Welcome to the very first lesson of the Foundations of Feature Engineering. As you embark on this journey, you will explore the critical role of feature engineering in data analysis and machine learning. Feature engineering is the process of transforming raw data into meaningful features that improve the performance of machine learning models, making it a vital step that can significantly influence the success of your analytical projects. In this lesson, we will also introduce you to essential tools that are commonly used, such as the pandas library in Python, which offers powerful functionalities for data manipulation.

In data analysis, a dataset is a collection of data, often presented in a table format, where:

• Rows represent individual data points.
• Columns represent features.

Features are the different aspects, attributes, or properties of the data used to train machine learning models. Understanding which features your data contains and how they relate to the problem you are trying to solve is crucial. This knowledge allows you to make informed decisions about which features to select, manipulate, or create, facilitating a more effective analysis process.

Here's a generic example of a poorly structured dataset:

In this dataset, the data suffers from several issues. There are missing values in the "Age" column for Alice, which can affect model accuracy. The "Temperature" feature appears irrelevant to predicting "Loan Approved" and could be removed or transformed. Lastly, the "City" feature, while informative, can have high cardinality, so it may require encoding into a more usable format, or creating region-based categories to reduce its impact on performance. Feature engineering allows us to address these issues, improving data quality and model performance.

In this course, we will work with the Titanic dataset, a classic real-world dataset that contains information about the passengers aboard the RMS Titanic, which tragically sank during its maiden voyage in 1912. This dataset includes a variety of features such as demographic details, passenger class, fare, and survival status. It provides an excellent opportunity to apply feature engineering techniques, as it encompasses both numerical and categorical data, missing values, and other complexities typical of real-world datasets.

By utilizing the Titanic dataset, you'll gain practical experience in handling data imperfections, transforming variables, creating new features, and preparing data for machine learning models using pandas. This hands-on approach will help you build a strong foundation in feature engineering, essential for effective data analysis and predictive modeling.

Let's start by getting familiar with pandas, a popular Python library that makes it easy to work with structured data like tables. If you've ever used spreadsheets, think of pandas as a powerful tool to handle similar data in Python.

If you're working on your own computer, you can install pandas using pip, Python's package installer. Open your terminal or command prompt and run:

But since we'll be using the CodeSignal IDE for this course, good news—pandas is already installed there! So we can dive right into working with the data without worrying about setup.

Now, let's load the Titanic dataset into our Python environment. We'll use the pd.read_csv() function from pandas to read the data from a CSV file into a DataFrame, which is a special pandas object that looks like a table.

First, we need to import pandas:

Then, we can load the dataset:

Now, df is a DataFrame containing our Titanic data.

We might be curious about how big our dataset is—how many passengers are included, and how many features (columns) we have. We can use the shape attribute to find out:

When we run this code, we'll get:

This output tells us that the dataset contains 891 rows and 15 columns. Each row represents a passenger, and each column represents a feature or attribute of the passengers.

To understand what kind of data we're dealing with, we can use the info() function. It gives us details about each column, such as the data type and how many non-null values there are:

When we execute this code, we'll see:

This output provides us with valuable information:

• Non-Null Count: Shows how many entries have actual data (not missing) in each column. For example, the age column has 714 non-null entries, meaning there are missing age values for some passengers.
• Dtype: Indicates the data type of each column, such as integers (int64), floating-point numbers (float64), objects (typically strings), and booleans (bool).
• Total Columns: We can confirm there are 15 columns in total.

Understanding the types of data and where missing values exist is crucial for effective feature engineering.

Let's peek at the first few rows of the dataset to see what the data looks like. We can use the head() function for this:

When we run this code, we'll see:

This gives us a snapshot of the dataset:

• Rows 0-4: The first five passengers.
• Columns: Features like survived, pclass, sex, age, etc.
• NaN Values: Indicates missing data. For instance, the deck column has NaN, meaning the deck information is missing for these passengers.

Viewing the data helps us understand its structure and identify any immediate issues like missing values.

To get a quick summary of the numerical data in our dataset, we can use the describe() function. It calculates statistics like mean, standard deviation, and quartiles:

Running this code gives us:

Here's what these statistics tell us:

• Count: The number of non-missing values for each column. Notice that age has 714 entries, again highlighting missing values.
• Mean: The average value. For example, the average fare was about £32.20.
• Std: Standard deviation, showing how spread out the values are.
• Min and Max: The range of values. For age, the youngest passenger was 0.42 years old (approximately 5 months), and the oldest was 80 years old.
• Percentiles (25%, 50%, 75%): These show the distribution of the data. For instance, 50% of passengers were 28 years old or younger.

This statistical overview helps us understand the distributions and can reveal outliers or unusual data points that may affect our analysis.

To sum up, in this introductory lesson, we have laid the groundwork for feature engineering by covering the importance of feature engineering, understanding datasets and their features, and learning to use pandas to explore the Titanic dataset. These foundations are critical for tackling more advanced topics in future lessons. As you move forward, reflect on these techniques and try to apply them to build confidence. Stay curious and experiment as much as possible to maximize your learning experience!