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+web: sh setup.sh && streamlit run app.py

README.md

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----
-title: Hash IT Hackathon
-emoji: ⚡
-colorFrom: red
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.32.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
+# Stock Price Prediction App
+
+Welcome to the Stock Price Prediction App! This app allows you to visualize stock price data, explore technical indicators, and make short-term price predictions using machine learning models.
+
+Created and designed by [Vikas Sharma](https://www.linkedin.com/in/vikas-sharma005/).
+
+## Table of Contents
+
+- [Description](#description)
+- [Features](#features)
+- [Setup](#setup)
+- [Usage](#usage)
+- [Technologies](#technologies)
+- [License](#license)
+
+## Description
+
+The Stock Price Prediction App is a Streamlit-based web application that provides users with tools to analyze historical stock price data, visualize technical indicators, and make short-term price predictions using different machine learning models.
+
+## Features
+
+- **Visualize Technical Indicators**: Explore various technical indicators such as Bollinger Bands, MACD, RSI, SMA, and EMA to gain insights into stock price trends.
+
+- **Recent Data Display**: View the most recent data of the selected stock, including the last 10 data points.
+
+- **Price Prediction**: Predict future stock prices using machine learning models including Linear Regression, Random Forest Regressor, Extra Trees Regressor, KNeighbors Regressor, and XGBoost Regressor.
+
+## Setup
+
+1. Clone the repository:
+   ```sh
+   git clone https://github.com/vikasharma005/Stock-Price-Prediction.git
+   ```
+
+2. Navigate to the project directory:
+   ```sh
+   cd stock-price-prediction-app
+   ```
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+3. Install the required Python packages using pip:
+   ```sh
+   pip install -r requirements.txt
+   ```
+
+## Usage
+
+1. Run the Streamlit app:
+   ```sh
+   streamlit run app.py
+   ```
+
+2. The app will open in your default web browser. Use the sidebar to choose options for visualization, recent data display, or making price predictions.
+
+3. Follow the on-screen instructions to input the stock symbol, select a date range, and choose technical indicators or prediction models.
+
+## Technologies
+
+- Python
+- Streamlit
+- pandas
+- yfinance
+- ta (Technical Analysis Library)
+- scikit-learn
+- XGBoost
+
+  ## Author
+
+<div id="header" align="center">
+  <img src="https://media.giphy.com/media/M9gbBd9nbDrOTu1Mqx/giphy.gif" width="100"/>
+</div>
+
+<h3 align="center">Hi there 👋, I'm Vikas</h3>
+<h4 align="center">Just learning New Skills😀</h4>
+
+<div id="socials" align="center">
+  <a href="https://www.linkedin.com/in/vikas-sharma005">
+    <img src="https://user-images.githubusercontent.com/76098066/186728913-a66ef85f-4644-4e3a-b847-98309c8cff42.svg">
+  </a>
+  <a href="https://www.instagram.com/_thisisvikas">
+    <img src="https://user-images.githubusercontent.com/76098066/186728908-f1a9919a-f4b2-4262-9515-683e77f8aabf.svg">
+  </a>
+  <a href="https://twitter.com/hitechvikas05">
+    <img src="https://user-images.githubusercontent.com/76098066/186728901-a4d90f01-2cdf-45c1-a1b3-73467c3d2698.svg">
+  </a>
+</div>
+
+You can find more about me and my projects on my [GitHub profile](https://github.com/vikasharma005).
+
+## License
+
+This project is licensed under the [MIT License](LICENSE).
+
+---

app.py

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+
+import streamlit as st
+import pandas as pd
+import yfinance as yf
+from ta.volatility import BollingerBands
+from ta.trend import MACD, EMAIndicator, SMAIndicator
+from ta.momentum import RSIIndicator
+import datetime
+from datetime import date
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.neighbors import KNeighborsRegressor
+from xgboost import XGBRegressor
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.ensemble import ExtraTreesRegressor
+from sklearn.metrics import r2_score, mean_absolute_error,accuracy_score
+
+
+st.title('Stock Price Analysis')
+st.sidebar.info('Welcome to the Stock Price Prediction App. Choose your options below')
+
+
+def main():
+    option = st.sidebar.selectbox('Make a choice', ['Visualize','Recent Data', 'Predict'])
+    if option == 'Visualize':
+        tech_indicators()
+    elif option == 'Recent Data':
+        dataframe()
+    else:
+        predict()
+
+
+
+@st.cache_resource
+def download_data(op, start_date, end_date):
+    df = yf.download(op, start=start_date, end=end_date, progress=False)
+    return df
+
+
+
+option = st.sidebar.text_input('Enter a Stock Symbol', value='SPY')
+option = option.upper()
+today = datetime.date.today()
+duration = st.sidebar.number_input('Enter the duration', value=3000)
+before = today - datetime.timedelta(days=duration)
+start_date = st.sidebar.date_input('Start Date', value=before)
+end_date = st.sidebar.date_input('End date', today)
+if st.sidebar.button('Send'):
+    if start_date < end_date:
+        st.sidebar.success('Start date: `%s`\n\nEnd date: `%s`' %(start_date, end_date))
+        download_data(option, start_date, end_date)
+    else:
+        st.sidebar.error('Error: End date must fall after start date')
+
+
+
+
+data = download_data(option, start_date, end_date)
+scaler = StandardScaler()
+
+def tech_indicators():
+    st.header('Technical Indicators')
+    option = st.radio('Choose a Technical Indicator to Visualize', ['Close', 'BB', 'MACD', 'RSI', 'SMA', 'EMA'])
+
+    bb_indicator = BollingerBands(data.Close)
+    bb = data
+    bb['bb_h'] = bb_indicator.bollinger_hband()
+    bb['bb_l'] = bb_indicator.bollinger_lband()
+    bb = bb[['Close', 'bb_h', 'bb_l']]
+    macd = MACD(data.Close).macd()
+    rsi = RSIIndicator(data.Close).rsi()
+    sma = SMAIndicator(data.Close, window=14).sma_indicator()
+    ema = EMAIndicator(data.Close).ema_indicator()
+
+    if option == 'Close':
+        st.write('Close Price')
+        st.line_chart(data.Close)
+    elif option == 'BB':
+        st.write('BollingerBands')
+        st.line_chart(bb)
+    elif option == 'MACD':
+        st.write('Moving Average Convergence Divergence')
+        st.line_chart(macd)
+    elif option == 'RSI':
+        st.write('Relative Strength Indicator')
+        st.line_chart(rsi)
+    elif option == 'SMA':
+        st.write('Simple Moving Average')
+        st.line_chart(sma)
+    else:
+        st.write('Expoenetial Moving Average')
+        st.line_chart(ema)
+
+
+def dataframe():
+    st.header('Recent Data')
+    st.dataframe(data.tail(10))
+
+
+
+def predict():
+    model = st.radio('Choose a model', ['LinearRegression', 'RandomForestRegressor', 'ExtraTreesRegressor', 'KNeighborsRegressor', 'XGBoostRegressor'])
+    num = st.number_input('How many days forecast?', value=5)
+    num = int(num)
+    if st.button('Predict'):
+        if model == 'LinearRegression':
+            engine = LinearRegression()
+            model_engine(engine, num)
+        elif model == 'RandomForestRegressor':
+            engine = RandomForestRegressor()
+            model_engine(engine, num)
+        elif model == 'ExtraTreesRegressor':
+            engine = ExtraTreesRegressor()
+            model_engine(engine, num)
+        elif model == 'KNeighborsRegressor':
+            engine = KNeighborsRegressor()
+            model_engine(engine, num)
+        else:
+            engine = XGBRegressor()
+            model_engine(engine, num)
+
+
+def model_engine(model, num):
+    df = data[['Close']]
+    df['preds'] = data.Close.shift(-num)
+    x = df.drop(['preds'], axis=1).values
+    x = scaler.fit_transform(x)
+    x_forecast = x[-num:]
+
+    x = x[:-num]
+
+    y = df.preds.values
+
+    y = y[:-num]
+
+
+    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.2, random_state=7)
+    model.fit(x_train, y_train)
+    preds = model.predict(x_test)
+    st.text(f'R2_SCORE: {r2_score(y_test, preds)} \
+            \nMAE: {mean_absolute_error(y_test, preds)}'
+          )
+    
+    
+    forecast_pred = model.predict(x_forecast)
+    day = 1
+    for i in forecast_pred:
+        st.text(f'Day {day}: {i}')
+        day += 1
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+streamlit
+scikit-learn
+yfinance
+pandas
+ta
+xgboost
+numpy
+Pillow
+tensorflow
+opencv-python-headless
+

setup.sh

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+mkdir -p ~/.streamlit/
+
+
+echo "\
+[server]\n\
+port = $PORT\n\
+enableCORS = false\n\
+headless = true\n\
+\n\
+" > ~/.streamlit/config.toml