• Sanskruti Satav
• Prof. Sachin Dada Shinde
• Dr. Rahat Khan

Stock price prediction is a key area of research in financial markets, with significant applications in investment and trading. This project uses Facebook's Prophet, a powerful time-series forecasting tool, to predict future stock prices. The objective is to develop a model that analyzes historical stock data and forecasts future trends. The process begins by collecting historical stock price data from reliable sources, followed by preprocessing and feature engineering to format the data for analysis. The Prophet model is trained on this data to identify patterns and trends in stock prices. The system is implemented through an interactive web application built using Streamlit. This application enables users to input stock preferences, select date ranges, and receive predictions based on the trained model. Users can also visualize both historical and predicted stock prices, aiding in decision-making. The project highlights the use of Prophet in providing valuable insights into stock price forecasting. It aims to make stock market prediction tools more accessible and practical for traders and investors.

Data Analytics, Machine Learning, Streamlit, Financial Markets, Yahoo Finance, Prophet.

"Stocks Price Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 8, page no.202-206, August-2025, Available :http://www.jetir.org/papers/JETIRHA06028.pdf

"Stocks Price Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 8, page no. pp202-206, August-2025, Available at : http://www.jetir.org/papers/JETIRHA06028.pdf