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This project has been completed for The Warren Group company; a Boston-based organization formed in 1872. It collectes the property data from the past twenty-two years. The outcome of this project is to estimate the future value of a property in the state of Massachusetts.
Analyses implements two models: Decision Tree and Random Forest. It is based on two reserch studies 'Building Risk Prediction Models for Type 2 Diabetes Using Machine Learning Techniques' by Xie Z, Nikolayeva O, Luo J, Li D. and 'Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting.' by Collins GS, Mallett S, Omar O, Yu LM.
LendingClub Loan Data Analytics (R and Tableau)
LendingClub Loan Data Analytics (R and Tableau)
This project has been realized by applying machine learning techniques to LendingClub loan data. LendingClub is a peer-to-peer lending platform which has been bringing borrowers and lenders together since 2007. It offers various loan products to interested parties through their proprietary technology platform. The scope of this project is to build machine learning models to predict the future interest rate and loan grade.
The goal of this project is to build a linear prediction model to estimate the Net Charge-Off Rate (NCO) from the commercial and industrial loan portfolio of Huntington Bancshares Incorporated under the basic and severely adverse macroeconomic scenario.
The following study have been done on “Wine Quality” dataset downloaded from UCI Machine Learning Repository and represents Portuguese "Vinho Verde" wines. The analysis addresses three hypothesis questions which undergo statistical analysis in Excel through summary statistics, constructing histograms and various charts, running t-test statistics, multiple regressions and ANOVA in order to accept or reject the hypothesis.
Airline Delay and Cancelation Data.pdfThe project explores the assumption that weather has influence on flight delays from Boston Logan airport for the Christmas holidays. It focuses on two questions: the likelohood of a flight to be delayed due to wether conditions and a flight to arrive on time.
The main objective of the study is to analyze the financial position of both companies. The purpose of this study is to examine the impact of the acquisition on acquirer companies’ financial performance.
After the financial analysis of both companies, the comparison of the Microsoft’s outlook before the acquisition of LinkedIn and the impact after the social network joins the giant software maker follows.
This project ecompasses the use of MS Visio to draw a entity relationship diagram (Crow's Foot ERD) for given Red Sox tickets data. After that, we used SQL Server database to obtain the queries to answers the questions related to the data.
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