This project aims to predict an individual's preference for working from home based on various input parameters. The model takes the following input parameters:
- Age
- Occupation
- Gender
- Same Office Home Location
- Kids
- RM Save Money
- RM Quality Time
- RM Better Sleep
- Calmer Stressed
- RM Professional Growth
- RM Lazy
- RM Productive
- Digital Connect Sufficient
- RM Better Work-Life Balance
- RM Improved Skillset
- RM Job Opportunities
The output is a target value indicating the likelihood of an employee's desire to work from home.
- Data Processing and Model Training: Implemented comprehensive data preprocessing techniques to clean and prepare data for modeling.
- Supervised Learning Models: Explored various supervised learning algorithms, including Random Forest, Decision Trees, and Support Vector Machines, to optimize prediction accuracy.
- Feature Engineering: Utilized feature selection and engineering methods to enhance model performance by identifying the most impactful variables.
- Performance Evaluation: Applied cross-validation and various metrics (accuracy, precision, recall) to evaluate the effectiveness of the models.
- Programming Language: Python
- Libraries:
- Pandas for data manipulation
- NumPy for numerical computations
- Scikit-learn for implementing machine learning models
- Streamlit for building the interactive web application
- Version Control: Git for source code management
- Clone the repository:
git clone https://github.com/professor1412/workfromoffice-or-workfromhome.git
Integration of real-time weather data to improve the accuracy of recommendations.
Incorporation of market trends and economic factors to assist employees in making informed work decisions.
Development of a mobile application for convenient access and usage on smartphones and tablets.
Continuous improvement through user feedback and data collection to enhance the model's performance.
Contributions to the project are welcome! If you have any suggestions, bug reports, or feature requests, please submit them through the issue tracker on the GitHub repository. Acknowledgements
We would like to express our gratitude to the data science community, mentors, and organizations for providing valuable insights, datasets, and support that contributed to the development of this Work from Home Decision Prediction System.
For any inquiries or questions, please contact us at [[email protected]].
- Clear Structure: Organized the content into distinct sections for better readability.
- Enhanced Description: Clarified the purpose of the project and how it operates.
- Key Features: Highlighted significant aspects of your work, including data processing and model training.
- Technology Used: Listed technologies and libraries used to demonstrate your technical expertise.
- Installation Instructions: Provided clear steps for users to set up and run your project.
- Future Enhancements: Included potential improvements, indicating ongoing development and forward-thinking.
- Contributions: Encouraged community engagement by inviting contributions and feedback.
- Acknowledgements: Showed appreciation for external contributions and data sources.
- Contact Information: Added a contact section for inquiries, giving a professional touch.
Feel free to customize any sections further, especially the contact information, and ensure it aligns with your specific project details. This updated README.md will serve as a strong representation of your work when included in your resume or shared in your GitHub portfolio! If you need any more assistance, let me know!