Predicting Student Engagement and Emotional State

This project aims to analyze and visualize student engagement and emotional states based on a dataset containing various features such as free time, sleep, and perceived strictness. By utilizing data visualization techniques, the project provides insights into how these factors correlate with students' emotional states.

Project Overview

The project leverages the following steps:

1. Data Import and Preparation:

   • The dataset is imported from a provided URL.
   • Emotional states are mapped to more descriptive labels.

2. Data Visualization:

   • Pairplot: Visualizes pairwise relationships in the dataset, highlighting correlations between different features and emotional states.
   • KDE Plots: Kernel Density Estimation plots for visualizing the distribution of free time, sleep, and strictness with respect to happiness.
   • Heatmap: Displays the correlation matrix to show the strength of relationships between different variables in the dataset.

Libraries Used

• pandas: For data manipulation and analysis.
• seaborn: For creating informative statistical graphics.
• matplotlib: For plotting graphs and figures.

Code Explanation

Data Import and Setup

The project begins by importing the necessary libraries and setting up the seaborn style for plots. The dataset is then loaded from a URL, and a dictionary is used to map numerical codes to descriptive emotional state labels.

Data Visualization

1. Pairplot:

   • A pairplot is generated to visualize pairwise relationships in the dataset, with hue set to the emotional state to differentiate between different emotional states.

2. KDE Plots:

   • KDE plots are created for 'Freetime', 'Sleep', and 'Strictness', with hue set to emotional state. These plots show the distribution of these variables and how they relate to happiness.

3. Heatmap:

   • A heatmap is generated to visualize the correlation matrix of the dataset. This helps in understanding the relationships and dependencies between various features.

Analysis

• The pairplot helps in identifying potential correlations between different features and emotional states.
• The KDE plots show how the distribution of free time, sleep, and strictness varies with happiness, providing insights into which factors might influence happiness.
• The heatmap provides a comprehensive view of how different features correlate with each other, highlighting significant relationships.

Conclusion

By visualizing the dataset, the project provides valuable insights into how various factors like free time, sleep, and strictness correlate with student happiness and emotional states. These visualizations can help educators and researchers better understand student engagement and emotional well-being.