Kapish Anand

Wallet, gap, rank prediction of customer. Optimized and pre-processed data before building a regression model. Performed Feature Engineering on data having 148 columns using Python libraries like Numpy, Pandas, matplotlib and Seaborn. Built and trained model using Random Forest Regression and XGBoost Regression. Analyzed model prediction using R2_score, MSE. Large Trade Classification. Analyzed and performed EDA on data before building a classification model. Segmented data by creating clusters using K-Means clustering. Performed Feature Engineering using Python libraries like Numpy, Pandas, matplotlib and Seaborn. Built and trained model using Random Forest Classification and XGBoost Classification. Analyzed model prediction using classification report, confusion matrix. Detecting PII sensitivity using ML. Analyzed data for better understanding of problem statement. Used sampling techniques to overcome imbalanced dataset. Logistic Regression, Decision Trees and AdaBoost were implemented for classification. Performed hyperparameter tuning for optimization and fine tuning of model. Analyzed performance in terms of accuracy, precision and recall. Client Profitability. Migrated client profitability analytics application to Hadoop using big data technologies. Migrated sybase scripts to pyspark scripts. Created predictive models using regression techniques.

Responsible for developing custom components of application with respect to performance. Automated profiler analysis to prevent revenue leakages and over billing. Developed Oracle PL/SQL scripts. Performed performance tuning activities for Oracle database.

Responsible for designing, developing custom part of the product which managed all aspects of credit agreement lifecycle. Migrated SQL Server scripts to Oracle PL/SQL scripts. Developed new PL/SQL scripts. Responsible for optimizing data access and performance tuning.

Responsible for designing, developing, and supporting of databases and database objects for product development. Designed database objects for performance, scalability and maintainability. Provided estimates of work involved in data migration.

Responsible for requirements gathering and documenting requirements. Followed industry coding standards for developing database objects, PL/SQL scripts. Responsible for knowledge sharing to new team members. Performed Unit testing of requirements. Monitoring and resolving defects.