• Neha Kandhare
• Shreedhar Parge
• Dnyaneshwari Nikat
• Snehal Jagdale
• Anoop Kushwaha

Stock price prediction is a critical area of research and application in financial markets. This project employs Facebook's Prophet, a robust time series forecasting tool, to predict future stocks prices. The aim is to build a predictive model capable of analyzing historical stocks data and forecasting future trends. The project begins with the collection of historical stocks price data from reliable sources. After preprocessing and feature engineering, the data is structured into the required format for analysis. The Prophet model is then trained using this prepared dataset, learning patterns and trends in the stock’s prices. The study's implementation is executed through an interactive web application developed using Streamlit. This application allows users to input stocks preferences, select date ranges, and receive real-time stocks price predictions based on the trained Prophet model. Users can visualize historical and predicted stocks prices, enabling them to make informed decisions. The project contributes to the field of stocks market prediction by demonstrating the application of Prophet in a user-friendly and accessible manner for traders and investors seeking valuable insights into future stocks price trends.

Nifty 50 Stocks Price Prediction, Data Science, Machine Learning, Streamlit, Stocks Market, Yahoo Finance, Prophet.

"Nifty 50 Stocks Price Prediction App", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.k326-k331, April-2024, Available :http://www.jetir.org/papers/JETIR2404A41.pdf

"Nifty 50 Stocks Price Prediction App", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppk326-k331, April-2024, Available at : http://www.jetir.org/papers/JETIR2404A41.pdf