プロフィール詳細

プロジェクトを作成

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Dr. Chikodi R.に依頼

Nigeria

Senior Analyst | Data-Driven Problem Solver

プロフィール概要

Accomplished Statistician, Data Analyst, and Business Analyst with over five years of experience delivering data-driven insights and operational improvements across diverse industries. Adept at leveraging analytical tools, statistical models, and business intelligence platforms to optimize processes, identify trends, and drive strategic decision-making. Demonstrates expertise in bridging technical and business domains, facilitating stakeholder engagement, and delivering actionable solutions that align with organizational goals. Skilled in SQL, R, Power BI, Tableau, and Excel, with a proven ability to translate complex data into clear insights that improve efficiency, reduce costs, and enhance customer experiences. Passionate about leveraging analytics to solve complex challenges and achieve measurable results.

専門分野

• ☆ Biostatistics
• ☆ Statistics
• ☆ Clinical Trials
• ☆ Clinical Trial Design
• ☆ Clinical Trials Data Analysis
• ☆ Business Operations
• ☆ Business Strategy
• ☆ Business Statistics
• ☆ Data-Driven Decision Making
• ☆ Political Economy
• ☆ Macroeconomics
• ☆ Econometrics
• ☆ Microeconomics
• ☆ Market Research
• ☆ Qualitative Market Research
• ☆ Quantitative Market Research
• ☆ Marketing Analytics

サービス

• Writing

Writing Technical Writing, General Proofreading & Editing

職務経験

Business Analyst / Business Intelligence Consultant

Topdel Engineering Limited

1月 2023 - 現在

Business Analyst

Adamore (Nigeria)

1月 2023 - 現在

Data Analyst

JSG Industrial Limited

1月 2021 - 12月 2023

Research and Development officer

Daraju Industrial Limited

1月 2019 - 1月 2021

学歴

(Business Analysis)

Piston and Fusion Business Aademy

6月 2024 - 8月 2024

Bachelors of Technology in Statistics

Federal University of Technology Owerri

2月 2012 - 12月 2017

Bachelors of Technology in Statistics (Statistics)

Federal University of Technology Owerri

1月 2012 - 12月 2017

認定資格

• Business Analyst

  Piston and Fusion Business Academy

  1月 2023 - 現在

出版物

JOURNAL ARTICLE

this paper, we aimed to predict house price in Norwich and evaluate the factors that drive the price. To achieve this, we trained four boosting (Gradient Boosting, XGBoost, LightGBM, and CatBoost) to predict the house price. The performance of these models was evaluated in a standard evaluation approach and post-hoc residual evaluation approach within three designed instances (testing, training, and combined [testing + training]). The predictive performance and significant predictors were identified, with Beds, Baths, Sqm, and other features showing high significance, while age of the house was not significant. We found out that GradientBoost and XGboost are closely related in their residuals, while LightBoost operates independently .

This study analyzed economic indicators, to assess patterns in Nigeria's GDP, poverty rate, and other key variables over a multi-year period, using a combination of predictive modeling, correlation analysis, clustering, and dimensional reduction techniques. The predictive analysis revealed a strong alignment between the observed and predicted values for GDP and poverty rates, with minor error margins and accuracy levels close to 100%, underscoring the model’s robustness in forecasting these economic indicators. Correlation analysis highlighted significant positive relationships between GDP and other variables, particularly importation, export, and exchange rates, while inflation and MPR showed moderate associations. Cluster analysis identified three distinct periods with economic similarity: 2014–2018, marked by stable growth; 2019–2022, likely influenced by external shocks or adjustments; and 2023, standing out as unique, potentially due to significant economic changes. The tDistributed Stochastic Neighbor Embedding (t-SNE) further validated these clusters by revealing distinct separations between the identified periods. The combined results offer insights into Nigeria’s economic patterns, revealing periods of stability and adjustment, and highlighting variables that significantly influence GDP and poverty, aiding policymakers in identifying trends and making data-informed decisions .

Adekunle, J.D., Oyeniran, M.I., Ayanlowo, E.J., Dada, A.M., and Robert, C.O. (2024) “Machine Learning Application in Aquaculture: Predicting Fish Aggressive Behavior and Growth in Diverse Aquatic Environment,” TIJER- International Research Journal, vol. 14, Issue- 10, pp. 198-215 .

Adekunle, J.D., Oyeniran, M.I., Dada, A.M., Robert, C.O., Sule, H.S., & Akinpelu, T.T. (2024). “A Smart Heart Diagnostic Machine: Developing an Effective Predictive Classification Algorithm.” Global Scientific Journal, 12(9) pg. 804-824. http://www.globalscientificjournal.com/researchpaper/A_Smart_Heart_Diagnostic_Machine_Developing_an_Effective_Predictive_Classification_Algorithm.pdf.