text_html_output_1.png
import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sns

df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')

df.isna().sum().sum()

def plot_features(df):
    fig, axs = plt.subplots(figsize=(16, 10), ncols=3, nrows=6)
    for count, col in enumerate(df.columns):

        sns.boxplot(data=df, x=col, ax=axs[count // 3][count % 3])
    plt.tight_layout()
    plt.show()

plot_features(df)

def plot_corr(df):
    plt.figure(figsize=(16, 10))
    corr = df.drop(columns=['id', 'yield']).corr()
    cmap = sns.diverging_palette(230, 20, as_cmap=True)
    sns.heatmap(corr, cmap=cmap, center=0, annot=True, square=True, linewidths=0.5, cbar_kws={'shrink': 0.5})
    plt.title('Correlation matrix of features')
    plt.show()
    return corr
corr = plot_corr(df)