Aim: Comparatively analysing the efficiency of classification and clustering algorithms for diabetes detection Steps

• Exploring Dataset
• Droping columns which are not required
• Checking for null values
• Upscalling dataset - Incase of pima indiana
• X and Y split
• Countplot for the datasets
• t-SNE plot for the datasets to see the spread
• Scalling the dataset
• Train and test split of the model
• Training classification models
• Training clustering models
• Comparing their performance

Conclusion

• Standard machine learning and boosting algorithms were deployed
• XGBoost gave the best performance on both datasets for classification models
• Agglomerative clustering gave the best perfromance on both datasets for clustering models
• Over all XGBoost was better, making classification models more efficient on the datasets