Exploratory Data Analysis (EDA) and Charts Used in the Analysis

Exploratory Data Analysis (EDA) is the process of examining data to identify patterns, trends, and relationships before building a prediction model. Data visualization plays an important role because it makes student performance insights easier to understand.

In this student performance analysis project, EDA is performed after data cleaning and preparation. Charts are used to study how factors like study hours, attendance, and other attributes influence exam scores.

• Trend Identification: Helps understand how study hours or attendance relate to exam scores.
• Category Comparison: Makes it easy to compare performance across different student groups.
• Relationship Analysis: Identifies how numerical factors are connected to final scores.

The project uses the following charts:

1. Line Chart
   Used to analyze patterns such as how exam scores change with increasing study hours or attendance levels. It helps identify upward or downward trends.

2. Bar Chart
   Used to compare average scores across different student categories, such as attendance levels or study groups. It clearly highlights which groups perform better.

3. Correlation Heatmap
   Used to measure the strength of relationships between numerical variables like study hours, attendance, and exam scores. It visually shows positive or negative correlations that impact student performance.

By combining EDA with these visualizations, the project transforms raw student data into meaningful insights that support building an accurate prediction model.