Data ScientistLoan defaulter prediction:-The aim of this project is to find if customer is going to default on a loan or not.-For this project we used 85k data. Applied all classification algorithms. Used Random Forest algorithm as it gave 0.85 Recall score.- Did EDA, Data Preprocessing, Encoding, Missing values treatment, outlier Treatment, Model building.Achieved 7% profit to the bank by using this.Customer Churn Prediction- The aim of this project is to find customer churn.- For this project we used 90k customer's data. -Did EDA, Data Preprocessing, Encoding, Missing values treatment, outlier Treatment, Model building.-Used GBM classifer which gave 0.865 Recalls Score and 8% profit to the company.

Build Release Engineer-Working in Build Release Team where all the changes are collected from developers and make them visible in production without any error by completing 45day test cycle.-The process confains Exception Analysis, Nightly Deployments, Latest builds, Defect fixing, Sanity Testing, Fetching logs