Exploratory Data Analysis and Visualization

After cleaning and preparing the dataset, the next step is to explore the data and understand patterns, trends, and relationships that influence student performance. Exploratory Data Analysis, commonly known as EDA, helps transform raw student data into meaningful insights using summaries and visualizations.

In this project, EDA focuses on understanding how different factors such as study hours, attendance, teacher quality, parental education level, and distance from home impact exam scores. These relationships become clearer when represented visually.

To analyze how numerical factors affect performance, relationships between variables like study hours and exam scores are examined. This helps identify whether increased effort or better attendance leads to improved academic results.

Bar charts are used to compare average scores across different categories such as teacher quality levels or parental education backgrounds. These comparisons help identify which groups of students tend to perform better.

A correlation heatmap is used to measure the strength of relationships between numerical variables. It visually highlights positive and negative correlations, making it easier to understand which factors strongly influence student performance.

Through these visualizations, student data becomes more intuitive and easier to interpret. EDA builds the foundation for identifying key performance factors and preparing the dataset for further analysis or predictive modeling in the next module.