Create app.py

app.py

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+import streamlit as st
+import yfinance as yf
+import matplotlib.pyplot as plt
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_squared_error
+
+st.title("Stockiza: Stock Price App")
+
+# Get user input for stock symbol
+stock_symbol = st.text_input("Enter a stock symbol:", "AAPL")
+
+# Add a button to fetch data
+fetch_button = st.button("Fetch Data")
+
+if fetch_button:
+    try:
+        # Fetch stock data using yfinance
+        stock = yf.Ticker(stock_symbol)
+        stock_info = stock.info
+
+        # Display stock information
+        st.subheader(f"{stock_info['longName']} ({stock_symbol})")
+
+        # Check if 'currentPrice' key exists in stock_info
+        if 'currentPrice' in stock_info:
+            st.write(f"Current Price: ${stock_info['currentPrice']:.2f}")
+        else:
+            st.write("Current Price: Not available")
+
+        # Check if other keys exist before accessing them
+        if'regularMarketDayRange' in stock_info:
+            st.write(f"Day's Range: ${stock_info['regularMarketDayRange']}")
+        if 'fiftyTwoWeekRange' in stock_info:
+            st.write(f"52-Week Range: ${stock_info['fiftyTwoWeekRange']}")
+        if'regularMarketVolume' in stock_info:
+            st.write(f"Volume: {stock_info['regularMarketVolume']:,.0f}")
+        if'marketCap' in stock_info:
+            st.write(f"Market Cap: ${stock_info['marketCap']:,.2f}")
+
+        # Add a graph
+        stock_data = stock.history(period="5y")
+        fig, ax = plt.subplots()
+        ax.plot(stock_data.index, stock_data["Close"])
+        ax.set_title(f"{stock_symbol} Stock Price")
+        ax.set_xlabel("Date")
+        ax.set_ylabel("Price ($)")
+        st.pyplot(fig)
+
+        # Prepare data for time series model
+        stock_data['Date'] = pd.to_datetime(stock_data.index)
+        stock_data['Year'] = stock_data['Date'].dt.year
+        stock_data['Month'] = stock_data['Date'].dt.month
+        stock_data['Day'] = stock_data['Date'].dt.day
+
+        # Split data into training and testing sets
+        X = stock_data[['Year', 'Month', 'Day']]
+        y = stock_data['Close']
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+        # Train a random forest regressor model
+        model = RandomForestRegressor(n_estimators=100, random_state=42)
+        model.fit(X_train, y_train)
+
+        # Make predictions on the test set
+        y_pred = model.predict(X_test)
+
+        # Evaluate the model
+        mse = mean_squared_error(y_test, y_pred)
+        rmse = mse ** 0.5
+        st.write(f"Root Mean Squared Error (RMSE): {rmse:.2f}")
+
+        # Use the model to predict the stock price 5 years from now
+        future_date = pd.to_datetime('2027-12-31')
+        future_data = pd.DataFrame({'Year': [future_date.year], 'Month': [future_date.month], 'Day': [future_date.day]})
+        future_price = model.predict(future_data)
+        st.write(f"Predicted Price 5 Years from Now: ${future_price[0]:.2f}")
+
+    except Exception as e:
+        st.error(f"Error: {e}")