Delete Apple_dep.py

Apple_dep.py

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-#!/usr/bin/env python
-# coding: utf-8
-
-# In[2]:
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-
-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'])
-
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-# In[ ]:
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