GayathriMadala/Analysis_on_Diabetes_data

In this project of "Performance Analysis of Supervised Machine Learning Techniques", various supervised algorithms namely Logistic Regression, KNearestNeighbors, Support Vector Machine, Decision Tree Classifier and Random Forest Classifier are applied on to real-time Diabetes dataset. This dataset has missing values and they are removed by applying both the mean imputation and median imputation techniques. This dataset is then normalized using Standard Scaler and MinMax Scaler. The cleaned and normalized data is further treated with the all 5 specified algorithms and their confusion matrices, accuracy scores, precision values and recall values are generated. These values are compared by generating corresponding bar graphs and the best algorithm, best imputation techniques are determined.