📊 Student Lifestyle & Stress Analysis Dashboard

This project analyzes the impact of lifestyle factors on student stress levels using data analytics techniques and interactive dashboards built in Power BI and Tableau. It includes EDA, data visualization, descriptive statistics, predictive modeling, and correlation analysis.

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📁 Dataset Overview

• File: student_lifestyle_dataset.csv (unzipped from StudentLifeStyle.zip)
• Records: Each row represents a student with lifestyle attributes and their stress level.
• Features Include:
  • Gender
  • Age
  • GPA
  • Sleep_Hours
  • Study_Hours
  • Physical_Activity
  • Internet_Usage
  • Stress_Level (target variable)

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🧪 Steps & Techniques

1. 📊 Exploratory Data Analysis (EDA) - Python

• Handled missing values and checked for duplicates.
• Visualized distributions using:
  • Countplots (Stress_Level)
  • Boxplots (GPA, Sleep_Hours)
  • Histograms (Internet_Usage)
• Categorical encoding for modeling.

2. 📈 Correlation Analysis

• Pearson correlation for numerical features.
• Heatmaps to identify correlated features with Stress_Level.

3. 📋 Descriptive Statistics

• Used .describe() and groupby aggregation.
• Summarized mean, median, std by stress levels.

4. 🧠 Predictive Modeling

• Logistic Regression, Random Forest Classifier.
• Train-test split (80-20).
• Evaluation using accuracy, precision, recall, and confusion matrix.

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📊 Tableau Dashboard

Features:

• Treemap: Stress Level segmented by GPA Category.
• Bar Chart: Sleep Hours by Stress Level.
• Scatter Plot: GPA vs. Internet Usage with Stress Level as color.
• Filters for Gender and Physical Activity.

Instructions:

1. Connect Tableau to the CSV file.
2. Create calculated fields (e.g., GPA Category).
3. Use "Show Me" panel to build visuals.
4. Add sheets to a single dashboard view.

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📊 Tableau Visualizations

Dashboard