This study examines the impact of insufficient recognition and appreciation on employee satisfaction and turnover intentions. Utilizing a dataset comprising 1,200 employees across diverse industries, the research employs Python and Scikit-learn algorithms—specifically, a Random Forest Classifier—alongside Pandas and Seaborn/Matplotlib libraries for data processing and visualization. The model achieved an accuracy of 87%, with key predictors of dissatisfaction identified as recognition frequency, perceived appreciation of team contributions, and intra-team communication. A comparative analysis demonstrated the superior performance of the Random Forest algorithm over Logistic Regression and Support Vector Machines, particularly in handling non-linear relationships and providing interpretable feature importance rankings. The findings underscore ingratitude as a significant determinant of workplace dissatisfaction and attrition risk. The study highlights the organizational imperative of fostering a culture of genuine appreciation, offering actionable insights to improve employee retention and psychological well-being
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