• Madhanraj KN
• Piyush R

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 building logistic regression model, we plan evaluate our model using different scaling techniques and sampling methods. In this study we will apply different scaling techniques (MinMaxScaler, StandardScaler, and RobustScaler), and use different sampling methods (simple random sampling and stratified sampling) to split the data into training and testing sets. We'll then compare the performance of the models based on accuracy, precision, recall, and F1 score.

Type 2 Diabetes, Logistic Regression, Min Max Scaling, Standardization, Robust Scaling

"Prediction of Type-2 Diabetes using Logistic Regression, and selection of optimal Scaler and Sampling Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.e576-e584, June-2024, Available :http://www.jetir.org/papers/JETIR2406475.pdf

"Prediction of Type-2 Diabetes using Logistic Regression, and selection of optimal Scaler and Sampling 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. ppe576-e584, June-2024, Available at : http://www.jetir.org/papers/JETIR2406475.pdf