Drew Giffin
4.0 KiB
Student Habits and Stress Prediction Analysis
A machine learning project that examines the habits of students with the goal of gaining insight into how their daily routines may affect their stress levels. Habits such as studying, extracurricular involvement, sleep, socialization, and physical activity, as well as performance indicators like GPA, are analyzed to understand their correlation with stress.
Goal
The main goal of this project is to predict a student's stress level, categorized as low, moderate or high, depending on their times spent on different activities and academic performance. By providing clarity on the factors that most influence stress, the project aims to give students actionable context for developing less stressful lifestyles without compromising academic success.
Dataset
The dataset used in this project was sources from Kaggle, containing information about time spent on daily activities, academic performance, and reported stress level. Each entry has figures like time spent studying, sleeping, exersizing, socializing, and participating in extracurricular activities, as well as GPA.
The target variable is the stress level, indicated as low, moderate or high. These features will allow us to explore the correlation between lifestyle patterns, academic performance and stress. They will also help us build a predictive model capable of identifying which habits most strongly influence stress.
Exploratory Data Anaysis
Key Insights:
- Students who study more are more likely to have a higher GPA and more stress.
- Physical activity has a negative correlation with other activities, one being study and therefore stress.
- Students who sleep more were less likely to be very stressed.
- Some outliers were observed and will be need to be removed before training for more accurate results.
Figures:
Data Preprocessing
No missing values or duplicate rows were found in the dataset. Outliers in numeric features were identified using the interquartile range (IQR) method and removed before training. This helps reduce the impact of extreme values and can improve model performance.
Feature Engineering
To improve model performance and reduce redundancy, I performed feature engineering before training:
- GPA was removed because it was highly correlated with study time, reducing redundant information and potential multicollinearity.
- Features such as extracurricular activity time and social time were removed due to low predictive importance, minimizing noise and helping the model focus on the most relevant factors.
Modeling
This model was made using logistic regression, it works well in this situation because it models the probability of each class based on the input features, making it effective for categorical outcomes. After experimenting with different hyperparameter settings, including various solvers and iteration limits, I found that removing them entirely did not noticeably change the model's performance, indicating that the default configuration worked well enough for this purpose.
Results
