• Arunadevi Khaple
• Ketan Mahandule
• Niraj Kulkarni
• Yash Joshi
• Shreyas Natu

This research paper investigates the usage of Python and fbprophet for stock price prediction. The paper outlines the approach used to create the prediction model after reviewing relevant literature on stock price prediction. The fbprophet model was trained using historical stock prices and technical indicators as inputs, and its accuracy was evaluated using new data. This research article provides a thorough overview of the numerous machine learning methods, such as additive regression models and time series analysis, that are used to forecast stock prices. The findings reveal that the fbprophet model predicts stock values well, even beating other machine learning models in several circumstances. In addition, utilizing historical stock market data and the fbprophet model, the research provides a methodology for projecting stock market trends based on technical analysis. The results show that the fbprophet model has a high potential for short-term stock market trend prediction, which can assist financial professionals make better judgements about stock purchases and sells.

Additive regression models, fbprophet, stock price prediction, data analytics, machine learning

"Exploring Additive Regression Models and fbprophet for Stock Price Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.j126-j130, May-2023, Available :http://www.jetir.org/papers/JETIR2305966.pdf

"Exploring Additive Regression Models and fbprophet for Stock Price Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppj126-j130, May-2023, Available at : http://www.jetir.org/papers/JETIR2305966.pdf