Upload app.py

app.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import streamlit as st
+import pickle
+import pandas as pd
+import numpy as np
+from datetime import timedelta
+import yfinance as yf
+import warnings
+warnings.filterwarnings('ignore')
+
+# Load your trained Linear Regression model
+with open('mod_AAPL.pkl', 'rb') as file:
+    model = pickle.load(file)
+
+st.title("📈 Apple Stock Forecast (Next 30 Days)")
+st.write("Uses a Linear Regression model with `Date_Ordinal` and `Prev_Close` as features.")
+
+# --- 📅 Select date range to fetch data
+st.subheader("📅 Select date range to fetch Apple stock data")
+start_date = st.date_input("Start Date", value=pd.Timestamp("2020-01-01"))
+end_date = st.date_input("End Date", value=pd.Timestamp.now())
+
+# --- Fetch data from Yahoo Finance
+st.write("🔄 Fetching AAPL stock data...")
+apple_data = yf.download("AAPL", start=start_date, end=end_date)
+
+# --- Handle empty data case
+if apple_data.empty:
+    st.error("⚠️ No data found for the selected date range. Please try another range.")
+else:
+    st.success("✅ Data fetched successfully.")
+    st.write("Showing latest data:")
+    st.dataframe(apple_data.tail())
+
+    # --- Preprocessing
+    apple_data.index = pd.to_datetime(apple_data.index)
+    apple_data['Date_Ordinal'] = apple_data.index.map(lambda x: x.toordinal())
+    apple_data['Prev_Close'] = apple_data['Close'].shift(1)
+    apple_data.dropna(inplace=True)
+
+    # Predict next 30 days from last date
+    last_known_close = apple_data['Close'].iloc[-1]
+    last_date = apple_data.index.max()
+    future_dates = pd.date_range(start=last_date + timedelta(days=1), periods=30)
+    future_preds = []
+
+    prev_close = last_known_close
+    for date in future_dates:
+        date_ordinal = date.toordinal()
+        features = pd.DataFrame({'Date_Ordinal': [date_ordinal], 'Prev_Close': [prev_close]})
+        pred = model.predict(features)[0]
+        future_preds.append(pred)
+        prev_close = pred  # Roll forward
+
+    forecast_df = pd.DataFrame({'Date': future_dates, 'Predicted_Close': future_preds})
+
+    st.subheader("🔮 Next 30-Day Forecast")
+    st.dataframe(forecast_df)
+
+    # --- Plot
+    st.line_chart(forecast_df.set_index('Date')['Predicted_Close'])
+
+
+# In[ ]:
+
+
+
+