Project 1 Customer Service

Resume

Data Scientist

Personal details

Name Prabakaran Sellamuthu

Email address *******@*****.***

Phone number +573*********

Address Department of Accountings and Finance, Faculty of Economics and Administrative, Cali, Cali, Valle del Cauca, Colombia, 764001 Valle Del Cauca

Date of birth April 17th, 1972

Place of birth Tamil Nadu

Driver's license 11/07/2022/TN/34

Gender Male

Nationality Indian

Civil status Married

Website javerianacali.edu.co/profesores/sellamuthu-prabakaran LinkedIn linkedin.com/in/praba-karan-6894b832

Education

Jun 2021 - May 2022 PGP in Data Science

Purdue University,, West Lafayette, IN, USA

During my course, I learned techniques such as PYTHON, Machine learning, R Studio, Tableau, NLP, and Artificial intelligence to extract meaningful information and to predicted future patterns and behaviors. Gained advances in technology, the internet, social media, and the use of technology have all increased access to big data. During this course, did more than 8 projects in the field of data science and used growing as technology advances and big data collection and analyzed techniques and I became more sophisticated in this field. Project completed during PGP Data Science (as on 15th May 2022) Project 1 - High value customers identification for an E-Commerce company Objective: - Find significant customers for the business who make high purchases of their favorite products. The organization wants to roll out a loyalty program to the high-value customers after identification of segments. Analysis Tasks: Used R Studio

• Used the clustering methodology to segment customers into groups.

• Used the K Means and Hierarchical clustering algorithms.

• Identify the right number of customer segments.

• Provided the number of customers who are highly valued.

• Identified the clustering algorithm that gives maximum accuracy and explained robust clusters.

Project 2 - High value customers identification for an E-Commerce company Objective: One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock sometimes, due to the inappropriate machine learning algorithm. Analysis Tasks: Used R Studio

Basic Statistics tasks -Task performed

• Which store has maximum sales.

• Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of mean to standard deviation.

• Which store/s has good quarterly growth rate in Q3’2012.

• Some holidays have a negative impact on sales. Found out holidays which have higher sales than the mean sales in non-holiday season for all stores together.

• Provide a monthly and semester view of sales in units and give insights. Statistical Model - Task performed

• For Store 1 – Molded prediction models to forecast demand.

• Linear Regression – Utilized variables like date and restructure dates as 1 for 5 Feb 2010 (starting from the earliest date in order). Hypothesize if CPI, unemployment, and fuel price have any impact on sales.

• Change dates into days by creating new variable.

• Selected the model which gives best accuracy.

Project 3 - Comcast Telecom Consumer Complaints

Objective: Comcast is an American global telecommunication company. The firm has been providing terrible customer service. They continue to fall short despite repeated promises to improve. Only last month (October 2016) the authority fined them a $2.3 million, after receiving over 1000 consumer complaints. The existing database will serve as a repository of public customer complaints filed against Comcast. It will help to pin down what is wrong with Comcast's customer service. Analysis Task - Used Python

To Perform these tasks, I used different Python libraries such as NumPy, SciPy, Pandas, scikit-learn, matplotlib, and BeautifulSoup.

• Imported data into Python environment.

• Provided the trend chart for the number of complaints at monthly and daily granularity levels.

• Provided a table with the frequency of complaint types. Which complaint types are maximum i.e., around internet, network issues, or across any other domains.

• Created a new categorical variable with value as Open and Closed. Open & Pending is to be categorized as Open and Closed & Solved is to be categorized as Closed.

• Provide state wise status of complaints in a stacked bar chart. Use the categorized variable from Q3. Provide insights on: (Which state has the maximum complaints and Which state has the highest percentage of unresolved complaints)

• Provided the percentage of complaints resolved till date, which were received through the Internet and customer care calls.

• Analyzed the results and provided with insights. Project 4 - California Housing Price Prediction

Objective: The project aims at building a model of housing prices to predict median house values in California using the provided dataset. Analysis Tasks - Used Python

• Build a model of housing prices and predicted median house values in California using the provided dataset.

• Train the model to learn from the data and predicted the median housing price in any district, given all the other metrics.

• Predicted housing prices based on median income and ploted the regression chart for it.

Project 5 - Mercedes-Benz Greener Manufacturing

Objective: Reduce the time a Mercedes-Benz spends on the test bench. Analysis Tasks - Used Python

• Identified the column(s), the variance is equal to zero, then I removed those variable(s).

• Checked for null and unique values for test and train sets.

• Applied label encoder.

• Performed dimensionality reduction.

• Predicted my test df values and used XGBoost.

Project 6 - Building user-based recommendation model for Amazon Objective: The dataset provided contains movie reviews given by Amazon customers. Reviews were given between May 1996 and July 2014. Analysis Task - Used Python

1. Exploratory Data Analysis: • Found the movies have maximum views/ratings?

• Found the average rating for each movie? And defined the top 5 movies with the maximum ratings. • Defined the top 5 movies with the least audience. 2. Recommendation Model: Some of the movies hadn’t been watched and therefore, are not rated by the users. Netflix would like to take this as an opportunity and build a machine learning recommendation algorithm which provides the ratings for each of the users. • Divided the data into training and test data. • Figured a recommendation model on training data. • Made the predictions on the test data.

Project 7 - Building user-based recommendation model for Amazon Objective: Create a dashboard to compare all the parameters mentioned above for different countries, to strategize market penetration and to target new customers.

Analysis Task - Used Tableau - • Created a geographic map showing the countries' fields. Colored the map based on the income column from the secondary dataset. • Included a webpage to show data from the world bank webpage driven by an URL action from a geography graph. • Created a KPI Table to show the comparison between the selected period and the period prior to the selected one. • Created Growth Indicator Shapes based on the Growth %. • Created a trend line to show the selected category values. Created a dashboard filter for income group to be applied for all charts with the filter action enabled in the map as well.

Project 8 - Comparison of Region Based on Sales

Objective: Help the organization by creating a dashboard to visualize the sales comparison between two selected regions.

Analysis Task - Used Tableau• Selected Sample Superstore as Dataset • Created a hierarchy called Location for the variable Country. • Created two parameters: Primary Region and Secondary Region with all regions listed in them. Here, primary, and secondary region are the two regions where the sales are being compared. • Created a First Order Date. • Created a dashboard.• Partition the dashboard to display the below details of Primary Region and Secondary Region.

Project 9 - Retail – PGP (Final Capstone)

Objective: It is a critical requirement for business to understand the value derived from a customer. RFM is a method used for analyzing customer value. Customer segmentation is the practice of segregating the customer base into groups of individuals based on some common characteristics such as age, gender, interests, and spending habits. Perform customer segmentation using RFM analysis. The resulting segments can be ordered from most valuable (highest recency, frequency, and value) to least valuable (lowest recency, frequency, and value). Project Task Week 1 (Data Cleaning) - Used Python

• Performed a preliminary data inspection and data cleaning.

• Performed cohort analysis (a cohort is a group of subjects that share a defining characteristic). Observe how a cohort behaves across time and compare it to other cohorts.

• Build a RFM (Recency Frequency Monetary) model. Recency means the number of days since a customer made the last purchase. Frequency is the number of purchases in each period. It could be 3 months, 6 months or 1 year. Monetary is the total amount of money a customer spent in that given period. Therefore, big spenders will be differentiated among other customers such as MVP (Minimum Viable Product) or VIP.

• Calculated RFM metrics.

• Build RFM Segments. Give recency, frequency, and monetary scores individually by divided them into quartiles.

Project Task: Week 2 (Data Modeling)

• Created clusters using k-means clustering algorithm.• Created a dashboard in tableau by choosing appropriate chart types and metrics useful for the business. Project 10 - Healthcare PGP (Final Capstone)

Objective: NIDDK (National Institute of Diabetes and Digestive and Kidney Diseases) research creates knowledge about and treatments for the most chronic, costly, and consequential diseases. The dataset used in this project is originally from NIDDK. The objective is to predict whether a patient has diabetes, based on certain diagnostic measurements included in the dataset. Build a model to accurately predict whether the patients in the dataset have diabetes or not. Project Task: Week 1 (Data Exploration) - Used Python

• Performed descriptive analysis. Understand the variables and their corresponding values. On the columns below, a value of zero does not make sense and thus indicates missing value:

• Visually explored these variables using histograms. Treat the missing values accordingly.

• There are integer and float data type variables in this dataset. Created a count

(frequency) plotted describing the data types and the counted of variables.

• Checked the balance of the data by plotting the count of outcomes by their value.

• Created scatter charts between the pair of variables to understand the relationships.

• Performed correlation analysis. Visually explore it using a heat map. Project Task: Week 2 (Data Modeling) – Used Python

• Devised strategies for model building. It is important to decide the right validation framework. Express your thought process.

• Applied an appropriate classification algorithm to build a model.

• Compared various models with the results from KNN algorithm.

• Created a classification report by analyzing sensitivity, specificity, AUC (ROC curve), etc.

• Data Reporting: Created a dashboard in tableau by choosing appropriate chart types and metrics useful for the business.

Project 11 - Real estate - PGP (Final Capstone)

Objective: A banking institution requires actionable insights into mortgage-backed securities, geographic business investment, and real estate analysis. The mortgage bank would like to identify potential monthly mortgage expenses for each region based on monthly family income and rental of the real estate. A statistical model needs to be created to predict the potential demand in dollars amount of loan for each of the region in the USA. Also, there is a need to create a dashboard which would refresh periodically post data retrieval from the agencies. The dashboard must demonstrate relationships and trends for the key metrics as follows: number of loans, average rental income, monthly mortgage and owner’s cost, family income vs mortgage cost comparison across different regions. Project Task: Week 1 (Data Import and Preparation:) – Used Tableau

• Imported data.

• Figured out the primary key and look for the requirement of indexing.

• Gauged the fill rate of the variables and devise plans for missing value treatment.

• Exploratory Data Analysis (EDA): Performed debt analysis.

• Performed EDA and come out with insights into population density and age.

• Created bins for population into a new variable by selecting appropriate class interval so that the number of categories don’t exceed 5 for the ease of analysis.

• Detailed my observations for rent as a percentage of income at an overall level, and for different states.

• Performed correlation analysis for all the relevant variables by creating a heatmap and described my findings.

Project Task: Week 2 (Data Pre-processing) – Used Tableau

• The economic multivariate data has a significant number of measured variables. The goal was to be found where the measured variables depend on a few smaller unobserved common factors or latent variables.

• Each variable is assumed to be dependent upon a linear combination of the common factors, and the coefficients are known as loadings. Each measured variable also includes a component due to independent random variability, known as “specific variance,” because it is specific to one variable. Obtained the common factors and then plot the loadings. Use factor analysis to find latent variables in our dataset and gain insight into the linear relationships in the data.

• Data Modeling: Build a linear Regression model to predict the total monthly expenditure for home mortgages loan.

Jan 2003 - Dec 2006 Doctor of Philosophy (Ph. D)

Indian Institute of Technology Roorkee,, Roorkee India Project - Title: The Statistical Mechanics of Financial Markets Duration: 4 Years

Description: The overriding objective of this research project was to carry this convergence/unification of physics and finance program further through a study of the symmetry groups of the dynamical equations relevant to financial processes and, as mentioned above, intertwining the physics & finance through stochastic processes to facilitate (i) the evolution of a model of financial markets amenable to the quantum mechanical framework and (ii) the generalizations of extant results to enhance their domain of applicability.

Jun 2000 - Dec 2001 Master of Technology (M. Tech) Indian Institute of Technology Dhanbad, Dhanbad, India Project - Title: Working Capital Management in a Manufacturing Organization-A Case study of TISCO

Duration: 8 Months

Description: A comparative study has been undertaken between the TISCO and Steel Industry to identify the trend and feasibility of adopting a sound Working Capital Management policy. In this thesis, there was a systematic presentation of the analysis relating to the working management policy adopted in the manufacturing organization. The study focused attention on the various functions of Working Capital Management in TISCO and comparative analysis has been made with Steel Industry, taking into consideration seven years’ time period. Four functional areas have been identified as analyzed data for both TISCO and Steel Industry. Discussed Cash Management, Credit Management, Inventory Management, and

Management of the Operating Cycle for a period of seven years, ranging from 1993 to 1999.

Short Term Project - Title: Emerging trend in Tobacco consumption in India

Duration: 2 Months

Description: Analyzed the attitude of Tobacco consumers and sellers while using cigarettes by using of Questionnaires. Jun 1990 - Jun 1994 Bachelor of Engineering (B. E) Madras University,, Chennai, India

Project – Title: Agitative type of Component Washing Machine Duration: 8 Months

Description: A component washing machine is used to remove rust, grease, oil, dirt, and other particles from the components with the help of a chemical solution. The reaction is activated by constant agitation of the container containing the solution and the components. The type of agitation that is being used in the industries is manual agitation. A pneumatic circuit has been used for the automatic process. The automated process of agitation of the container containing the components and the chemical solution using the principle of automatic reciprocal has the many advantages over the process of manual agitation, mechanical or hydraulic.

Employment

Aug 2015 - Present Associate Professor

Pontificia Universidad Javeriana Cali, Valle Del Caucai, Colambia, S.A Teaching and Research. I have published more than 25 research papers in the reputed international journals and international conference in the area of financial markets and mathematical model during this period. Jan 2013 - Jul 2015 Associate Professor

Business School, Universidad Del Norte, Barranquilla, Colombia, S.A. Teacing and Research. Coordinator for Business Finance, Financial Research Centre, Business School, Universidad Del Norte, Barranquilla, Colombia. Atlantic, S. A.

Nov 2008 - Sep 2012 Head & Asst. Professor

King Saud University (KSU), Riyadh, Kingdom of Saudi Arabia (KSA) Teach Finance and Risk Management subjects to undergraduate students using lectures, assignments, and research projects. Direct research programs of graduate students and advise on research matters. Research in the field of specialization and publish findings in scholarly journals or books. May serve on faculty committees dealing with such matters as curriculum planning and degree requirements and perform a variety of administrative duties. Conduct Workshop and presentation series for faculty. Developing the Course Curriculum for the Finance Department,

Developing the Course plan for the Finance Department, working for quality assurance to AACSB, EQUIS, and NCAAA accreditation process, Involved in projects and working papers, Established the Finance Department and Conducting Interviews for selecting faculty.

Jan 2007 - Nov 2008 Asst. Professor

University of Petroleum and Energy Studies,, New Delhi, Gurgaon, India. Developing the Course Curriculum for the Finance Department, Developing the Course plan for the Finance Department, working for quality assurance to AACSB, EQUIS, and NCAAA accreditation process, involved in projects and working papers, Established the Finance Department and Conducting Interviews for selecting faculty. Acted the responsibility as a Program Director for M.S (Energy Studies). Had full academic responsibility for this Program and Program Director for M. Tech (Petroleum Informatics).

Teach Finance and Risk Management subjects to undergraduate students using lectures, assignments, and research projects. Direct research programs of graduate students and advise on research matters. Research in the field of specialization and publish findings in scholarly journals or books. Served on faculty committees dealing with such matters as curriculum planning and degree requirements and perform a variety of administrative duties. Took the Class for the MBA (Power Management) students, M.S (Energy Trading) students, M.S (Energy System), MBA (Oil & Gas), and M. Tech (Petro Informatics) and revising the syllabus. Supervision for the student’s project works and interviewing for admission purposes.

Member of the Ph.D. Candidate selection committee. Skills

Leadership/Communication

Skills, Strategies Planning,

Team Buliding and

Leadership, Budgeting

expertise and Analysis, Strong

Analytical Skills, Operating

and Financial Policy

Development, Financial

Analysis, Analytical

Reasoning, Cost Benefit

Education Accounting, Capital

Finance and Data Collection &

Analysi

Excellent

Languages

English Fluent

Tamil Fluent

Spanish Good

Hindi Good

Hobbies

Playing Cricket, Watching TV News Channel, Doing Exercise and Gardening. Profile

I have had experience in commodity pricing model, agricultural derivatives, energy derivatives and weather derivatives. In addition, I have learned thoroughly the theoretical concepts of statistical mechanics & pricing of mathematical modelling Operation Research to apply them for analyzing the managing problems such as financial markets, supply-chain logistics in agricultural manufacturing industries, etc. I have published sixty research papers in the reputed international journals and international conference in the area of financial markets and mathematical model. Analytical

During my course, I learned techniques such as PYTHON, Machine learning, R Studio, Tableau, NLP, and Artificial intelligence to extract meaningful information and to predicted future patterns and behaviors. Gained advances in technology, the internet, social media, and the use of technology have all increased access to big data. During this course, did more than 8 projects in the field of data science and used growing as technology advances and big data collection and analyzed techniques and I became more sophisticated in this field. I am planned to implement the finance knowledge in Data science by using techniques such as PYTHON, Machine learning, R Studio, Tableau, NLP, and Artificial intelligence. This PGP Data Science would allow me to progress in this extremely niche domain besides deploying the concepts of engineering, computing, mathematics, and business for developing functional problem-solving skill. With PGP Data Science, I can help diverse industries and sectors like financial markets, energy markets, Health care industries, insurance companies, real estate., retails markets, management, and banking industries to develop the strategies that work. The multi – disciplinary field of data science combines computer science with probability, statistics, analysis, management skills, decision making and problem solving. Data science is also the answer to the most complex business and practical questions. So, I believe that the data science is future of AI, and it is beneficial to learn about it more cater to my interest. I hope to use the knowledge I gained from my Bachelor, Master, and Doctorate to perform meaningful analysis of large data sets. I have been intensively involved in learning mathematics throughout my education and had a particular interest in the subject. I am familiar with concepts such as multivariable calculus, elementary statistics, and probability theory. I have also studied data structure which were the beginning of my interest and played a crucial role in forming my choices. I wish to use my knowledge and skills in data science to provide well – sustained research and analyses for companies.

References

Mr Amuthan Krishnamurthi

Amazon, California

+1-408-***-****, *********@*******.***

Mr Dhanakodi Karur

T-Mobile, Seatle

+1-408-***-****, *********@*****.***

Qualities

Statistical analysis and computing, Machine Learning, Big data, Data Visualization, Data Wrangling, Mathematics, Statistics, Big Data, PYTHON, Machine learning, R Studio, Tableau, NLP, Artificial Intelligence, Financial Modeling, Financial Engineering, Financial Mathematice and Risk Management. Achievements

Research Publications (Journals/ Conference)

A. Discipline-Based Scholarship

Peer Review-Journal

1. Prabakaran.S, Singh.J.P,” A TOY MODEL OF FINANCIAL MARKETS”-Electronic Journal of Theoretical Physics (EJTP). ISSN 1729-5254, Vol.3, Issue No 11, pp. 11-27 (May 2006). INDEXED IN SCOPUS. 2. Prabakaran.S, Singh.J.P, “BLACK SCHOLES OPTION PRICING WITH STOCHASTIC RETURNS ON HEDGE PORTFOLIO”- Electronic Journal of Theoretical Physics (EJTP). ISSN 1729-5254, Vol 3, Issue No. 13 pp 19-28

(December 2006). INDEXED IN SCOPUS.

3. Prabakaran.S, Singh.J.P, “GROUP PROPERTIES OF THE BLACK SCHOLES EQUATION & ITS SOLUTIONS”- Far East Journal of Mathematical Sciences (FJMS). ISSN: 0972-0871, INDEXED IN SCOPUS, Volume 27 No. 1, pp. 15 - 25(October 2007).

4. Prabakaran.S, Singh.J.P, “ON THE DISTRIBUTION OF RETURNS & MEMORY EFFECTS IN INDIAN CAPITAL MARKETS” - International Research Journal of Finance and Economics. (IRJFE). ISSN 1450-2887, INDEXED IN SCOPUS, Issue 14, pp 165 – 176 (2008).

5. Prabakaran.S, Singh.J.P, “QUANTUM COMPUTING THROUGH QUATERNIONS.” - International Journal of Pure and Applied Physics (IJPAP). ISSN 0973-1776, Volume 4, Number 1 (2008) pp 87 - 96. 6. Prabakaran.S, Khalid Alkhathlan “MEMORY EFFECTS ON SAUDI ARABIAN STOCK MARKET – EMPIRICAL EVIDENCE” Enterprises Risk Management (ERM), ISSN 1937-7916, 2009, Vol. 1, No 2: E2, pp 87 - 96. 7. Prabakaran.S, K Ravichandran ‘BLACK SCHOLES MODEL – AN ECONOPHYSICS APPROACH’. Enterprises Risk Management (ERM), ISSN 1937-7916, 2010, Vol. 1, No. 1: E5, pp 115 - 127. 8. Prabakaran S et al. “INFLUENCE OF SERVICE QUALITY ON CUSTOMER SATISFACTION: APPLICATION OF SERVQUAL MODEL” International Journal of Business and Management (IJBM), ISSN 1833-3850, Vol. 5, No 4, April 2010, pp 117 - 124. INDEXED IN SCOPUS. 9. Prabakaran S et al. “APPLICATION OF SERVQUAL MODEL ON MEASURING SERVICE QUALITY: A BAYESIAN APPROACH” Enterprises Risk Management (ERM), ISSN 1937-7916, 2010, Vol. 1, No. 1:E9 pp 145 - 169.

10. Prabakaran.S, “THE STUDY OF MARKETS AND PRICES –THE THERMODYNAMICS APPROACH” International Journal of Pure and Applied Physics (IJPAP), ISSN 0973-1776, Vol 6, No 3 (2010), pp. 333–346. 11. Prabakaran. S, “MARKET FLUCTUATIONS – THE THERMODYNAMICS APPROACH” Global Journal of Finance and Management (GJFM), ISSN 0975- 6477, Vol 3, No 2 (2011), pp. 193-208. 12. Prabakaran S, “RATIONALITY IN ECONOMICS – THE THERMODYNAMICS APPROACH AND EVALUATION CRITERIA” in Journal of Empirical Economics (JEE). ISSN: 2310-3256 – Vol 3(2014), Issue 1. pp: 43-55. 13. Prabakaran S, “EXCHANGE RATE EQUILIBRIUM – THE THERMODYNAMICS APPROACH” in International Journal of Financial Economics (IJFE). ISSN: 2310-3280 – Vol 3(2014), Issue 1. pp: 11-24. 14. Prabakaran S, “THERMODYNAMICS DESCRIPTION IN THE COLOMBIAN STOCK MARKETS” in (IRJFE). ISSN: 1450-2887, Issue 126, October 2014 – SCOPUS INDEX. 15. Prabakaran S, “STOCK MARKET - THE ECONOPHYSICS APPROACH” in International Journal of Applied Business and Economic Research. (IJABER), ISSN: 0972-7302, Vol. 12, No. 3, (2014): 857-866, SCOPUS INDEX.

16. Prabakaran S and Dr. Carlos D. Paternina-Arboleda, “LAWS OF THERMODYNAMICS DESCRIPTION IN THE ECONOMIC SYSTEM” in International Journal of Applied Engineering Research (IJAER), ISSN: 0973-4562, Volume 10, Number 11 (2015) pp. 286**-*****., SCOPUS INDEX. 17. Prabakaran S "BLACK SCHOLES OPTION PRICING MODEL – BROWNIAN MOTION APPROACH" in Global Journal of Pure and Applied Mathematics (GJPAM). ISSN 0973-1768 Volume 11, Number 6 (2015), pp. 4587- 4602. SCOPUS INDEX.

18. Prabakaran S “STATISTICAL THERMODYNAMICS OF MONEY (THERMONEY)" in Journal International Journal of Applied Engineering Research (IJAER). ISSN 0973-4562 Volume 11, Number 5 (2016) pp 3409- 3420. SCOPUS INDEX.

19. Prabakaran S, “CONSTRUCTION OF RISK – NEUTRAL MEASURE IN A BROWNIAN MOTION WITH EXOTIC OPTION “in the Far East Journal of Mathematical Sciences (FJMS). Volume 100, Number 10, 2016, Pages 1643-1674 ISSN: 0972-0871, ISSN: 0972-0871, INDEXED IN SCOPUS. 20. Prabakaran S “MODELING AND PRICING OF WEATHER DERIVATIVE MARKET " in the Global Journal of Pure and Applied Mathematics (GJPAM). ISSN 0973-1768, Volume 13, Number 12 (2017), pp. 8103-8126. SCOPUS INDEX.

21. Prabakaran S “STOCHASTIC PROCESS ON OPTION PRICING BLACK - SCHOLES PDE– FINANCIAL PHYSICS (PHYNANCE) APPROACH " in Global Journal of Pure and Applied Mathematics (GJPAM). ISSN 0973-1768. Volume 13, Number 12 (2017). SCOPUS INDEX. 22. Prabakaran S – Chapter published - Springer Proceedings in Business and Economics, in Advances in Panel Data Analysis in Applied Economic Research, 2017 International Conference on Applied Economics (ICOAE) - 2018 – Springer publisher.

23. . Prabakaran.S, “ MODELING AND PRICING OF ENERGY DERIVATIVE MARKET” - International Journal of Engineering & Technology. (IJET). ISSN 2227-524X, INDEXED IN SCOPUS. 7 (4.10) (2018) 148-156. 24. Prabakaran.S, “ CONSTRUCTION OF PDE BLACK - SCHOLES WITH JUMP-DIFFUSION MODELS” - Far East Journal of Mathematical Sciences (FJMS): 0972-0871, ISSN: 0972-0871, Volume 110, Number 1, 2018, Pages 131-163 INDEXED IN SCOPUS.

25. Prabakaran. “THE BLACK SCHOLES OPTION PRICING MODEL FOR INSURANCE DERIVATIVE" - Journal Global Journal Of Pure And Applied Mathematics (GJPAM): ISSN 0973-1768 Volume 16, Number 1 (2020), pp. 131-144 in SCOPUS Q4.

26. . Prabakaran. S, “CONSTRUCTION OF THE BLACK SCHOLES PRICING MODEL IN THE STOCK MARKET BY USING OF BROWNIAN MOTION APPROACH” - Far East Journal of Mathematical Sciences (FJMS): 0972-0871, ISSN: 0972-0871, Volume 123, Number 2, 2020, Pages 139-161, INDEXED IN SCOPUS. 27. Prabakaran S, Jose U. Mora, Ph.D., and Dr. Isabel Cristina Garcia Arboleda. “A TEMPERATURE STOCHASTIC MODEL FOR OPTION PRICING AND ITS IMPACTS ON THE ELECTRICITY MARKET” Economic Analysis and Policy (EAP), WoS Q2, Under Elsevier 68 (2020) 58 - 77. 28. Prabakaran S and Dr. Animesh Acharjee, PhD. “CONNECTING DIFFERENT BRANCHES DOMAINS THROUGH MATHEMATICAL MODELLING: AN INTERDISCIPLINARY APPROACH” International Journal of Interdisciplinary Research Methods, Vol.7, No.3, pp. 31-47, December 2020, Published by ECRTD-UK Print ISSN: ISSN 2398-712X, Online ISSN: ISSN 2398-7138. 29. . Prabakaran. S, “DESCRIPTION OF COLOMBIAN ELECTRICITY PRICING DERIVATIVES” - International Journal of Finance Research (IJFR): e-ISSN: 2746-136X. Vol 2, No.3, September 2021, Pages 191 - 211. DOI: https://doi.org/10.47747/ijfr.v2i3.349.

Other Intellectual Contributions

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