A Jupyter Notebook-based project for analyzing employee satisfaction data. This repository provides a framework for data exploration, visualization, and potential insights into factors influencing employee satisfaction.
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Data Loading & Preprocessing: Tools and examples for handling raw employee satisfaction datasets.
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Exploratory Data Analysis (EDA): Jupyter Notebooks designed for initial data insights and understanding distributions.
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Data Visualization: Utilizes popular Python libraries to create informative charts and graphs.
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Statistical Analysis: Framework for applying statistical methods to identify key drivers of satisfaction.
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Interactive Environment: Leverages Jupyter Notebook for an interactive and iterative analysis workflow.
This project is structured around a single Jupyter Notebook, serving as the primary environment for data analysis. It follows a typical data science workflow: data ingestion, cleaning, exploratory data analysis, visualization, and potentially statistical modeling. All code execution and output are contained within the notebook, making it a self-contained and reproducible analysis environment. Data files are expected to reside locally, ideally within a dedicated data/ directory.
| Area | Tool | Version |
|---|---|---|
| --- | --- | --- |
| Language | Python | 3.x |
| Environment | Jupyter Notebook | Latest |
| --- | --- | --- |
| Data Manipulation | Pandas | Latest |
| Numerical Operations | NumPy | Latest |
| --- | --- | --- |
| Plotting | Matplotlib | Latest |
| Statistical Visualization | Seaborn | Latest |
| --- | --- | --- |
| Machine Learning (Optional) | Scikit-learn | Latest |
To get a local copy up and running, follow these simple steps.
Ensure you have Python installed on your system. It's recommended to use a virtual environment to manage dependencies.
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Python 3.8+
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pip(Python package installer)
- Clone the repository:
git clone https://github.com/Shambsri21/Employeesatis.git
cd Employeesatis
- Create a virtual environment (recommended):
python -m venv venv
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Activate the virtual environment:
- On macOS/Linux:
source venv/bin/activate
- On Windows:
.\venv\Scripts\activate
- Install the required Python packages:
pip install jupyter pandas numpy matplotlib seaborn scikit-learn
This project does not rely on external environment variables for configuration. All necessary paths (e.g., for data files) are expected to be defined directly within the Jupyter Notebook.
To run the analysis and explore the employee satisfaction data:
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Ensure your virtual environment is activated.
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Start the Jupyter Notebook server:
jupyter notebook
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Open the Notebook: Your web browser should automatically open to the Jupyter dashboard. Navigate to and click on
employee_satisfaction_analysis.ipynb(or similar name if the main notebook file is different) to open the notebook. -
Run the cells: Execute the cells sequentially within the notebook to perform data loading, cleaning, analysis, and visualization. You can run cells by selecting them and pressing
Shift + Enter.
.
├── data/
│ └── employee_data.csv # Placeholder for your dataset
├── employee_satisfaction_analysis.ipynb
└── README.md
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Add more detailed comments and explanations within the Jupyter Notebook.
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Incorporate advanced statistical models or machine learning algorithms for predictive analysis.
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Explore interactive visualization libraries (e.g., Plotly, Bokeh).
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Create a sample dataset for easier replication and testing.
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Refactor notebook cells for better modularity and readability.
Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Given the nature of a Jupyter Notebook for data analysis, "testing" primarily involves:
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Manual Verification: Visually inspecting outputs, plots, and summary statistics to ensure they align with expectations and data characteristics.
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Data Integrity Checks: Implementing assertions or checks within the notebook to ensure data types, missing values, and ranges are as expected after preprocessing steps.
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Reproducibility: Ensuring that running the notebook from top to bottom consistently produces the same results.
Distributed under the MIT License. See LICENSE for more information. (Note: A LICENSE file is recommended to be added to the repository).