Zayid Rafiq

Developed a machine learning prediction model using different classification techniques to predict customer turnover, improving predictive accuracy and retention strategies
Analyzed and integrated key features from customer data, improving predictive model accuracy and generating valuable business insights for strategic decision making
Designed and developed an interactive Power BI Dashboard to track customer retention trends, analyze tenure-based correlations, and evaluate revenue impact
Leveraged Python, SQL, and Power BI to identify key churn drivers, uncover actionable insights, and implement data-driven retention strategies that enhanced customer retention and business performance

Developed a machine learning prediction model using different classification techniques to predict customer turnover, improving predictive accuracy and retention strategies, Analyzed and integrated key features from customer data, improving predictive model accuracy and generating valuable business insights for strategic decision making, Designed and developed an interactive Power BI Dashboard to track customer retention trends, analyze tenure-based correlations, and evaluate revenue impact, Leveraged Python, SQL, and Power BI to identify key churn drivers, uncover actionable insights, and implement data-driven retention strategies that enhanced customer retention and business performance

Performed in-depth data preprocessing and exploratory data analysis (EDA) to uncover trends, actionable insights, and anomalies for data-driven decision-making
Achieved an 87% improvement in sentiment classification accuracy through advanced feature engineering, refined data transformation, and model optimization techniques
Designed and implemented a customer review classification system using NLP and machine learning techniques, enhancing sentiment analysis and business insights
Translated the sentiment analysis findings into actionable Business intelligence, enhancing customer experience and optimizing product development strategies