Built a Clustering model based on algorithms of Hierarchical clustering, K Means which segregates 182 countries of the world into separate clusters such as developed, developing, under developed etc. according to their socio-economic performance.
While performing EDA dealt with 11.7k missing values with the help of imputation by SLR, KNN imputer, median and insertion, deletion.
Dataset and output of clustering model was then used to train a Classification model which predicts development status of given country with 93% accuracy.
Deployed model using streamlit.