<jupyter_start><jupyter_text>Kaggle - WiDS Datathon 2022 Modelo Nº1 Description:- All the features- The entire dataset for train (not separation train/test)- Catboost- Hiperparameters by default- Long value of iterations: 10000- Public Score = 42.525  1. Modules<jupyter_code>import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

import warnings
warnings.filterwarnings('ignore')

#model
from catboost import CatBoostRegressor<jupyter_output><empty_output><jupyter_text>2. Data<jupyter_code>#paths to the Github repo

train_path = 'https://raw.githubusercontent.com/ccollado7/wds-datathon-2022/main/data/raw/train.csv'
test_path = 'https://raw.githubusercontent.com/ccollado7/wds-datathon-2022/main/data/raw/test.csv'
submit_path = 'https://raw.githubusercontent.com/ccollado7/wds-datathon-2022/main/data/raw/sample_solution.csv'
#train dataset

train = pd.read_csv(train_path)
train.head()
#test dataset

test = pd.read_csv(test_path)
test.head()
#sumbit

submit = pd.read_csv(submit_path)
submit.head()
x_train = train.drop(['id','site_eui'], axis=1)
x_train.columns
y_train = train['site_eui']
cat_columns = list(train.select_dtypes(include=['object']).columns)
cat_columns
model_1_1 = CatBoostRegressor(cat_features = cat_columns,
                             iterations=10000,
                             verbose=10)
model_1_1.fit(x_train,y_train,plot=True)
x_test = test.drop('id', axis=1)
x_test.columns
submit['site_eui'] = model_1_1.predict(x_test)
submit.to_csv('modelo_1_1.csv',index=False)<jupyter_output><empty_output>
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{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '513760', 'license': 'MIT', 'n_tokens_mistral': 592, 'n_tokens_neox': 562, 'n_words': 102, 'path': 'notebooks/modelos/modelo_1/modelo_1.ipynb', 'repo_name': 'ccollado7/wds-datathon-2022', 'size': '1572'}