image_output_11.png image_output_5.png image_output_7.png image_output_4.png image_output_8.png image_output_6.png image_output_12.png image_output_3.png image_output_2.png image_output_1.png image_output_10.png image_output_9.png
import matplotlib.pyplot as plt

import numpy as np

import numpy as np # linear algebra

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

import seaborn as sns

df = pd.read_excel('/kaggle/input/pumpkin-seeds-dataset/Pumpkin_Seeds_Dataset.xlsx')

df.shape

df.duplicated().sum()

df.isna().sum()

numerical_cols = df.select_dtypes(include=np.number).columns.to_list()
numerical_cols[:4]

skewness_values = df[numerical_cols].skew().to_list()
skewness_values[:3]

skewness_values = df[numerical_cols].skew().to_list()
skewness_values[:3]

for col, sk in zip(numerical_cols, skewness_values):
    plt.figure(figsize=(14, 6))
    sns.histplot(data=df, x=col, bins=10, kde=True)
    plt.title(f'Distribution of {col}\nskewness:{sk:.2f}', fontsize=20)