Preprocessing Data

It involves transforming raw data into a clean and usable format so that models can use the data for training purpose. There are three primary factors determining the usability of data:

1. Structure: It defines how data is stored.

2. Cleanliness: It means the duplicates, missing values and other anomalies that may affect its reliability and usefulness for analysis must be cleaned.

3. Accuracy: Accuracy indicates how well the data matches the real-world values to ensuring reliability.

Data Features

Data features are the characteristics or properties of the data. For example, in a table of student records, student's name, age, or class are Data Features.

In AI models, there are two types of features: independent and dependent.
Independent features are the input to the model—they're the information we provide to make predictions.
Dependent features are the outputs or results of the model—they're what we're trying to predict.
For example -