requirements.txt
<predict_on_test><EOS> 
%%bash
python main.py neural_network predict