How to clean and prepare your data using Python

Collected data in real-world applications has missing values, anomalies or even data types could be wrong. Many believe that 70% or more of spent hours in machine learning projects belong to collecting, and cleaning of the data. In this video, we will talk about different steps that are common in data preparation. Also, the following links help you to navigate easier to the portion of the video that you are interested in more.
Github: github.com/mes...
🕒 VIDEO SECTIONS 🕒
1:24 Importing data
7:26 Filtering columns and rows
23:40 Data manipulation

[@nickname_day1053]

thank sir

[@mehdichellak4373]

thank you sir.

[@OJustBen]

Thanks mate.