species     island  bill_length_mm  ...  flipper_length_mm  body_mass_g     sex
0  Adelie  Torgersen            39.1  ...              181.0       3750.0    Male
1  Adelie  Torgersen            39.5  ...              186.0       3800.0  Female
2  Adelie  Torgersen            40.3  ...              195.0       3250.0  Female
3  Adelie  Torgersen             NaN  ...                NaN          NaN     NaN
4  Adelie  Torgersen            36.7  ...              193.0       3450.0  Female

[5 rows x 7 columns]
species               0
island                0
bill_length_mm        2
bill_depth_mm         2
flipper_length_mm     2
body_mass_g           2
sex                  11
dtype: int64

So, the dataset has 7 columns and some of the columns have missing values. Here, we are interested in the numerical columns. So, let’s drop the categorical columns except for the species column. And then, fill all the missing values with the median value of the column.

import seaborn
from sklearn.linear_model import LinearRegression
from sklearn.feature_selection import RFE
from sklearn.preprocessing import LabelEncoder

df = seaborn.load_dataset("penguins")
print(df.head())
print(df.isnull().sum())

df.drop(labels=["island", "sex"], axis=1, inplace=True)
df = df[["bill_length_mm", "bill_depth_mm", "flipper_length_mm", "body_mass_g", "species"]]

df.bill_length_mm.fillna(value=df["bill_length_mm"].median(), inplace=True)
df.bill_depth_mm.fillna(value=df["bill_depth_mm"].median(), inplace=True)
df.flipper_length_mm.fillna(value=df["flipper_length_mm"].median(), inplace=True)
df.body_mass_g.fillna(value=df["body_mass_g"].median(), inplace=True)
print(df.isnull().sum())

The output shows the following: …