Update app.py

app.py

@@ -7,7 +7,6 @@ from sklearn.preprocessing import StandardScaler
 from sklearn.model_selection import train_test_split
 from datetime import datetime, timedelta
 import numpy as np
-import matplotlib.pyplot as plt
 
 # Function to compute RSI
 def compute_rsi(data, window):
@@ -21,27 +20,30 @@ def compute_rsi(data, window):
  return rsi
 
 # Set up the Streamlit app
-st.title("Bitcoin Price Prediction")
-st.write("This app uses historical data to predict future Bitcoin prices.")
+st.title("Stock Price Prediction")
+st.write("This app uses historical data to predict future stock prices.")
 
-# Fetch Bitcoin historical data using yfinance
-btc_data = yf.download('BTC-USD', start='2020-01-01', end=datetime.today().strftime('%Y-%m-%d'))
-btc_data.reset_index(inplace=True)
+# User input for stock ticker symbol
+ticker = st.text_input("Enter the stock ticker symbol:", value='AAPL')
+
+# Fetch stock historical data using yfinance
+stock_data = yf.download(ticker, start='2020-01-01', end=datetime.today().strftime('%Y-%m-%d'))
+stock_data.reset_index(inplace=True)
 
 # Display the historical data
-st.write("Historical Bitcoin Data")
-st.dataframe(btc_data.tail())
+st.write(f"Historical Data for {ticker}")
+st.dataframe(stock_data.tail())
 
 # Feature engineering
-btc_data['MA_10'] = btc_data['Close'].rolling(window=10).mean()
-btc_data['MA_50'] = btc_data['Close'].rolling(window=50).mean()
-btc_data['RSI'] = compute_rsi(btc_data['Close'], window=14)
-btc_data['Return'] = btc_data['Close'].pct_change()
-btc_data.dropna(inplace=True)
+stock_data['MA_10'] = stock_data['Close'].rolling(window=10).mean()
+stock_data['MA_50'] = stock_data['Close'].rolling(window=50).mean()
+stock_data['RSI'] = compute_rsi(stock_data['Close'], window=14)
+stock_data['Return'] = stock_data['Close'].pct_change()
+stock_data.dropna(inplace=True)
 
 # Prepare features and target variable
-X = btc_data[['Open', 'High', 'Low', 'Volume', 'MA_10', 'MA_50', 'RSI', 'Return']]
-y = btc_data['Close']
+X = stock_data[['Open', 'High', 'Low', 'Volume', 'MA_10', 'MA_50', 'RSI', 'Return']]
+y = stock_data['Close']
 
 # Split the data
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
@@ -60,7 +62,7 @@ rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
 rf_model.fit(X_train_scaled, y_train)
 
 # Predict future prices using ensemble method
-future_dates = [btc_data['Date'].iloc[-1] + timedelta(days=x) for x in range(1, 15)]
+future_dates = [stock_data['Date'].iloc[-1] + timedelta(days=x) for x in range(1, 15)]
 future_df = pd.DataFrame(index=future_dates, columns=X.columns)
 future_df = future_df.fillna(method='ffill')
 
@@ -74,7 +76,7 @@ combined_predictions = (lr_predictions + rf_predictions) / 2
 # Display predictions
 predictions_df = pd.DataFrame({'Date': future_dates, 'Predicted Close': combined_predictions})
 predictions_df.set_index('Date', inplace=True)
-st.write("Future Bitcoin Price Predictions")
+st.write(f"Future Price Predictions for {ticker}")
 st.dataframe(predictions_df)
 
 # Plot the predictions