Place: kolar

Phone:821-***-****

E-mail: admcbk@r.postjobfree.com

NAME SIRISHA S J

OBJECTIVE Seeking a challenging position, where I can utilize my technical and personal knowledge for the growth of the organization as well as my career growth.

SUMMARY

Hands-on experience in statistics (Hypothesis, Chi-square, Anova, Linear Regression, Logistic Regression, K means Cluster, Decision Tree and Random Forest, KNN).

Good understanding on Data Analysis using NumPy and Pandas in Python and Matrices and DataFrames in R.

Worked extensively on Data Visualization using Seaborn in Python and GGplot in R.

Worked on few Visualization projects using Tableau.

Good hands-on experience in building Algorithms which include Decision Tree, Linear Regression, Random Forest, Logistic Regression, K means Cluster, KNN.

Worked on many projects using statistical, analytical and machine learning techniques for model design and productive analysis.

Completed training in Data Science using Python and R.

Worked as an Intern for three months at M/S Digicia Technologies Pvt Ltd.

TECHNICAL

SKILLS

Software Application: Microsoft Word (Word, Excel, Power Point) Programming

Languages:

Python, R, C

Data Analysis: NumPy and Pandas – Python, Matrix and Dataframe –R

Data Visualization: Seaborn – Python, GGplot – R, Tableau Machine Learning: Linear Regression, Decision Tree, Random Forest, Logistic Regression, K-means cluster,

KNN

INTERN

PROJECTS

Project 1: Building Credit Score Prediction model for a bank Description: Banks often depend on credit score prediction models to approve or deny a loan request. A good prediction model is necessary for a bank so that they can provide maximum credit without exceeding the risk threshold. This data science project uses credit score dataset which has fairly large volume of data (~250K). The prediction models were built following various approaches like - random forest, gradient boosting and logistic regression. A predictive model was built that will automatically generate each applicant with a credit score which is human readable and easy to interpret.

Activities Performed: Importing Data sets

Data Set Exploration

Visualization of data

Removing the Outliners

Building Regression Model

Decision Tree

Project 2: Time series clustering in Identifying the bundles Description: The weekly sales transaction datasets consists of weekly purchased quantities of 800 products over 52 weeks. Normalized values were provided too. The objective of this data science project in R is to find out product bundles that can be put together on sale. Typically Market Basket Analysis was used to identify such bundles. We compared the relative importance of time series clustering in identifying product bundles. Activities Performed: Importing Data sets

Data Set Exploration

Visualization of data

Removing the Outliners

Building Regression Model

K means Clustering

ACADEMIC

PROJECTS

Project 1: Bike sharing and renting.

Description: To predict the count of the vehicles that goes on rent at a certain degree of temperature and to forecast the future demand of vehicles. Analytical Tools: Excel, Python, R

Technical Tools: Linear Regression

Project 2:Predicting the choice of college

Description:To predict whether the college belongs to a private college or public college and the probability of getting a private college. Analytical Tools: Excel, Python

Technical Tools:Logistic Regression

Project 3: Banking peer group lending

Description: To predict interest rates based on borrowers and loan attributes. To identify key drivers of interest rates and create an application to predict interest rate based on given customer and loan attribute. Analytical Tools: Excel, Python, R

Technical Tools: Linear Regression

Project 4: Customer Segmentation for one of the leading Credit card company

Description: To develop the customer segmentation, to understand the customer behavior and to define a strategy for marketing Analytical Tools: Excel, R, Python

Technical Tools: Segmentation(K-means clustering)

Project 5: Predicting house price

Description: To identify key drivers of the house price and predict the house price based on drivers.

Analytical Tools: Excel, R

Technical Tools: Decision Tree

WORK

HISTORY

INTERNSHIP, DIGICIA TECHNOLOGIES PVT.LTD.

March 2019 – May 2019

Worked as an intern in Digicia Technologies Pvt. Ltd for a period of 3 months. As an Intern I was able to work on various activities of Data Science which include – Data Analysis, Data Visualization and Machine Learning.

EDUCATION

Course Institution Board/University Year of

completion

Aggregate

Bachelor of

engineering

(CS)

Government

engineering

college

Ramanagar

Visvesvaraya technological

university

2019

70 %

II PUC Alvas Pre-

University

college

Moodbidri

PUC Board

2015

81.6 %

10 th

SFS High

School

Malur

SSLC Board

2011

84.96 %

Contact this candidate