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.gitattributes

@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+models/model_bca.keras filter=lfs diff=lfs merge=lfs -text
+models/model_bni.keras filter=lfs diff=lfs merge=lfs -text
+models/model_bri.keras filter=lfs diff=lfs merge=lfs -text
+models/model_bsi.keras filter=lfs diff=lfs merge=lfs -text
+models/model_mri.keras filter=lfs diff=lfs merge=lfs -text

app.py

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+# Import necessary libraries
+from datetime import datetime
+import streamlit as st
+import yfinance as yf
+from predictions import feature_engineering, load_custom_model, run_inference
+
+# Main function to run the app
+def main():
+    st.title("Stock Prediction App")
+
+    # Define the ticker symbols
+    ticker_symbols = ['BCA', 'BRI', 'Mandiri', 'BNI', 'BSI']
+
+    # Create a dropdown menu for selecting a ticker symbol
+    selected_ticker = st.selectbox('Select a Ticker', ticker_symbols)
+    
+    # Button to trigger prediction
+    if st.button("Predict the following bank company"):
+        with st.spinner('Loading...'):
+            # Fetch stock data for the selected ticker symbol
+            stock_data = fetch_stock_data(selected_ticker)
+            
+            if stock_data is not None:
+                # Perform model inference
+                predicted_price = perform_inference(stock_data, selected_ticker)
+                
+                # Display the predicted price
+                st.write(f"Predicted Close Price for {selected_ticker}: {predicted_price}")
+            else:
+                st.error("Failed to retrieve data for the selected ticker. Please try again.")
+
+# Function to perform inference on the stock data
+def perform_inference(stock_data, selected_ticker):
+    # Perform feature engineering
+    X_test = feature_engineering(stock_data)
+
+    # Load the corresponding model
+    model = load_custom_model(selected_ticker)
+
+    if model is not None:
+        # Run inference
+        predicted_prices = run_inference(model, X_test)
+        return predicted_prices[-1]  # Return the last predicted price
+    else:
+        return None
+
+# Function to fetch stock data for the selected ticker symbol
+def fetch_stock_data(selected_ticker):
+    # Define the start date (1 year ago) and end date (today)
+    start_date = datetime(2023, 1, 1)
+    end_date = datetime.now()
+
+    # Fetch stock data based on the selected ticker symbol
+    if selected_ticker == 'BCA':
+        return yf.download('BBCA.JK', start=start_date, end=end_date)
+    elif selected_ticker == 'BRI':
+        return yf.download('BBRI.JK', start=start_date, end=end_date)
+    elif selected_ticker == 'Mandiri':
+        return yf.download('BMRI.JK', start=start_date, end=end_date)
+    elif selected_ticker == 'BNI':
+        return yf.download('BBNI.JK', start=start_date, end=end_date)
+    elif selected_ticker == 'BSI':
+        return yf.download('BRIS.JK', start=start_date, end=end_date)
+    else:
+        return None
+
+# Run the main function
+if __name__ == "__main__":
+    main()

models/model_bca.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b09fb494658563e2432e9dd3a0a77f86a184dc1b89d58d74885835bda116a050
+size 4105007

models/model_bni.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ceadf438fdbc73ad49fc27405629878823203df33913df1f7c05a42d55b54c8
+size 4105007

models/model_bri.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ecd756f519b3118691fd77b90472a0c26f51ae86c7c4bec6e96f9d5741b352eb
+size 4105007

models/model_bsi.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0e5849af3421ffc307ac2446b45fc1c13fc62a6ab9a3699f095ea38090b352fb
+size 4105007

models/model_mri.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4122396e29b69f6b1a4e949dce3c7d616be4a495fe747522ffa0c9b59605500a
+size 4105007

predictions.py

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+from sklearn.preprocessing import MinMaxScaler
+import numpy as np
+import pandas as pd
+from datetime import datetime, timedelta
+import tensorflow as tf
+from tensorflow.keras.models import load_model as tf_load_model
+
+# Define the scaler globally
+scaler = MinMaxScaler(feature_range=(0, 1))
+
+def feature_engineering(stock_data):
+    
+    # Define the end date for fetching data (yesterday's date)
+    end_date = datetime.now() - timedelta(days=1)
+
+    # Fetch data until yesterday's date
+    dataset = stock_data["Close"].loc[:end_date]
+    dataset = pd.DataFrame(dataset)
+    data = dataset.values
+
+    # 75% to Train, 25% to Test
+    train_size = int(len(data) * 0.85)
+    test_size = len(data) - train_size
+
+    train_data = data[:train_size]
+    test_data = data[train_size-5:]
+
+    scaled_train = scaler.fit_transform(np.array(train_data).reshape(-1, 1))
+    scaled_test = scaler.transform(np.array(test_data).reshape(-1, 1))
+
+    # Create dataset with 60 time steps and (59 input and only 1 output in each) as this is a regression problem
+    X_train = []
+    y_train = []
+
+    for i in range(60, len(scaled_train)):
+        X_train.append(scaled_train[i-5:i, 0])
+        y_train.append(scaled_train[i, 0])
+
+    # Convert Xs, y to arrays
+    X_train, y_train = np.array(X_train), np.array(y_train)
+
+    # Reshape data
+    X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
+
+    # Creating a testing set with 60 time-steps and 1 output
+    X_test = []
+    y_test = []
+
+    for i in range(5, len(scaled_test)):
+        X_test.append(scaled_test[i-5:i, 0])
+        y_test.append(scaled_test[i, 0])
+
+    X_test, y_test = np.array(X_test), np.array(y_test)
+    X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
+
+    return X_test
+
+# Function to load a specific model
+def load_custom_model(selected_model):
+    if selected_model == 'BCA':
+        model = tf_load_model('model_bca.keras')
+    elif selected_model == 'BNI':
+        model = tf_load_model('model_bni.keras')
+    elif selected_model == 'BRI':
+        model = tf_load_model('model_bri.keras')
+    elif selected_model == 'Mandiri':
+        model = tf_load_model('model_mri.keras')
+    else:
+        model = None
+    return model
+
+def run_inference(model, X_test):
+    # Perform inference using the loaded model
+    predicted_prices = model.predict(X_test)
+
+    # Inverse scaling to get the actual prices
+    predicted_prices = scaler.inverse_transform(predicted_prices)
+
+    return predicted_prices

requirements.txt

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+scikit-learn==1.3.2
+yfinance==0.2.37
+tensorflow==2.15.0
+numpy==1.26.3
+streamlit
+seaborn