text_plain_output_1.png
import matplotlib.pyplot as plt

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import seaborn as sns

train_df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample_submission_data = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')

train_df.shape

train_df = train_df.drop(['id'], axis=1)
test_df = test_df.drop(['id'], axis=1)

columns_to_drop = ['MinOfUpperTRange', 'AverageOfUpperTRange', 'MaxOfLowerTRange', 'MinOfLowerTRange', 'AverageOfLowerTRange']
train_df = train_df.drop(columns=columns_to_drop)
test_df = test_df.drop(columns=columns_to_drop)

features = train_df.columns.tolist()

# create subplots
fig, axes = plt.subplots(4, 3, figsize=(12, 12))
axes = axes.ravel()

# loop over features and plot scatter plot
for ax, feature in zip(axes, features):
    ax.scatter(train_df[feature], train_df['yield'])
    ax.set_xlabel(feature)
    ax.set_ylabel('yield')

# adjust subplot spacing
# plt.subplots_adjust(hspace=0.5)

# display plot
plt.show()

train_df.columns