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👩‍🎓 About Me

I'm a Data Scientist with a strong foundation in statistics, machine learning, and data science. My passion lies in transforming complex data into actionable insights that drive meaningful decisions.

🎯 Areas of Expertise:

  • 🤖 Machine Learning & Deep Learning
  • 🏥 Health Data Analysis & Medical AI
  • 📊 Statistical Modeling & Predictive Analytics

💡 Mission: To explore how data-driven methodologies can learn, adapt, and enhance decision-making while maintaining precision and responsibility.

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🎓 Education

Period Degree Institution Grade
08.2022 – 12.2024 Master of Science in Computing Sciences, Data Science
 Tampere University, Finland 4.62 / 5.00
01.2016 – 01.2020 Bachelor of Science in Information and Communication Engineering Bangladesh University of Professionals, Bangladesh 3.83 / 4.00

🛠️ Skills & Technologies

💻 Programming Languages

🤖 Machine Learning, Data Science & Visualization

⚙️ Tools, Platforms, & IDEs

📄 Technical Documentation

📑 Research & Publications

📄 Conference Paper

Performance Comparison of Machine Learning Techniques in Identifying Dementia from Open Access Clinical Datasets (Springer, 2021)

👩‍💻 Authors: Miah, Y., Prima, C.N.E., Seema, S.J., Mahmud, M., Shamim Kaiser, M.

🔗 Read Paper

Conference Paper

  • 🔍 This work compared several ML techniques—including SVM, Logistic Regression, ANN, Naive Bayes, Decision Tree, Random Forest, and KNN—for dementia identification using open-access clinical datasets.
  • 📈 Results showed that SVM and Random Forest performed best on datasets such as OASIS, ADNI, and DementiaBank.

📘 Research Paper Status: Under Review

Early risk factor prediction in chronic kidney disease diagnosis using feature selection and machine learning algorithms

👩‍💻 Authors: Chowdhury Nazia Enam Prima, Martti Juhola

Journal: Methods of Information in Medicine

Journal Paper

  • 🔍 Proposed a methodology for early identification of the top 10 CKD risk factors using feature importance (tree-based models) with sequential feature selector, and ReliefF algorithm.
  • 🤖 Tested 8 ML algorithms: RF, DT, SVM, GB, KNN, NB, LR, and an ensemble voting classifier using grid search cross-validation for hyperparameter tuning.
  • 📈 Achieved strong performance with accuracies ranging from 86% to 98%.

🎓 Academic Theses

DegreeYearTitleFocus Areas
M.Sc.2024Early risk factor prediction in chronic kidney disease diagnosis incorporating feature selection and machine learning algorithms 🔗Read PaperMachine learning algorithms,Health Data Analytics
B.Sc.2019An Ensemble approach combining machine learning algorithms for improving dementia prediction through parameter tuningEnsemble Learning, Machine learning, Disease Prediction

🚀 Featured Projects

🫀 Heart Disease Detection

Goal: Building a predictive classification model to identify individuals with heart disease accurately.

Tech Stack:

Heart Project
  • ✅ Implemented EDA, Data Preprocessing, and Predictive Modelling using 9 ML algorithms.
  • 💡 AdaBoost showed highest accuracy of 86.84% with 0.93 recall.
  • 📊 Most classifiers performed above 80% accuracy.
🔗 View Project
🏠 House Price Prediction

Goal: Predicting house prices using regression techniques in ML.

Tech Stack:

House Project
  • ✅ Implemented ML models using 3 regressors — RF, LR, and GB.
  • 📊 Evaluated models with RMSE, MAE, and R².
  • ⭐ Gradient Boosting performed best with R² = 0.82 and MAE = 0.14.
🔗 View Project

🏅 Awards & Recognition

Year Title / Role Organization Description
2022 🎓 Finland Scholarship Tampere University Recipient of the prestigious Finland Scholarship to pursue M.Sc. in Data Science.
2019–2020 🌐 Social Media Coordinator IEEE BUP Student Branch — Women in Engineering (WIE) Affinity Group Contributed to promoting gender diversity and inclusion in STEM by leading social media outreach campaigns, creating awareness of research opportunities, and organizing events.

📬 Let's Connect!

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