STOCK MARKET PRICE PREDICTION

 Introduction:

In this blog, we'll be discussing a project that involves stock market price prediction. The project was created using Python programming language and various libraries such as Streamlit, Prophet, Plotly, and Ufinance. These libraries are used for visualization, time-series forecasting, and financial data retrieval. The aim of this project is to predict stock prices of various companies based on past data and to provide a user-friendly interface for users to interact with the model and visualize the predictions.

Project Overview:

The project consists of two main parts: data collection and model building. We used the Yahoo Finance API to retrieve historical stock data of various companies. We then used this data to train our time-series forecasting model using Prophet, a library specifically designed for forecasting time-series data. Finally, we created a web application using Streamlit, a Python library used for building interactive web applications. The web application allows users to select a company and visualize the predicted stock prices.

Data Collection:

We used the Yahoo finance library to retrieve historical stock data of various companies from the Yahoo Finance API. We collected data for the past 5 years for each company and stored it in a Pandas dataframe. The data includes the date, opening price, closing price, high price, low price, and volume of shares traded. We then preprocessed the data to ensure that it was in the correct format for our model.

Model Building:

We used the Prophet library to build our time-series forecasting model. Prophet is a library developed by Facebook that is specifically designed for forecasting time-series data. It uses a decomposable time series model with three main components: trend, seasonality, and holidays. The trend component models non-periodic changes in the time series, while the seasonality component models periodic changes in the time series. The holidays component models the effect of holidays and special events on the time series.

We trained our model on the historical stock data for each company using the Prophet library. We then used the model to predict the future stock prices of each company for the next 30 days.

Web Application:

We used the Streamlit library to create a web application that allows users to select a company and visualize the predicted stock prices. The web application consists of a dropdown menu where users can select a company. Once a company is selected, the predicted stock prices for the next 30 days are displayed in a line chart using the Plotly library. The chart includes the predicted opening price, closing price, high price, and low price for each day.

Conclusion:

In this blog, we discussed a project that involves stock market price prediction using Python programming language and various libraries such as Streamlit, Prophet, Plotly, and Yfinance. The project aims to predict stock prices of various companies based on past data and to provide a user-friendly interface for users to interact with the model and visualize the predictions. The project can be further improved by adding more features such as sentiment analysis and news analysis to improve the accuracy of the predictions.