Diabetes-Prediction-Using-Ensemble-Techniques

Diabetes is one of the most commonly known chronic diseases, leading to complications in health if it is unidentified and not diagnosed. Implemented various machine learning algorithms on the data collected from PIMA Indian Diabetes Database, which is sourced from the UCI Machine learning repository. Applied machine learning techniques such as K Nearest Neighbors, Logistic regression, Naive Bayes, Decision trees, Gaussian process, Linear SVM, RBF SVM, Xgboost, Gradient boost, AdaBoost and Random forest. All these mentioned algorithms are applied to the normalized data. The performance comparison of the model is discussed based on the accuracy as an evaluation metric, along with a brief description of how every model is implemented in this paper. The voting classifier is applied on top of the best models from the above machine learning techniques listed.