Missing Value Handling

This project demonstrates different techniques used to identify and handle missing values in a dataset. Multiple methods are applied to clean the dataset and make it more consistent for further use.

Techniques Used

• fillna()
• ffill()
• bfill()
• ffill(axis=1)
• bfill(axis=1)
• Mode value replacement

Tasks Performed

• Loading the dataset
• Checking missing values
• Handling missing values using different techniques
• Cleaning the dataset
• Saving the processed dataset

Repository Files

• remove null values – Notebook containing the complete process
• fill null values
• mode
• Loan payments data.csv – New & Cleaned dataset

Output

• After applying missing value handling techniques, the cleaned dataset is saved as New_dataset.csv.