● Developed machine learning models to segment customers based on behavior and preferences, utilizing Python (Pandas, Scikit-learn). ● Analyzed customer transaction data to improve targeted marketing efforts. ● Achieved a 25% increase in marketing campaign ROI through effective customer segmentation.

● Developed predictive models to identify customers at risk of churn, using Python (Pandas, Scikit-learn). ● Analyzed telecom subscriber data to reduce customer attrition rates. ● Successfully reduced churn rate by 15% through proactive churn prediction strategies.

● Developed a time-series forecasting model to predict loan loss provisions.Utilized unstructured historical loan data and economic indicatorsto forecast future credit losses. ● Python, Time Series Analysis, Scikit-Learn. ● Reduced forecasting errors by 20% compared to the previous model, which can lead to better risk management.

● Built models predicting customer lifetime value, can be helpful in increasing revenue by 15%. ● Employed Python (Pandas, Scikit-learn), Machine Learning (ML) for predictive modeling, and SQL for database. ● Enabled targeted marketing strategies based on customer transaction history.

● Developed a time series algorithm to count the number of reps done by an athlete using huge unstructured data of squat rep. ● Implemented signal processing algorithms to preprocess raw sensor data, including noise reduction and feature extraction. ● Documented the project comprehensively, ensuring ease of understanding.

● Led a team of two to develop a system for keeping records of hostel students of attendance, fees, allotted rooms. ● Prepared the PowerPoint presentation to communicate the process and the results to the teaching staff.

● Collaborated with other teams for effective data analysis, utilizing Python and SQL for data cleaning, preprocessing, and analysis. ● Analyzed complex secondary datasets using analytical tools, such as Scikit-Learn and Statistical Analysis. ● Communicated seamlessly with internal and external partners.