A naukri.com initiative
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Predicting Customer Happiness
- Understanding customer satisfaction is crucial for business success in today’s competitive market.
- A logistics and delivery startup aimed to predict customer happiness based on survey responses and streamline their survey by identifying significant predictors of customer sentiment.
- The dataset, analyzed using Python with packages like pandas and sklearn, included responses on a scale of 1 to 5 for customer satisfaction surveys.
- Data validation, Exploratory Data Analysis (EDA), Predictive Model Evaluation, Feature Engineering, and Model Training were key steps in the analysis.
- Findings included correlations between survey responses and customer happiness, important predictors of unhappiness, and optimal feature selection using Recursive Feature Elimination.
- Logistic Regression, Random Forest, and Supervised Vector Classifier (SVC) were evaluated for prediction accuracy and recall, with Random Forest performing best.
- Reducing features to optimize the Random Forest model improved Unhappy recall to 88% while maintaining an overall accuracy of 81%.
- The project successfully utilized data analysis and machine learning to predict customer happiness and identify critical survey questions related to customer sentiment.
- Recommendations included focusing on maximizing customer satisfaction in key areas identified through the analysis.
- The author expressed gratitude for the Apziva Residency program and mentorship received in enhancing their skills and knowledge in data science.
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