Employee Satisfaction Analysis

📊 Overview

This project performs an exploratory data analysis (EDA) on employee satisfaction survey data to uncover patterns and insights related to job satisfaction, stress, workload, commute, and more.

📁 Dataset

• Filename: employee_survey.csv
• Contains various features like Age, Gender, Dept, JobSatisfaction, SleepHours, etc.

🔧 Tools Used

• Python
• Jupyter Notebook
• Pandas, NumPy
• Seaborn, Matplotlib

📌 Steps Covered

1. Importing Libraries
2. Loading the Dataset
3. Previewing the Data
4. Checking for Duplicates and Missing Values
5. Classifying Features (Continuous vs Categorical)
6. Univariate Analysis
7. Bivariate Analysis
8. Visualizing Job Satisfaction

📈 Key Insights

• You can add bullet points here after your analysis (e.g., "Most employees with high satisfaction have balanced workloads and regular sleep.")

📂 How to Run

1. Clone the repo
2. Open the .ipynb file in Jupyter Notebook or any compatible tool
3. Run the cells in order

🤝 Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss.

📜 License

MIT