threshold value is 0.8.

After that, we are creating a DataFrame that contains only the selected features from the original DataFrame.

The output of the above program will be:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 344 entries, 0 to 343
Data columns (total 7 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   species            344 non-null    object 
 1   island             344 non-null    object 
 2   bill_length_mm     342 non-null    float64
 3   bill_depth_mm      342 non-null    float64
 4   flipper_length_mm  342 non-null    float64
 5   body_mass_g        342 non-null    float64
 6   sex                333 non-null    object 
dtypes: float64(4), object(3)
memory usage: 18.9+ KB
None
<class 'pandas.core.frame.DataFrame'>
Selected Features:
{'bill_length_mm', 'bill_depth_mm', 'flipper_length_mm'}
   bill_length_mm  bill_depth_mm  flipper_length_mm
0            39.1           18.7              181.0
1            39.5           17.4              186.0
2            40.3           18.0              195.0
3             NaN            NaN                NaN
4            36.7           19.3              193.0

As we can see bill length and body mass are strongly correlated. So, we selected only one of the features.