Student Stress Analysis — Data Science Project A machine learning project that analyzes and predicts stress levels in students based on various physiological, psychological, and academic parameters. Overview Student stress is a growing concern in academic environments. This project uses a real-world dataset of student-related stress indicators to perform exploratory data analysis (EDA) and build predictive models that can identify and classify stress levels. The goal is to uncover patterns in the data and build a model capable of predicting whether a student is likely to be stressed based on measurable features. Dataset The dataset contains parameters collected from students, which may include:

Physiological factors — sleep quality, headache frequency, blood pressure, etc. Psychological factors — anxiety level, mental health history, depression indicators Academic factors — study load, academic performance, extracurricular activities Social factors — social support, bullying, peer pressure

Target variable: Stress level (e.g., low / medium / high, or binary stressed / not stressed)

Workflow

Data Exploration (EDA) — Understand distributions, correlations, and key stress indicators Preprocessing — Handle missing values, encode categorical features, normalize/scale data Feature Engineering — Select or construct the most relevant features Modeling — Train machine learning models to predict stress levels Evaluation — Compare models using accuracy, F1-score, confusion matrix, etc. Insights — Interpret results and identify the top predictors of student stress

Tech Stack

Language: Python Libraries: scikit-learn, pandas, NumPy, Matplotlib, Seaborn Environment: Jupyter Notebook