OBJECTIVE

 A Data Science enthusiast with hands-on experience in handling data-driven solutions using Statistical and Machine Learning techniques.  With 3 years of experience as a Software Engineer in the Customer banking domain.

SKILLS

Machine Learning: Classification, Regression, Clustering | Statistical Methods: Predictive Analysis, Hypothesis, Exploratory Data Analysis | Programming Languages: Python, Java | Database Language: SQL, RDMS | G Suite: Sheets, Docs | Tools: Tableau 

PROJECTS

 Capstone Project: Hotel Cancellation Prediction

o Hotel booking cancellation is the biggest hardship for Revenue Management.

o The various techniques used in the predictive model building are descriptive statistics, outlier treatment, need for data standardization, and various performance metrics to validate the performance of predictions on Test & Train sets along with model tuning.

Models: CART, Random Forest, KNN, Gradient Boosting.

● Bank Customer Segmentation:

o Using the concepts of Hierarchical and K-Means clustering for the bank customer segmentation and creating clusters as Max payers or Full Payers, Non-Payers, Revolvers.

o The various techniques used are descriptive statistics, outlier treatment, the impact of scaling on clusters, cluster profiling. Models: Agglomerative Clustering, K-Means Clustering.

● Visualizing Insurance Claims using Tableau:

○ Create interactive dashboards and storyboards to provide high-level insights.

○ Explore the art of problem-solving with the aid of visual analytics