Forecasting Number of Indian Startups using Supervised Learning Regression Models

For regulators and investors, estimating the potential of the Indian market requires accurately predicting the number of startups in the Indian ecosystem. Startup growth may be predicted with great accuracy using Supervised Learning Regression models. These models take into account a wide range of variables, including financing, market demand, and competition. The purpose of this research is to use Supervised Learning Regression models to make predictions about the future of the startup scene in India. Information from the Startup Database, official papers, and scholarly journals all factored into the analysis. Supervised Learning Regression models are then used to make predictions about future growth based on the identified variables, using training data taken from the past. Factors including finance availability, government regulations, and market demand are identified in the report as having a substantial influence on the number of startups in India. The potential expansion of the startup sector in India is foreseen by using Supervised Learning Regression models to forecast the future number of companies in the Indian ecosystem. The findings of this research support the use of linear models for estimating future startup activity in India. Policymakers and investors may benefit from this study's results by learning more about the forces that are propelling India's startup scene forward.