Project Data

SIDDESHWAR VASILI

Email ID: ****************@*****.***

in.linkedin.com/in/siddeshwarvasili

Mobile: +919*********

Professional Summary

* **** ** ********** ** active and involved team member in building models for client’s

important business problems like loan defaulter prediction, churn prediction, cross- sell

and up-sell, customer segmentation.

Good analytical skills and ability to manage large amounts of data.

Proven ability in managing multiple projects simultaneously, coordinating with team

members, and in meeting tight deadlines.

Good understanding of banking and telecom domains.

Experience presenting data in the form of tables and charts in order to make analytical

arguments.

Research Experience in Data Mining and Analytical Customer Relationship Management

(ACRM).

Researched, implemented Non-Negative Matrix Factorization (NMF) algorithm - NMF,

which helps Text Mining, Image pattern reorganization, Feature Selection.

Proficiency in MS Office applications, especially MS Excel and PowerPoint.

Good interpersonal and communication skills to work with business users in gathering

requirements.

We recently published a book on Data Quality Framework, Based on the work of

the respective Best Practices

http://idrbt.ac.in/publications/Frameworks/DQ%20Framework.pdf

Professional Experience Reserve Bank of India R&D Center (IDRBT), Hyderabad,

India

Research Associate/Data Analyst May 2013 – Present

Clients: State Bank of India and State Bank Associate Banks

Educational Qualifications Sri Venkateswara University

Master of Computer Applications (MCA), 2009 – 2012

Sri Venkateswara University

B.Sc (M.S.Cs), 2006 – 2009

Board of Intermediate Education, Andhra Pradesh

Intermediate, 2004 - 2006

Mathematics, Physics, Chemistry

Board of Secondary Education, Andhra Pradesh

SSC, 2003-2004

Technical Skills

Programming Languages Java, SAS

Operating Systems Windows XP/7

Web Development Tools HTML

IDE Tools Eclipse

Data Mining & Analytics Tools IBM SPSS Modeler V 15.0 with Text Mining, SAS

Enterprise Miner Client 12.1 with Text Mining, Knime V

2.1 integrated with Weka algorithms, Rapid Miner V 6.0

Databases Oracle 10g

Web servers Apache Tomcat6.0

Technical Projects

Major Project: CRM & Data Analytics for Public Sector Banks

Project Name#1: Defaulter Prediction Modelling

Description: This project mainly focuses on predicting the potential future default customers

based on the historical loan data of customers. In this process we build different predictive

models using techniques like Decision Trees, Logistic Regression, Neural Network, and Support

Vector Machine, and then applied the best model on the production data.

Responsibilities:

1) Identification of appropriate fields and collection of data from the banks

2) Data cleansing/ Data pre-processing/Data reparation

3) Features selection

4) Building the predictive models

5) Generating the prediction rules for better business understanding

6) Applied the best predictive model and scored the production data.

7) Detailed analysis on the output

Environment: SAS E-Miner Client 12.1, IBM SPSS Modeler 15.0, Knime V 2.1 integrated with

Weka algorithms, and Rapid Miner V 5.1

Project Name#2: Customer Churn Prediction Modelling

Description: Predicting the degree of Churn involved in each customer to reduce churn. This

project mainly focuses on predicting the potential future churners based on the historical savings

bank account data of customers. We built predictive models using different techniques like

Decision Tree, Logistic Regression, Neural Network, and Support Vector Machine and then

applied the best model on the production dataset.

Responsibilities:

1) Identification of appropriate fields and collection of data from the banks

2) Data cleansing/Data pre-processing/Data preparation

3) Features selection

4) Building the predictive models

5) Generating the prediction rules for business understanding

6) Applied the best predictive model and scored the production data.

7) Detailed analysis on the output

Environment: SAS E-Miner Client 12.1, IBM SPSS Modeler 15.0, Knime V 2.1 integrated with

Weka algorithms, and Rapid Miner V 5.1

Project Name#3: Customer Segmentation

Description: This project is mainly used for Target marketing and campaign management.

Customers are segmented based on their demographical and transactional details.

Responsibilities:

1) Collection of customer’s demographic and transactional data

2) Data cleansing/ Data preparation

3) Variables selection

4) Generating rules for different identified customer segments

Environment: SAS E-Miner Client 12.1, IBM SPSS Modeler 15.0, Knime V 2.1 integrated with

Weka algorithms, and Rapid Miner V 5.1

Project Name#4: Market Basket Analysis

Description: This project is mainly used to improve cross-sell and up-sell of the products. We

identified the products that are purchased together based on the customer buying patterns, and

then suggested the right product to the right customer at the right time to buy.

Responsibilities:

1) Collection of customer’s product transactional details

2) Identifying the products that go together very frequently using association rule mining

algorithms

Environment: SAS E-Miner Client 12.1, IBM SPSS Modeler 15.0, Knime V 2.1 integrated with

Weka algorithms, and Rapid Miner V 5.1

Project Name#5: Sentiment Analysis on feedbacks

Description: Sentiment Analysis aims to determine the attitude of a speaker or a writer respect

to some topic in program.

Responsibilities:

1) Collection of participants feedbacks.

2) Data cleansing/Data pre-processing.

3) Identified the positive, negative and neutral scores.

Environment: Rapidminer6, java, Excel.

Place: Hyderabad

Date: Siddeshwar V

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