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import pandas as pd

import seaborn as sns

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold, KFold
from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor, GradientBoostingRegressor
from sklearn.ensemble import HistGradientBoostingRegressor
from sklearn.svm import SVR, LinearSVR
from sklearn.neighbors import KNeighborsRegressor
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.linear_model import LinearRegression, Ridge, Lasso, LogisticRegression
from sklearn.metrics import mean_absolute_error, roc_auc_score, roc_curve
from sklearn.preprocessing import LabelEncoder, StandardScaler, MinMaxScaler
from sklearn.pipeline import make_pipeline, Pipeline
from sklearn.feature_selection import SelectFromModel
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.decomposition import PCA, NMF
from sklearn.manifold import TSNE
from umap import UMAP
from scipy.cluster.hierarchy import dendrogram, ward
from xgboost import XGBRegressor, XGBClassifier
from lightgbm import LGBMRegressor
from catboost import CatBoostRegressor
sns.set_theme(style='white', palette='viridis')
pal = sns.color_palette('viridis')
pd.set_option('display.max_rows', 100)

train = pd.read_csv('../input/playground-series-s3e14/train.csv')
test_1 = pd.read_csv('../input/playground-series-s3e14/test.csv')
orig_train = pd.read_csv('../input/wild-blueberry-yield-prediction-dataset/WildBlueberryPollinationSimulationData.csv')
train.drop('id', axis=1, inplace=True)
test = test_1.drop('id', axis=1)
orig_train.drop('Row#', axis=1, inplace=True)

train.head(10)