This section outlines all the data transformation processes done to dataset.
Before any data transformation, measures were taken to ensure data integrity.
Creation of Backup Sheet
A backup of the original dataset was created.
The backup sheet was protected to prevent modifications and hidden from the working environment to avoid accidental changes.
Data Integrity Checks
Prior to any modification, thorough checks were conducted to ensure the integrity and consistency of the dataset.
Convert Data to Table
The data was converted into a table format to enhance data handling and analysis capabilities.
Duplicate Record Removal
Identified and removed 13 duplicate records based on multiple criteria: property_id, latitude, longitude, address, type, and price. Remain 230 unique records.
Removal of Irrelevant & Duplicated Columns
Removed repeated and irrelevant columns (e.g., address, images, status, and added) to streamline the dataset.
Handling Zeros and Null Values
Applied conditional formatting to highlight records with values below zero, such as distance_to_uol, time_by_walk, and price, as well as to identify empty records. 6 records were removed and remain 224.
Concatenation of Column Values
Concatenated the fname and sname columns using the CONCAT and TRIM functions to create a full_name column for the agent.
Text to Column
Split the address field, which contained both the address line and the area within Leicester, into two separate columns using a comma delimiter.
Standardisation of Similar Addresses
Standardised minor inconsistencies where the same address appeared differently after splitting the column.
Data Formatting
Formatted the data appropriately for currency, distance, and time to ensure consistency and accuracy in reporting and analysis.
Renaming Column Names
Renamed several columns to more appropriate titles (e.g., member, trade, property, type) for clarity.
