Overview:

The Stock Market Predictor is a web application built using Streamlit that allows users to predict future stock prices of selected companies. It leverages historical stock data from Yahoo Finance and uses the Prophet forecasting model to generate predictions.

Features** Select stock from a predefined list of companies. Choose the number of years for future prediction (1 to 4 years). View raw stock data. Visualize historical stock prices. Generate and display future stock price predictions. Analyze forecast components such as trends and seasonal patterns. **Technologies Used **Streamlit: For creating the web application. yFinance: For fetching historical stock data. Prophet: For forecasting future stock prices. Plotly: For data visualization. Installation To run this application, you need to have Python installed. Follow the steps below to set up the environment:

Clone the repository: git clone https://github.com/yourusername/StockMarketPredictor.git cd stock-market-predictor Install the required packages:

Code: pip install streamlit yfinance prophet plotly Running the Application To start the application, run the following command in the terminal:

Code: streamlit run app.py Application Structure app.py: The main file that contains the Streamlit application code. requirements.txt: List of required packages for the application. Code Explanation Here is a breakdown of the main parts of the application:

Imports Code: import streamlit as s from datetime import date import yfinance as yf from prophet import Prophet from prophet.plot import plot_plotly from plotly import graph_objs as g These are the necessary libraries for building the application, handling dates, fetching stock data, forecasting, and visualization.

Constants Code: START = "2018-01-01" CURRENT = date.today().strftime("%Y-%m-%d") stocks = ("HYLN", "AMZN", "RBLX", "CVNA", "AAPL", "RIVN") Defines the start date for fetching historical data, the current date, and the list of available stocks for prediction.

User Inputs Code:

picked_stocks = s.selectbox("Pick data set for the prediction", stocks) quantity_years = s.slider("Years of prediction:", 1, 4) timePeriod = quantity_years * 365 Creates user interface elements for selecting the stock and the prediction period.

Data Loading Code:

@s.cache_data def load_All_Data(ticker): data = yf.download(ticker, START, CURRENT) data.reset_index(inplace=True) return data

data_loading_states = s.text("Loading this data....") data = load_All_Data(picked_stocks) data_loading_states.text("Loading's complete!") Defines a function to load historical stock data and displays a loading text while the data is being fetched.

Raw Data Display Code:

s.subheader('Raw information') s.write(data.tail()) Displays the raw stock data.

Raw Data Plotting Code:

def plottingRawData(): fig = g.Figure() fig.add_trace(g.Scatter(x=data['Date'], y=data['Open'], name='stock_open')) fig.add_trace(g.Scatter(x=data['Date'], y=data['Close'], name='stock_close')) fig.layout.update(title_text="Time Series Data", xaxis_rangeslider_visible=True) s.plotly_chart(fig)

plottingRawData() Creates a function to plot the historical stock prices and calls it to display the plot.

Data Preparation and Forecasting

Code:

df_train = data[['Date', 'Close']] df_train = df_train.rename(columns={"Date": "ds", "Close": "y"})

mm = Prophet() mm.fit(df_train) future = mm.make_future_dataframe(periods=timePeriod) forecast = mm.predict(future) Prepares the data for the Prophet model, fits the model, and makes future predictions.

Forecast Display Code:

s.subheader('Forecast data') s.write(forecast.tail()) s.write('forecast data') fig1 = plot_plotly(mm, forecast) s.plotly_chart(fig1) s.write('forecast components') figure2 = mm.plot_components(forecast) s.write(figure2) Displays the forecast data and components.

Conclusion This application provides a user-friendly interface for predicting future stock prices using historical data and advanced forecasting models. It's a powerful tool for investors and analysts looking to gain insights into stock market trends.