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Make sure to complete this challenge, it is essential for any Data person.
Data Cleaning plays a crucial role in every Data Analytics or Machine Learning project, as the quality of your data directly impacts the reliability of your insights. In today's session, we will explore simple methods to clean data frames using Python. Datasets can present various issues, such as incorrect formats (e.g., integers stored as strings), duplicate records, empty cells, and more. Fortunately, there exist multiple approaches to tackle these problems effectively.
Wrong format
Click to see how to handle wrong format
Empty cells
An empty cell can be handled in several ways such as filling the empty cell with the average value of the column (mean), median, mode, filling empty cells with the value before or after them, or even dropping the empty cells.
Read more here.
For a more interactive learning process, I have provided a working Kaggle notebook and a video link for you to watch and practice along. You can download the dataset from Kaggle and practice locally on your PC or open the notebook ***here. Follow the steps below to access a copy of the notebook and follow the instructions in the video below.***