Data Cleaning and Feature Engineering

After loading and inspecting the weather dataset, the next step is preparing the data for analysis. Real-world datasets often contain missing values, incorrect data types, duplicate records, or columns that require transformation before meaningful insights can be extracted.

Data cleaning ensures that the dataset is accurate, consistent, and ready for analysis. Feature engineering further improves the dataset by creating new variables that help reveal patterns and trends.

In this module, we will clean the weather data, handle missing values, convert date columns into a usable format, and create additional features that simplify weather trend analysis.

Data cleaning is one of the most important steps in any Data Science project.

Even the most advanced analysis techniques can produce misleading results if the underlying data contains errors.

Common data quality issues include:

• Missing values
• Duplicate records
• Incorrect formats
• Inconsistent entries
• Invalid observations

By cleaning the data first, we ensure that the analysis is reliable and accurate.