Summary
This chapter demonstrates how to load a dataset with pandas, use printing to inspect its contents, and perform essential data preprocessing steps: handling missing values, encoding categorical variables, and addressing feature scale differences.

General domain of usage
Machine learning

Begin preprocessing by exploring the dataset. Throughout this course, the **penguin dataset** will be used, with the task of predicting the species of a penguin.


There are three possible options, often referred to as **classes** in machine learning:

The features are: `'island'`, `'culmen_depth_mm'`, `'flipper_length_mm'`, `'body_mass_g'`, and `'sex'`.

The dataset is stored in the `penguins.csv` file. It can be loaded from a link with the `pd.read_csv()` function to examine its contents:


import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins.csv')

print(df.head(10))

This dataset presents several issues that need to be addressed:

* Missing data;
* Categorical variables;
* Different feature scales.


## Missing Data

Most ML algorithms cannot process missing values directly, so these must be addressed before training. Missing values can either be **removed** or **imputed** (replaced with substitute values).

In `pandas`, empty cells are represented as `NaN`. Many ML models will raise an error if the dataset contains even a single `NaN`.


## Categorical Data

The dataset includes categorical variables, which machine learning models are unable to process directly.


Categorical data must be **encoded** into numerical form.


## Different Scales

`'culmen_depth_mm'` values range from 13.1 to 21.5, while `'body_mass_g'` values range from 2700 to 6300. Because of that, some ML models may consider the `'body_mass_g'` feature **much more important** than `'culmen_depth_mm'`.

**Scaling** solves this problem. It will be covered in later chapters.

Master the fundamentals of Machine Learning and the Scikit-learn library. Explore the complete ML workflow, from handling missing values and encoding categorical data to scaling features. Build efficient, leak-proof data preprocessing pipelines using ColumnTransformer. Transform raw datasets into model-ready structures and implement robust predictive pipelines.

Getting Familiar with Dataset

Missing Data

Categorical Data

Different Scales