Microfinance institutions in India provide a set of financial services to the economically weaker sections. Recently, a large number of microfinance institutions have emerged in India and they have favorable impact for poverty reduction. The impact of these institutions on entrepreneurship and society, needs to be explored in greater depth. The objective of this study is to apply machine learning techniques to explore this impact. The research uses a MIX dataset for three successive years, namely 2017, 2018, and 2019. This dataset comprises eight variables centered on gross loan portfolio. Principal Component Analysis (PCM) has been applied on the sample dataset for dimensionality reduction, resulting in two main components and each component consist of fraction from eight variables. Then, the sample dataset has been labelled with the help of clustering using K-means clustering technique. Further, classification models based on K-Nearest Neighbors (KNN) algorithm and Support Vector Machine (SVM) are applied to predict the appropriate category of entrepreneurship. The experiment result shows that the machine learning techniques have been found effective and useful tools for estimating the impact of microfinance on entrepreneurship in India.
