Exploratory Data Analysis (EDA) is a crucial preliminary step in the data science process, aimed at gaining insights into the underlying structure, patterns, and relationships within a data set. Through a variety of statistical and visualization techniques, EDA allows data scientists to uncover the key characteristics of the data, identify outliers, understand the distribution of underlying features, and detect missing values.

One of the primary benefits of EDA is its role in enhancing the effectiveness of prediction models. By thoroughly understanding the data through EDA, data scientists can make informed decisions about feature selection, transformation, and engineering, leading to more accurate and robust prediction models. EDA helps identify relevant variables, understand their interactions, and uncover any potential biases or confounding factors that might affect the predictive performance of models. By leveraging the insights gained from EDA, data scientists can build prediction models that are better tailored to the underlying data structure, resulting in improved accuracy and reliability in making predictions.

In this project, you learn:

All you need is a basic knowledge of Python and a browser. A basic understanding of statistics and data science is helpful, but not strictly necessary.