This code performs the following steps: 1. **Display Initial Message:** Prints...

This code performs the following steps:

1. Display Initial Message: Prints the message Initiating data cleaning process by removing incomplete records... to indicate that the data cleaning process is starting.

2. Remove Rows with Null Values:

   • Creates a new DataFrame df_cleaned_data_no_nulls by dropping all rows in the original DataFrame df that contain any NaN or null values. This is achieved using df.dropna().

3. Calculate Record Counts:

   • Stores the number of rows in the original DataFrame df as initial_record_count.
   • Stores the number of rows in the cleaned DataFrame df_cleaned_data_no_nulls as cleaned_record_count.
   • Calculates the number of rows that were removed during the cleaning process by subtracting cleaned_record_count from initial_record_count, storing the result in records_removed.

4. Print Data Cleaning Summary:

   • Displays the original shape of the DataFrame (df.shape).
   • Displays the shape of the cleaned DataFrame after removing rows with null values (df_cleaned_data_no_nulls.shape).

5. Display Remaining Null Counts:

   • Prints a summary of the number of null values remaining in each column of the cleaned DataFrame. This is done using df_cleaned_data_no_nulls.isnull().sum().

In summary, this code initiates a data cleaning process by removing rows with null values and provides a summary of the dataset's shape before and after the cleaning, along with remaining null counts in the cleaned dataset.