# 🌟 Student Well-being & Academic Performance EDA

This project explores how **student habits and well-being** influence **academic performance (CGPA)**. Using a dataset of study patterns, sleep, screen time, stress levels, and extracurricular activities, we uncover meaningful trends that can help improve learning outcomes and student life.

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## 📁 Project Files

- **`firsttask.csv`** – Original dataset provided for analysis.  
- **`student-wellbeing-eda.py`** – Python script for data cleaning, analysis, and visualization.  
- **`cleaned_firsttask.csv`** – Cleaned dataset ready for analysis.

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## 🎯 Objective

Analyze the impact of daily habits and well-being factors on students’ academic performance to identify patterns, correlations, and actionable insights.

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## 🛠️ Analysis Workflow

1. **Data Exploration**
   - Examine dataset structure, missing values, duplicates, and outliers.  

2. **Data Preprocessing**
   - Handle missing and inconsistent records.  
   - Encode categorical variables (Stress Level, Extracurricular Activities).  
   - Prepare clean data for analysis.

3. **Exploratory Data Analysis (EDA)**
   - Investigate correlations between **study hours, sleep, screen time** and CGPA.  
   - Compare academic performance across **stress levels**.  
   - Analyze CGPA differences between students who participate in **extracurricular activities** vs those who don’t.  
   - Visualize patterns using scatter plots, bar charts, and box plots.

4. **Insights**
   - Generate actionable insights supported by graphs and statistics.

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## 📊 Key Insights

- 📈 Moderate stress levels often correspond with higher CGPA than high stress.  
- 💤 Optimal sleep hours positively impact academic performance.  
- 📱 Excessive screen time tends to lower CGPA.  
- 📚 Consistent study hours correlate with better performance.  
- 🎨 Participation in extracurricular activities is associated with higher CGPA.

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## 💻 How to Run

Clone the repository and install dependencies:

git clone https://github.com/prince-rai88/Student-Well-being-Academic-Performance-EDA.git
cd Student-Well-being-Academic-Performance-EDA
pip install -r requirements.txt


Run the analysis:

python student-wellbeing-eda.py