This project analyzes survey data collected via SurveyMonkey to extract key insights and trends. The dataset is processed using Python, and visualizations are created to present meaningful findings. The final results are compiled into a presentation.
- Data - Survey Monkey Output.xlsx: Raw survey data exported from SurveyMonkey.
- Script1-Data_manipulation.ipynb: Jupyter notebook for data cleaning and analysis.
- Final_Presentation.xlsx: Summary of key insights and visualizations.
Ensure you have the following installed:
- Python 3.x
- Jupyter Notebook
- Pandas
- Matplotlib / Seaborn (for visualization)
- Clone this repository:
git clone https://github.com/rohitblaze10/survey-analysis.git cd survey-analysis - Install the required Python libraries:
pip install pandas matplotlib seaborn jupyter
- Open Jupyter Notebook:
jupyter notebook
- Run
Script1-Data_manipulation.ipynbto process and analyze the data. - View results in
Final_Presentation.xlsx.
- Top 3 Divisions Represented:
- Infrastructure (48 respondents)
- Finance (44 respondents)
- Information Technology (26 respondents)
- Position Levels:
- Staff (116 respondents)
- Managers (46 respondents)
- Department Leads (28 respondents)
- Generational Breakdown:
- Generation X (75 respondents, born 1965-1980)
- Millennials (66 respondents, born 1981-2000)
- Baby Boomers (39 respondents, born 1946-1964)
- Gender Distribution:
- Female (96 respondents)
- Male (86 respondents)
- Non-Binary (1 respondent)
- Prefer not to answer (13 respondents)
- Employment Type:
- Nearly all respondents (198) are full-time employees.
- Enhance data cleaning steps.
- Add more visualizations for better insights.
- Automate report generation.
Feel free to fork this repository and make improvements. Pull requests are welcome!
[Specify a license, e.g., MIT, Apache 2.0] .