Update app.py

app.py

@@ -2,6 +2,7 @@ import streamlit as st
 import requests
 from Pandas_Market_Predictor import Pandas_Market_Predictor
 import pandas as pd
+from sklearn.decomposition import PCA
 
 # Hard-coded API key for demonstration purposes
 API_KEY = "QR8F9B7T6R2SWTAT"
@@ -21,6 +22,11 @@ def calculate_indicators(data):
     data['Inside'] = (data['high'] < data['high'].shift(1)) & (data['low'] > data['low'].shift(1))
     return data
 
+def apply_pca(data, n_components=5):
+    pca = PCA(n_components=n_components)
+    reduced_data = pca.fit_transform(data)
+    return pd.DataFrame(reduced_data, index=data.index)
+
 def main():
     st.title("Stock Trend Predictor")
 
@@ -39,11 +45,14 @@ def main():
     # Rename columns
     df = df.rename(columns={'1. open': 'open', '2. high': 'high', '3. low': 'low', '4. close': 'Close', '5. volume': 'volume'})
 
-    # Calculate indicators
-    df = calculate_indicators(df)
+    # Apply PCA for dimensionality reduction
+    df_reduced = apply_pca(df)
+
+    # Calculate indicators on reduced data
+    df_reduced = calculate_indicators(df_reduced)
 
     # Create predictor
-    my_market_predictor = Pandas_Market_Predictor(df)
+    my_market_predictor = Pandas_Market_Predictor(df_reduced)
 
     # Predict Trend
     indicators = ["Doji", "Inside"]