Employee Attrition Predictor .pptx

In today's fast-paced business environment, retaining talent has become a paramount concern for organizations worldwide. The cost associated with employee attrition is not just monetary but also includes loss of knowledge, increased recruitment expenses, and potential disruption to ongoing projects. The key to minimizing this impact is early prediction and proactive measures. Enter the Employee Attrition Predictor.

The Employee Attrition Predictor is an innovative tool that leverages advanced machine learning algorithms to predict the likelihood of an employee leaving an organization. Designed with utmost precision by leveraging a dataset crafted by IBM's esteemed data scientists, this tool promises accuracy and actionable insights.

1. Employee Attrition Predictor Raj Tandon D17C/62 Anurag Tripathi D17C/68 Param Pandey D17C/50

2. Content Chapter 1: Introduction Chapter 2: Literature Survey Chapter 3: Requirements Chapter 4: Proposed Design Chapter 5: Implementation Chapter 6: Result Analysis Chapter 7: Conclusion

3. Chapter 1: Introduction In today's fast-paced business environment, retaining talent has become a paramount concern for organizations worldwide. The cost associated with employee attrition is not just monetary but also includes loss of knowledge, increased recruitment expenses, and potential disruption to ongoing projects. The key to minimizing this impact is early prediction and proactive measures. Enter the Employee Attrition Predictor. The Employee Attrition Predictor is an innovative tool that leverages advanced machine learning algorithms to predict the likelihood of an employee leaving an organization. Designed with utmost precision by leveraging a dataset crafted by IBM's esteemed data scientists, this tool promises accuracy and actionable insights.

4. Chapter 2: Literature Survey Employee attrition has been a topic of extensive research due to its profound impact on organizational sustainability and growth. 1. Traditional Methods of Predicting Attrition: Smith & Jones (2015) delved into the traditional methods of predicting attrition, primarily relying on employee feedback, periodic reviews, and personal interviews. 2. Machine Learning in Human Resources (HR): According to Brown et al. (2017), HR departments have started incorporating machine learning techniques to enhance various HR processes, including recruitment, talent management, and attrition prediction..

5. Requirements HARDWARE Processor : Minimum: Dual-core CPU Recommended: Quad-core CPU or higher Memory (RAM): Minimum: 4 GB Recommended: 8 GB or higher Storage: Minimum: 20 GB of free space (to store the application, dataset, and other dependencies) SOFTWARE Deployment and Integration: Flask integration with PythonAnywhere server Frontend: Browsers: Latest versions of Chrome, Firefox, Safari, or Edge for optimal user experience. Tools: HTML5, CSS3 Development Tools: Text Editor or Integrated Development Environment (IDE) like Visual Studio Code, PyCharm, or Jupyter Notebook.

6. Proposed System 1. System Architecture Overview: The system will follow a three-tier architecture: Presentation Layer : User interface designed using HTML and CSS. Logic Layer : Flask application, integrating the machine learning model, data processing, and business logic. Data Layer : A database system (if required) to store potential relevant information, like user queries, feedback, or additional datasets. 1. Presentation Layer Design: Homepage: A Login/Registration screen for user management Prediction Interface: A form-based interface where users can input employee details. The results will display the prediction and potentially relevant insights or recommendations.

7. Implementation We have deployed our ML model with Flask microframework, with the authentication being done with login credentials. The link to our project can be found here .

8. Result Analysis Our model has been trained using the Random Forest Classification algorithm, with n_estimators as 750. The training to testing ratio used by the model is 70:30, which gave us a decent accuracy of 86%. The required and important plots and metrics used by us are shown as follows.

9. Conclusion In conclusion, the development and implementation of an employee attrition predictor is a valuable tool for organizations looking to enhance their workforce management strategies. By leveraging predictive analytics and data-driven insights, businesses can proactively identify and address factors contributing to attrition, allowing them to reduce turnover rates, improve employee satisfaction, and ultimately increase productivity and profitability. It is crucial for organizations to continuously refine and adapt their attrition prediction models to stay ahead in the competitive job market and foster a positive workplace culture that retains top talent.