<jupyter_start><jupyter_text>House Prices: Advanced Regression Techniqueshttps://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview  Load data<jupyter_code>import pandas as pd
train_df = pd.read_csv('data/train.csv', sep=',', header=0)
test_df = pd.read_csv('data/test.csv', sep=',', header=0)
combine = [train_df, test_df]
[combine[0].shape, combine[1].shape]
train_df.head()
train_df = train_df.fillna(0)
train_df.isnull().sum().sum()
from supervised.automl import AutoML
X_train = train_df.drop(["SalePrice", "LotFrontage"], axis=1)
Y_train = train_df["SalePrice"]
automl = AutoML(results_path="models", 
                total_time_limit=60 * 60 * 12,
                algorithms=[
                    "Baseline",
                    "Linear",
                    "Decision Tree",
                    "Random Forest",
                    "Extra Trees",
                    "LightGBM",
                    "CatBoost",
                ])
automl.fit(X_train, Y_train)<jupyter_output>Create directory models
AutoML task to be solved: regression
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{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '387699', 'license': 'Apache-2.0', 'n_tokens_mistral': 359, 'n_tokens_neox': 336, 'n_words': 63, 'path': 'house_price/mljar-supervised.ipynb', 'repo_name': 'dariuszzbyrad/kaggle_challenge', 'size': '1061'}