Input and Output

In this student performance analysis project, understanding how data moves from input to output helps beginners clearly see how raw educational data is converted into insights and predictions.

Input

• A student performance dataset in CSV format.
• The dataset includes information such as hours studied, attendance percentage, sleep hours, previous scores, tutoring sessions, and exam scores.
• The file is uploaded into Google Colab and loaded into a Pandas DataFrame for processing.

Output

• A cleaned and structured dataset with missing values handled.
• Exploratory Data Analysis visualizations showing relationships between attendance, study habits, and exam scores.
• Correlation heatmaps highlighting factors that influence student performance.
• A trained Linear Regression model that predicts exam scores based on student-related features.
• Clear insights that help understand which factors contribute most to academic performance.