• Abhishek N
• Piyush Raj

This study aims to utilize machine learning, specifically logistic regression, to predict an individual's likelihood of having diabetes based on medical data, addressing a pressing global health concern. In addition to logistic regression, in this study we plan to explore techniques of scaling and varying range of train-test data ratios for each scaling technique and compare performance and accuracy of each scaler to identify best performing scaler and optimal train-test split ratio.

Type 2 Diabetes, Logistic Regression, Min Max Scaling, Standardization, Robust Scaling, Divide by max.

"Prediction of Type-2 Diabetes using Logistic Regression and selection of optimal Normalization and Data Reduction Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.f211-f220, June-2024, Available :http://www.jetir.org/papers/JETIR2406522.pdf

"Prediction of Type-2 Diabetes using Logistic Regression and selection of optimal Normalization and Data Reduction Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppf211-f220, June-2024, Available at : http://www.jetir.org/papers/JETIR2406522.pdf