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Data Scientist
June 21, 2026 – Present
BRS
February 12, 2023 – February 12, 2023
book recommandation system using k-means agloritham
View ProjectAugmented-Reality-Application-for-Wearing-Watches
July 3, 2021 – October 16, 2021
Augmented-Reality-Application-for-Wearing-Watches — GitHub repository
View ProjectfinalprojectGateway
June 13, 2021 – June 13, 2021
finalprojectGateway — GitHub repository
View ProjectDMW_Project
December 3, 2020 – December 3, 2020
Wine tasting is an art since decades. The main purpose of this study is to predict wine quality based on physicochemical data. These data sets contain around 1600 instances for red wine with 11 features of physicochemical data such as alcohol, chlorides, density, total sulfur dioxide, free sulfur dioxide, residual sugar, and pH. The following five different algorithms were used to classify the quality of red wine: random forests, support vector machines, naïve bayes, logistic regression and decision tree. There are 3 quality classes of red wine. The most successful classification was obtained by using Naïve Bayes Algorithm with 99.25 accuracy rate. In this study, it is also observed that the use of principal component analysis in the feature selection increases the success rate of classification in Naïve Bayes Algorithm
View ProjectCultural Fit Analysis
The candidate shows a broad interest in various technical areas, including web development (HTML, CSS, JavaScript, PHP) and augmented reality, in addition to data science. While this indicates versatility, the depth of focus on Data Science for a senior role is not clearly established. The projects are all personal, which is common for an entry-level candidate, but for a senior role, industry experience or more complex, collaborative projects would be expected. The target role is 'Data Scientist', and while there are relevant projects, the overall project diversity might suggest a less specialized focus than typically desired for a senior role.
Soft Skills & Operational Fit
The candidate's profile primarily showcases technical project work. There is insufficient data to assess soft skills such as teamwork, leadership, or communication style, or operational fit beyond technical capabilities.