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

bill_length_mm       0
bill_depth_mm        0
flipper_length_mm    0
body_mass_g          0
species              0
dtype: int64

As we can see, after the median imputation, there are no missing values in any column. Now, a machine learning model works with numbers. So, we need to label encode the species 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())

label_encoder = LabelEncoder()
df["species"] = label_encoder.fit_transform(df["species"])
print(df.head())

Now, we will select relevant features based on Recursive Feature Elimination (RFE). We can use the following Python code for that purpose: …