"Data Engineering is like you taking all the frustrating parts of being a data analyst and combining them with all the frustrating parts of being a software engineer.
Until payday and you forget about the frustrations for a moment, haha!!!"

Hey there, are you into machine learning and want to gain most of your date, probably you should try featuring engineering in your endeavors.
Feature engineering essentially the process of converting raw data like numbers, dates, into something that your machine learning model can comprehend and use.
The better the features, the better your model will perform.
Even with the best algorithms, your model is only as good as the information you feed it. Good features equal better accuracy. Giving your model glasses helps it see things well.

Now let’s take a look at types of features and know what we are missing out on;

1. Numerical Features: Continuous values, like age, wages, or height. 2.Categorical Features: Are distinct groups like gender(male/female), city (Nairobi, Mombasa). 3.Ordinal Features: Are categorized traits that have a natural order, such as education level or grades (A, B, C). 4.Temporal Features: Include date and time-related information, such as timestamps.

Let’s look at Cool Feature Engineering Tricks:
1.Clean Your Data: Remove missing values and outliers.
Missing value handling techniques include imputation (mean, median, mode), the use of algorithms that support missing values, and the removal of rows/columns with missing data.
Outlier Detection and Removal techniques include use of statistical methods (e.g., Z-scores, IQR) or model-based approaches to identify and manage outliers.
2.Encode Categories: Convert categories into numbers
Label Encoding where you transform categories into numerical labels (e.g., Male = 0, Female = 1).
One-Hot Encoding where you create binary columns for each category (e.g., gender_Male and gender_Female).
Target Encoding where you replace categories with the target variable's mean for each category.
3.Scale Features: Ensure that all of your data is on the same scale so that no one feature overpowers another.
Normalization is the process of scaling features to a specific range, which is usually between 0 and 1.
Standardization involves scaling features to have a zero mean and unit variance.
Robust Scaling uses percentiles to scale features, making it less vulnerable to outliers.
4.Create New Features: Merge or alter existing features to create new ones.
For example, multiply two characteristics or generate time-based features such as "day of the week."

Here are some of the best practices;
1.Understand Your Data: Perform some analysis to determine what's going on.
2.Start Simple: Don't overcomplicate things; instead, start with basic features.
3.Test and iterate: Experiment with different features to find what works best.
4.Avoid Data Leakage: Using future data to forecast the past is like cheating on a test.
5.Document your work: Take notes on what you've done so you can repeat it if necessary.

That’s a lot we have looked at. Its amazing right? In that mood of excitement, we also have to take caution at these two things so that we be on the safer side;
1.Don't Overfit: Adding too many features may make your model perform well on existing data but poorly on new data.
2.Use Domain Knowledge: Rather than relying just on tools, construct relevant features based on your understanding of the data.

All the best!