INTRODUCTION
Exploratory Data Analysis (EDA) is a fundamental process in data science that helps uncover the underlying structure of data, providing insights that guide further analysis and decision-making. EDA involves a range of techniques aimed at understanding and preparing data for more complex analysis.

what are the goals of EDA?
The primary goals of EDA are to understand the dataset, identify data quality issues, select relevant features, and generate hypotheses. By summarizing the data through measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation), EDA provides a foundational understanding of the data’s distribution and variability. Data visualization plays a crucial role, allowing analysts to create histograms, box plots, and scatter plots to visualize univariate, bivariate, and multivariate relationships. These visual tools help in detecting patterns, trends, and outliers.
when we say univariate relationships we refer to the analysis of a single variable in isolation, focusing on its distribution, central tendency, and dispersion. Bivariate and multivariate relationships involve analyzing the interactions between two or more variables, respectively

practical steps in EDA
Effective EDA involves several practical steps. First, loading and inspecting the data using tools like Pandas in Python is essential for understanding its structure. Data visualization follows, where charts and plots help reveal relationships and anomalies. Identifying patterns and trends leads to more focused analysis, and the process is iterative, requiring refinement based on initial findings.

Data cleaning is another critical aspect of EDA. This includes handling missing values through imputation (adding zeros to replace null values)or removal, detecting outliers, and transforming data (e.g., normalization and encoding) to prepare it for modeling. Tools like Matplotlib, Seaborn, and Plotly in Python, are commonly used for these tasks.

In summary, EDA is an iterative and exploratory process that lays the groundwork for robust data analysis. By thoroughly understanding and cleaning data, analysts can ensure that their subsequent analyses are accurate and insightful. For those looking to deepen their knowledge, this notebook can be a good starting point [(https://colab.research.google.com/drive/1n8LRL1W3Er4fs3yb1urecOHuXdSfI3-E?usp=sharing)]