VRAJESH K S

E: ac4bfn@r.postjobfree.com

M: +91-960*******

LinkedIN: Vrajesh K S

BLOG:

http://vrajesh94.blogspot.in/

GITHUB:

https://github.com/vrajesh26

EDUCATION

PGP Business Analytics 2018

Praxis Business School

5.61/8.0 CQPI

B.E (ECE) 2015

Meenakshi Sundararajan

Engg. college(Anna Univ)

8.59/10.0 CGPA

XIIth 2011

Velammal Matric. 96.41%

school(State Board)

Xth 2009

Velammal Matric. 90%

school(State Board)

EXPERIENCE

COGNIZANT PROGRAMMER ANALYST

From [08/15] – To [06/17]

Worked for Medical devices project where I was responsible for developing SQL queries for data migration activity from one database to another. Involved in the functional testing of reports developed using Jreview, SAS, Electronic case report form and edit checks.

Received “Pillar of the Month” award for consistent performance and contribution to the project.

ANALYTICS PROJECTS

PREDICT RETAIL CHURN

PROBLEM STATEMENT- Several customers switch retail stores. It is known when a customer is going to churn, so the store can take measures to hold back the customers. Our aim is to build a model which can predict such customer churn.

DATASET- Who is Leaving? (Kaggle)

TOOLS USED- R

APPROACH- SMOTE algorithm to deal with the imbalanced dataset followed by Decision Tree algorithms, logistic regression and ensemble technique were applied for prediction.

LEADERBOARD RANK- 11

SALES PRICE PREDICTION (DATA TALES BEYOND

INFINITY)

PROBLEM STATEMENT- ChennaiEstate is a real estate firm based in Chennai that is involved in the property business for the past 5 years. Our aim is to predict the real estate sales price of a house based upon various features of the house and the sales transaction. DATASET- Data Tales Beyond Infinity (Analytics Vidhya) SKILLS

R 3.5/5

SQL 3.5/5

Python 2/5

NoSQL 2/5

Tableau 2/5

TOOLS USED- R

APPROACH- Dealt with missing data, feature engineering, Random Forest, regression models followed by variable selection and regularization techniques.

LEADERBOARD RANK- 46

LOAN PREDICTION

PROBLEM STATEMENT- Dream Housing Finance company which deals in all home loans wants to automate the loan eligibility process based on customer details. To automate this process, they have given a problem to identify the customers’ segments, those are eligible for loan amount so that they can specifically target these customers. DATASET- Loan Prediction III (Analytics Vidhya)

TOOLS USED- R, Python

APPROACH- Performed missing data imputation, feature engineering and classification algorithms, SVM, logistic regression were applied to compare and predict which model gives best accuracy.

LEADERBOARD RANK- 234

PREDICT EMPLOYEE ATTRITION

PROBLEM STATEMENT- Organizations face huge costs resulting from employee churn. Our aim is to predict which individuals might leave based on patterns and use key variables that influence churn based on IBM HR Analytics employee attrition data.

TOOLS USED- R

APPROACH- Under-sampling is performed to reduce the imbalance nature of data of interest, binning numerical variables followed by Decision Tree algorithms, logistic regression and ensemble techniques were applied for prediction.

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