Spaces:
Sleeping
Sleeping
File size: 5,074 Bytes
2a15b67 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 |
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(
"[](https://github.com/scorpionTaj)"
)
st.sidebar.markdown(
"[](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}")
|