• PRATIK MARATHE
• Nikhil Mandavkar
• Shubham Marshettiwar
• Gaurav Patil
• Prof. Jyotsna A Nanajkar

The increasing popularity of machine learning in the business world is due to its ability to provide valuable insights and support data-driven decision-making. Consequently, organizations may make better judgments, work more effectively, and perform better as a whole. A major problem facing companies across a range of sectors is employee attrition. Attrition can be caused by several factors, such as poor management and inadequate working conditions, making it important for organizations to analyze their policies to increase employee retention. The goal is to prevent or minimize employees leaving a company before hiring a replacement. Considering the recent influx of data, researchers in the field of artificial intelligence have also addressed this problem. Using a Random Forest Classifier and data that was made available to the public, this study addressed the problem of employee attrition. While evaluating the efficiency of a classification model, we employed a Confusion matrix, which consists of an N x N matrix, where N is the total number of target classes. The model is accurately recognized with the aid of the confusion model and random forest classifier, and the accuracy of the employees who left the company is higher than other models.

Machine learning; Employee Attrition; Random Forest classifier; Confusion Matrix.

"Employee Attrition Prediction By RF", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.f607-f611, March-2023, Available :http://www.jetir.org/papers/JETIR2303577.pdf

"Employee Attrition Prediction By RF", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppf607-f611, March-2023, Available at : http://www.jetir.org/papers/JETIR2303577.pdf