•Completed an Attrition Risk Report end to end by primarily cleaning and parsing a dataset with 1.5million entries on incidents related to product issues. Calculated scores for customers that were at risk of leaving the buisness.• Utilised sentimental analysis on customer survey results, identifying 10% more misflagged customers for followups.• Utilised data analyst techniques to draw insights from results by interpreting importance outliers and removing them from the dataset.•Evaluated multiple primary metrics to complete ascore, individually weighting importance of factors to identify top 10% of customers at risk of attriting. Saving up 60 buisness hours per quarter by automating survey sifting• Implemented data visualization dashboards using PowerBI, leading to a 20% improvement in decision-making efficiency and identification.