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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| from sklearn.linear_model import LinearRegression | |
| import matplotlib.pyplot as plt | |
| # Set a title for the app | |
| st.title("Stock Price Prediction") | |
| # Load the stock data from the CSV files | |
| data1 = pd.read_csv('Google_test_data.csv') | |
| data2 = pd.read_csv('tesla.csv') | |
| # Combine the datasets into a dictionary | |
| datasets = {'Google': data1, 'Tesla': data2} | |
| # Get the user's dataset selection | |
| selected_dataset = st.selectbox('Select Stock', list(datasets.keys())) | |
| # Retrieve the selected dataset | |
| selected_data = datasets[selected_dataset] | |
| # Prepare the data for prediction | |
| X = np.arange(len(selected_data)).reshape(-1, 1) | |
| y = selected_data['Close'] | |
| # Train the linear regression model | |
| model = LinearRegression() | |
| model.fit(X, y) | |
| # Define a flag for displaying the graphs and table | |
| display_graphs = False | |
| # Create a submit button | |
| if st.button('Submit'): | |
| display_graphs = True | |
| # Display the image as the background | |
| st.markdown( | |
| """ | |
| <style> | |
| .stApp { | |
| background-image: url('https://img.etimg.com/thumb/msid-96018223,width-1200,height-900,imgsize-117108,resizemode-8,quality-100/markets/stocks/news/traders-guide-these-2-stocks-have-up-to-6-upside-scope.jpg'); | |
| background-size: cover; | |
| } | |
| </style> | |
| """, | |
| unsafe_allow_html=True | |
| ) | |
| # Display the graphs and table if the submit button is clicked | |
| if display_graphs: | |
| # Create subplots with increased spacing between subplots | |
| fig, axes = plt.subplots(3, 1, figsize=(10, 20), sharex=True, gridspec_kw={'hspace': 0.5}) | |
| # Plot 1: Actual and Predicted Stock Prices | |
| axes[0].plot(selected_data['Date'], y, label='Actual') | |
| axes[0].plot(selected_data['Date'], model.predict(X), label='Predicted') | |
| axes[0].set_ylabel('Stock Price') | |
| axes[0].set_title(f'{selected_dataset} Stock Price Prediction') | |
| axes[0].legend() | |
| # Plot 2: Volume of Trades | |
| axes[1].plot(selected_data['Date'], selected_data['Volume']) | |
| axes[1].set_ylabel('Volume') | |
| axes[1].set_title(f'{selected_dataset} Volume of Trades') | |
| # Plot 3: Daily Percentage Change in Stock Prices | |
| daily_returns = selected_data['Close'].pct_change() * 100 | |
| axes[2].plot(selected_data['Date'], daily_returns) | |
| axes[2].set_xlabel('Date') | |
| axes[2].set_ylabel('Percentage Change') | |
| axes[2].set_title(f'{selected_dataset} Daily Percentage Change in Stock Prices') | |
| # Remove the background image | |
| st.markdown( | |
| """ | |
| <style> | |
| .stApp { | |
| background-image: none; | |
| } | |
| </style> | |
| """, | |
| unsafe_allow_html=True | |
| ) | |
| # Display the graphs in Streamlit | |
| st.pyplot(fig) | |
| # Display the table of price details and predicted prices | |
| st.subheader("Price Details and Predicted Prices") | |
| price_table = pd.DataFrame({'Date': selected_data['Date'], 'Actual Price': y, 'Predicted Price': model.predict(X)}) | |
| st.dataframe(price_table) | |