Upload 3 files

.gitattributes

@@ -33,3 +33,4 @@ 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
+Latest_stock_price_prediction.keras filter=lfs diff=lfs merge=lfs -text

Latest_stock_price_prediction.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c7b4232ecf9af8750042dfd8a8bcb00373ff36ae510d37732868db155f403cc
+size 1444539

app.py

@@ -0,0 +1,135 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+from keras.models import load_model
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import mean_squared_error, r2_score
+import yfinance as yf
+import matplotlib.pyplot as plt
+from datetime import datetime
+
+st.sidebar.title("Stock Price Prediction App 📈")
+st.sidebar.subheader("About the Developer")
+st.sidebar.markdown(
+    "Developed by [Tajeddine Bourhim ](https://tajeddine-portfolio.netlify.app/)."
+)
+st.sidebar.markdown(
+    "[![GitHub](https://img.shields.io/badge/GitHub-Profile-blue?logo=github)](https://github.com/scorpionTaj)"
+)
+st.sidebar.markdown(
+    "[![LinkedIn](https://img.shields.io/badge/LinkedIn-Profile-blue?logo=linkedin)](https://www.linkedin.com/in/tajeddine-bourhim/)"
+)
+
+st.sidebar.subheader("📚 About This App")
+st.sidebar.markdown(
+    "This is a stock price prediction app that uses a Long Short-Term Memory (LSTM) neural network to predict the closing price of a stock. The app uses the Yahoo Finance API to fetch the stock data."
+)
+
+stock = st.text_input("Enter the stock symbol (e.g. AAPL):")
+
+if stock:
+    end = st.date_input("End Date", datetime.now())
+    start = st.date_input("Start Date", datetime(end.year - 20, end.month, end.day))
+
+    apple_data = yf.download(stock, start, end)
+
+    if not apple_data.empty:
+        model = load_model("Latest_stock_price_prediction.keras")
+        st.subheader("📊 Stock Data")
+        st.write(apple_data)
+    else:
+        st.error("No data found for the given stock symbol.")
+else:
+    st.warning("Please enter a stock symbol.")
+
+split_len = int(len(apple_data) * 0.8)
+x_test = pd.DataFrame(apple_data.Close[split_len:])
+
+
+def graph_plotting(figsize, values, data, extra_data=0, extra_dataset=None):
+    """
+    Function to plot graphs with given parameters.
+    """
+    fig = plt.figure(figsize=figsize)
+    plt.plot(values, "Red")
+    plt.plot(data.Close, "b")
+    if extra_data:
+        plt.plot(extra_dataset)
+    return fig
+
+
+try:
+    # Display the original close price and moving average for 250 days
+    st.subheader("📈 Original Close Price and 250-Day Moving Average")
+    apple_data["MA_for_250_days"] = apple_data.Close.rolling(250).mean()
+    st.pyplot(graph_plotting((15, 6), apple_data["MA_for_250_days"], apple_data, 0))
+
+    # Display the original close price and moving average for 200 days
+    st.subheader("📈 Original Close Price and 200-Day Moving Average")
+    apple_data["MA_for_200_days"] = apple_data.Close.rolling(200).mean()
+    st.pyplot(graph_plotting((15, 6), apple_data["MA_for_200_days"], apple_data, 0))
+
+    # Display the original close price and moving average for 100 days
+    st.subheader("📈 Original Close Price and 100-Day Moving Average")
+    apple_data["MA_for_100_days"] = apple_data.Close.rolling(100).mean()
+    st.pyplot(graph_plotting((15, 6), apple_data["MA_for_100_days"], apple_data, 0))
+
+    # Display the original close price and moving average for 100 days and 250 days
+    st.subheader("📈 Original Close Price and 100-Day and 250-Day Moving Average")
+    st.pyplot(
+        graph_plotting(
+            (15, 6),
+            apple_data["MA_for_100_days"],
+            apple_data,
+            1,
+            apple_data["MA_for_250_days"],
+        )
+    )
+
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    scaled_data = scaler.fit_transform(x_test[["Close"]])
+
+    x_data = []
+    y_data = []
+
+    for i in range(100, len(scaled_data)):
+        x_data.append(scaled_data[i - 100 : i])
+        y_data.append(scaled_data[i])
+
+    x_data, y_data = np.array(x_data), np.array(y_data)
+
+    predictions = model.predict(x_data)
+
+    # Inverse transform the predictions and test data to original scale
+    inv_pre = scaler.inverse_transform(predictions)
+    inv_y_test = scaler.inverse_transform(y_data)
+
+    # Create a DataFrame to hold the original test data and predictions
+    ploting_data = pd.DataFrame(
+        {
+            "original_test_data": inv_y_test.reshape(-1),
+            "predictions": inv_pre.reshape(-1),
+        },
+        index=apple_data.index[split_len + 100 :],
+    )
+
+    # Display the original values vs predicted values
+    st.subheader("🔍 Original values vs Predicted values")
+    st.write(ploting_data)
+
+    # Plot the original close price vs predicted close price
+    st.subheader("📉 Original Close Price vs Predicted Close price")
+    fig = plt.figure(figsize=(15, 6))
+    plt.plot(pd.concat([apple_data.Close[: split_len + 100], ploting_data], axis=0))
+    plt.legend(["Data- not used", "Original Test data", "Predicted Test data"])
+    st.pyplot(fig)
+
+    # Calculate and display performance metrics
+    mse = mean_squared_error(inv_y_test, inv_pre)
+    r2 = r2_score(inv_y_test, inv_pre)
+    st.subheader("📊 Performance Metrics")
+    st.write(f"Mean Squared Error: {mse}")
+    st.write(f"R-squared: {r2}")
+
+except Exception as e:
+    st.error(f"An error occurred: {e}")