Kaggle competition: BNP Paribas Cardif Claims Management
<SOS> metric: LogLoss Kaggle data source: bnp-paribas-cardif-claims-management<predict_on_test>
!pip install -q git+https://github.com/mljar/mljar-supervised.git@dev
probability, classification = learn.get_preds(ds_type=DatasetType.Test)
 test.has_cactus = probability.numpy() [:, 0]
test.head()<save_to_csv>
import pandas as pd
from supervised.automl import AutoML
test.to_csv("submission_2.csv", index=False )<import_modules>
train = pd.read_csv(".. /input/bnp-paribas-cardif-claims-management/train.csv.zip")
 test = pd.read_csv(".. /input/bnp-paribas-cardif-claims-management/test.csv.zip")
 sub = pd.read_csv(".. /input/bnp-paribas-cardif-claims-management/sample_submission.csv.zip")
 x_cols = [f for f in train.columns if "v" in f]
import pandas as pd
import numpy as np
from PIL import Image
from tqdm import tqdm
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Conv2D, MaxPool2D, Dropout, BatchNormalization, Dense, Activation, GlobalAveragePooling2D
from keras.models import Model,Sequential
from keras.regularizers import l2<load_from_csv>
automl = AutoML(
 total_time_limit=8*3600,
optuna_time_budget=1800,
mode="Optuna", 
)
 automl.fit(train[x_cols], train["target"] )
<choose_model_class><EOS> 
pred = automl.predict_proba(test)
 sub["PredictedProb"] = pred[:, 1]
sub.to_csv("./1_submission.csv", index=False )