Forecasting of Economic Recession using Machine Learning

A significant, highly widespread, and long-lasting drop in economic activity is described as a recession. Since the time it touches the high end of the previous expansion to the trough, economists all around the world tend to search for the length and the factors in between this length that constitutes the recession. Recessions may only last a few months, but it may take years for the economy to bounce back and reach back to its previous high point. In this paper, we investigate various datasets and its pros and cons and try to give the shot to speculate the chances of recession, based on Machine Learning techniques and backed by concrete datasets for better approximation of result. Within a Machine Learning framework, we try to put in the values, specifically the Chicago Federation National Activity Index (CFNAI) and its 2 components: Monthly Average of 3 and CFNAI Diffusion Index from a period of March, 1967 to June, 2022, along with the S&P-500 index and various parameters associated with it (like 10yr Tbond, % Tbond, 2 year spread fedrate, CPI etc) from the day of the recorded period. Using unscaled, scaled and tuned models of machine learning technique, the model was able to predict the chances, ranging from 87% when the models used were unscaled, to slightly more than 94% for scaled models. Both the models are different from each other, and are compiled together for comparison and the purpose of having a surety about the model being correct in these regards.